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Optimisation of T2*-weighted MRI for the detection of small veins in multiple sclerosis at 3 T and 7 T
European Journal of Radiology 82 (2013) 719–727
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European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Optimisation of T∗2 -weighted MRI for the detection of small veins in multiple sclerosis at 3 T and 7 T Jennifer Elizabeth Dixon a,1 , Ashley Simpson b,2 , Niraj Mistry b,2 , Nikos Evangelou b,2 , Peter Gordon Morris a,∗ a b
Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, NG7 2RD, UK Academic Division of Clinical Neurology, University of Nottingham, Queen’s Medical Centre, Nottingham, NG7 2UH, UK
a r t i c l e
i n f o
Article history: Received 24 August 2011 Accepted 19 September 2011 Keywords: Multiple sclerosis MRI Magnetic susceptibility Small veins Diagnosis 7T
a b s t r a c t T∗2 -weighted magnetic resonance imaging at 7 T has recently been shown to allow differentiation between white-matter multiple sclerosis lesions and asymptomatic white-matter lesions, by the presence or absence of a detectable central blood vessel. The aim of the present work is to improve the technique by increasing the sensitivity to veins at both 3 T and 7 T, and to assess the benefit of ultra-high-field imaging. Signal-to-noise ratio (SNR) measurements and simulations are used to compare the sensitivity of magnitude T∗2 -weighted and susceptibility-weighted images for the detection of small veins (<1 pixel in diameter), both with and without the use of gadolinium. The simulations are used to predict the optimal scanning parameters in order to increase the sensitivity to these veins at both field strengths, and to reduce the inherent dependence on vessel orientation. The sensitivities of the sequences at both field strengths are compared, theoretically and experimentally, in order to quantify the benefit of imaging at ultra-high-field. Subjects with multiple sclerosis (MS) are scanned at both field strengths, using the optimised sequence parameters, as well as those used in previously published work, and the optimisation is shown to improve the detection of veins within lesions. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction There has been a great deal of recent interest in magnetic resonance imaging (MRI) at ultra-high-field (7 T). The higher signal-to-noise ratio (SNR) available at 7 T permits scanning at much higher spatial resolution than is possible at conventional field strengths (i.e. 1.5 T, 3 T), and the different relaxation times lead to enhanced contrast. In particular, the decrease in T 2 relative to T2 at 7 T greatly improves contrast in T∗2 -weighted imaging (T 2 is the refocusable component of the transverse relaxation, T∗2 , while T2 denotes the random, unrefocusable component). T∗2 -weighting, using gradient-echo acquisitions with long echo times to exploit differences in magnetic susceptibility, enables clear visualisation of iron-rich structures and small veins in the brain. The depiction of veins with diameters smaller than a pixel can be achieved, without the need for exogenous contrast agent, at field
∗ Corresponding author. Tel.: +44 115 9514747; fax: +44 115 9515166. E-mail addresses: [email protected] (J.E. Dixon), [email protected] (A. Simpson), [email protected] (N. Mistry), [email protected] (N. Evangelou), [email protected] (P.G. Morris). 1 Tel.: +44 115 9514747; fax: +44 115 9515166. 2 Tel.: +44 115 9709735; fax: +44 115 9709738. 0720-048X/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ejrad.2011.09.023
strengths as low as 1.5 T and relies on the paramagnetic nature of deoxyhaemoglobin [1]. In addition to the magnitude information, the phase of the MR signal in a T∗2 -weighted acquisition can be filtered to create an image, and the contrast in the magnitude image can be increased by multiplying it by a positive phase mask to create what are known as susceptibility-weighted images (SWI) [1–3], and by creating minimum intensity projections (mIPs) [1]. T∗2 -weighted imaging, and specifically the ability to visualise small veins, is of particular interest in the study of multiple sclerosis (MS). White-matter (WM) MS lesions are easily visible on MRI at conventional field strength, and so MRI is the main diagnostic test used in MS. However, although MRI is very sensitive to these WM abnormalities, its inherent specificity for MS lesions is poor. This is suggested to be due, in part, to the difficulties in differentiating between MS lesions (caused by demyelination) and lesions associated with ageing, vascular disease or hypertension, using current imaging methods [4]. Consequently, diagnostic criteria are necessary to refine the specificity of MRI findings by requiring lesions to be disseminated in space (according to anatomical locations) and time, often requiring follow-up scans to demonstrate a new lesion. Histological studies of MS have long since shown a close spatial relationship between MS lesions and small parenchymal veins [5] (in contrast, it is possible that ischaemic lesions, caused by lack of oxygenation, could be centred on narrow or blocked small arteries).
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However, in vivo study of this was previously limited by the difficulties in visualising both lesions and small veins in a single image. More recently, T∗2 -weighted MRI has proved useful in the detection of both lesions and veins. In 1999, Tan et al. [6] first investigated the perivenous nature of MS lesions in vivo, using T∗2 -weighted imaging to visualise lesions and SWI to detect veins, with the aid of gadolinium as a contrast agent, and found 94 of 95 detected lesions were centred on veins. In 2008, Hammond et al. [7] used T∗2 -weighted imaging at 7 T with a high in-plane resolution and 2-mm slice thickness to publish the first phase images of MS lesions, depicting veins within them in the same image. Ge et al. used magnitude T∗2 -weighted images with a similar voxel shape to find that all 80 lesions detected in 2 patients followed a vessel [8], while Tallantyre et al. used isotropic voxels (0.3 mm3 ) to image 8 patients and found a central vessel in 73 of 89 lesions (82%) [9]. However, in subsequent imaging of 1 patient at 3 T, Ge reported that the perivenous nature of the lesions was poorly detected. More recently, Tallantyre et al. compared T∗2 -weighted magnitude imaging at 3 T and 7 T and found that 3 T imaging detected veins in 45% of visible lesions, compared with 87% at 7 T [10]. WM lesions in elderly healthy volunteers showed a central vein in only 8% of lesions at 7 T, suggesting that perivenous lesion location could be a marker of demyelination. However, Lummel et al. found no significant difference in the likelihood of a lesion being perivenous between subjects with MS and a control group (80% vs. 78.2%) when using susceptibility-weighted angiography (SWAN) at 3 T, with 0.5-mm in-plane resolution and 2.6-mm slice thickness [11]. In this study, lesions smaller than 3 mm were excluded. In contrast, in 2011, Tallantyre et al. used 0.5-mm isotropic voxels in 7 T T∗2 -weighted imaging to find a significant difference in the proportion of perivenous lesions between MS and control groups [12] (80% vs. 19%, p < 0.001) when including lesions of all sizes (the mean lesion volume was 0.095 ml and 0.094 ml for the MS and control group, respectively). The proportion of lesions containing detectable veins was consistently higher in the MS group (53–100%) than in the control group (0–34%). It is clear that the choice of imaging parameters could have substantial impact on the ability of a technique to detect small veins. It is therefore important to establish the optimal scanning parameters, and to determine the sensitivity of the sequence to the detection of small veins, if the significance of the MRI findings is to be properly assessed. There is a substantial literature regarding the optimisation of T∗2 -weighted imaging for the detection of small veins. In 1998, Reichenbach et al. published empirical optimisations of T∗2 -weighted imaging at 1.5 T, and showed that, for veins with diameters larger than the in-plane voxel size, multiplication of the magnitude image by a phase mask (SWI) improves the visibility of veins [2]. Haacke et al. suggested that contrast in SWI may be increased, without a corresponding increase in acquisition time, by increasing the number of phase mask multiplications (m) rather than increasing the echo time (TE) [3]. In 2006, Xu and Haacke published theoretical and empirical optimisations of SWI and investigated the role of voxel aspect ratio on phase contrast [13]. This was added to by Deistung et al., who presented numerical simulations and experimental results for SWI at 7 T, comparing the signal in magnitude, phase and SWI, and suggesting optimal imaging parameters for visualisation of veins [14]. The authors confirmed earlier suggestions [15] of the importance of the orientation of the vessel with respect to the static field, and stated that visibility of very small vessels parallel to B0 may be reduced at 7 T. In the present work, SNR measurements and simulations are used to compare the sensitivity of magnitude (T∗2 -weighted) and SWI for the detection of small veins (<1 pixel in diameter), both with and without the use of gadolinium. The simulations
are used to predict the optimal scanning parameters in order to increase the sensitivity to these veins, at 3 T and 7 T, and to reduce the dependence on vessel orientation. The sensitivities of the sequences at both field strengths are compared, theoretically and experimentally, in order to quantify the benefit of imaging at ultra-high-field. Subjects with MS are scanned at both field strengths, using the optimised sequence parameters, as well as those used in previous work [10,12]. Optimisation of T∗2 -weighted imaging involves calculation of the optimal TE to be used at each field strength. Extending the TE allows the evolution of T∗2 contrast, which highlights the differences in susceptibility between blood and surrounding tissue, increasing the contrast between the two. However, the magnitude of the MR signal decays over the echo time so that a longer TE will result in less available signal, and hence a lower SNR. These considerations must be balanced so that the overall contrast-to-noise ratio (CNR) between the veins and tissue is maximised, enabling the detection of smaller vessels. As T∗2 decreases with increasing field strength, the TEs can be shortened at high-field, relative to lower fields, decreasing scan time and increasing SNR. A balance must also be sought between SNR and voxel size. Because of the small cross-section of the veins it is desired to detect, high resolution is required to visualise them; however, reducing the voxel size results in a loss in SNR, so that the vessels are less likely to be detected in the noisier image. The dependence of the susceptibility effect on the orientation of the vein with respect to the static magnetic field means that veins with an orientation perpendicular to the static magnetic field are more likely to be detected than the ones oriented parallel to it. With the aid of simulations, voxel volume and aspect ratio can be adjusted to minimise this effect, potentially increasing the accuracy of the technique. 2. Theory Modelling a vein as a cylinder of infinite length with radius rv , the change in resonant frequency (dω) for a spin inside a vein oriented at an angle to the static magnetic field (B0 ) can be calculated as [15]: dω =
dX 1 ω0 cos2 − 2 3
(1)
where ω0 is the Larmor frequency and dX is the susceptibility difference between the intra- and extra-vascular compartments. For a spin outside the vein, at a position r, ˚ from the centre of the vein, the change in frequency is given by: dω =
2
d rv ω0 2 r
sin2 cos(2˚)
(2)
The total signal within a voxel containing a vein (Sv ) after the echo time, TE, can then be calculated as a proportion of the initial amplitude of the signal (S0 ), by summing the contributions from all individual spin isochromats, j: S0 idωjTE (−TE/T∗2j ) e e N N
Sv =
(3)
j
whereas the signal from a voxel that does not contain a vein (Sn ) can be simply calculated by: ∗
Sn = S0 e−(TE/T2 (tissue))
(4)
where T∗2j is the transverse relaxation time at the position of the isochromat (i.e. of blood or WM) and T∗2 (tissue) is the transverse relaxation time in the tissue of interest (in this case, WM).
J.E. Dixon et al. / European Journal of Radiology 82 (2013) 719–727
For a vein to be considered detectable, we assume that the difference in signal caused by the vein must be greater than twice the standard deviation of the noise. This gives a threshold of Sn − Sv 2 > Sn SNR
(5)
For simulated susceptibility-weighted images, a similar threshold is used: SWIn − SWIv 2 > SWIn SNRSWI
(6)
where SWIv and SWIn are the signal in the SWI image for voxels with and without a vessel, respectively, and SNRSWI is the signal-to-noise ratio of the SWI image. SWI images are calculated according to SWI = Sh = Spm
(7)
where S is the signal in the magnitude image, p is the positive phase mask, and m is the number of multiplications of the phase mask. The value of the positive phase mask is calculated according to:
p=
1 1−
ϕ
− ≤ ϕ ≤ 0
(8)
0≤ϕ≤
According to Haacke et al. [3], the noise in the multiplied phase mask, dh, is given by dh =
m ϕ 1− n
m−1 dϕ
and dϕ =
dS S
Eq. (6) can then be rearranged to give:
1−
S − S n v S1
1−
pm − pm n
v
pm n
pn + m/ 2 > SNRmag pn
(9)
where SNRmag is the SNR in the magnitude image, and pv and pn correspond to the value of the positive phase mask for a voxel with and without a vessel, respectively. 3. Materials and methods Three patients with clinically definite MS were recruited: 2 male, 1 female; 2 secondary-progressive MS (SPMS), 1 relapsingremitting MS (RRMS); mean age 44 (range 27–62); mean disease duration 13.7 years (range 8–19); expanded disability status scale (EDSS) scores 1.5, 6.0 and 6.5. All subjects gave their informed consent and the study received ethical approval from the local research ethics committee. Two healthy volunteers (one male, one female, ages 26 and 31) were recruited by local advertisement for in vivo SNR measurements. All subjects gave their informed consent and the study received ethical approval from the local research ethics committee. 3.1. MR scanning 3 T scans were acquired using a Philips Acheiva 3 T system with a whole-body gradient set, whole-body transmit coil and 8-channel SENSE RF receive coil. Scanning at 7 T was performed using a Philips Acheiva 7 T scanner with whole-body gradient set, head-only transmit coil and 16-channel SENSE RF receive coil (NovaMedical). All sequences used a three-dimensional turbo-gradient-echo acquisition, with four overlapping and interleaved imaging stacks to reduce acquisition time. The original (pre-optimisation)
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sequences were those used in Tallantyre et al. [11,13] [TE = 20 ms, TR = 150 ms, flip angle = 14◦ , SENSE factor 2 (RL), EPI factor 3]. At 3 T, 0.8-mm isotropic voxels were used, with stacks overlapping by 3 mm to cover a volume of 192 × 164 × 95.2 mm3 in 7.2 min. At 7 T, 0.5-mm isotropic voxels were used, with stacks overlapping by 5 mm to cover a volume of 192 × 164 × 85 mm3 in 8.8 min. The TE and voxel shape/volume were varied for SNR measurements as described below, keeping all other parameters the same. The final optimised sequences, which were used to scan MS patients used 15-ms TE and 0.32 × 0.32 × 0.9 mm3 voxels (7 T) and 25-ms TE, 0.55 × 0.55 × 1.05 mm3 voxels (3 T). A fluid-attenuated inversion-recovery (FLAIR) sequence [256 × 204 × 140 mm3 FOV, 1 × 1 × 2.5 mm3 voxel size, TSE factor 27, 125-ms TE, 11-s TR, 2800-ms TI, 120◦ refocussing pulses, acquisition time 6 min] was also used to obtain images of the 3 MS patients. This was used to identify lesions. 3.2. Measurement of SNR: the dynamic noise scan method The traditional method of measuring SNR (the ratio of the magnitude of the signal in a sample to the standard deviation of the noise in a region of interest (ROI) outside the sample) is not possible using multi-element SENSE reconstruction, as the noise is not uniformly distributed throughout the image, and hence a background region is not representative of the SNR characteristics of an ROI within the image. An alternative approach, known as the ‘dynamic noise scan’ method [16], creates a second image with no RF excitation (and hence no signal) but is using the same parallel imaging processing. ‘Clean’ T∗2 -weighted images were acquired using the usual protocol, but with SENSE factor (R) set to 1.0 and CLEAR reconstruction turned off, in order to produce images with a constant noise level. The images were acquired with two dynamics – the second with no RF excitation – to create a magnitude image and a noise-only image. Two ROIs were selected in the WM and grey matter (GM) of the magnitude image, and the average signal in each was recorded. The standard deviation of the signal in the corresponding ROIs on the noise-only image was then recorded. Because this noise has a Rayleigh distribution, a correction was performed to determine the standard deviation of the normally distributed (Gaussian) noise, using Eq. (10): SDcorr =
SDmeans
2 − /2
≈ 1.526 SDmeans
(10)
The SNR in these ‘clean’ scans was then calculated by dividing the signal from the magnitude image by the corrected standard deviation of the noise in the corresponding ROI. A second scan is then performed with the required SENSE factor (2.0 in this case) so that the geometry factor (g) can be calculated. The SNR of this scan is then found from: SNRRED =
SNRCLEAN √ g R
(11)
where g is the average geometry factor inside the ROI. This method is too time-consuming to be performed in vivo for all the voxel sizes and TEs required for this study. The measurement was therefore performed in vivo using 1 × 1 × 1 mm3 voxels over the range of TEs required (5–50 ms), on two healthy volunteers. Measurements of SNR were then performed over a range of 10 voxel sizes using a phantom (with 20-ms TE), in order to confirm the effect of a change in resolution on the SNR, and the results then scaled to measurements obtained in vivo over a smaller range of voxel sizes using the same TE. The SNR vs. TE curve was then scaled to each voxel volume. In this way, SNR was determined for a range of TEs (5–50 ms) and voxel sizes (0.043–0.512 mm3 ).
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Table 1 T∗2 values (in ms) for venous and arterial blood and white-matter at 3 and 7 T. Refs. [17,18].
Venous blood Arterial blood White-matter
3T
7T
16 148 53.2
7.4 72 27.7
3.3. Simulations The signal change due to the presence of a vein in a voxel was then simulated for a range of vein radii (1–700 m), and orientations in the static field, using the theory discussed above. This was repeated over the range of echo times and voxel volumes and shapes (eight different aspect ratios for each shape). T∗2 values for WM were taken from Peters et al. [17] and those for venous and arterial blood were calculated according to Blockley et al. [18] (see Table 1). Susceptibility values for arterial and venous blood were calculated according to Xblood = 2.26 × Hct (1 − Y), where Hct is the haematocrit and Y is the blood oxygen saturation. This gives values of 0.36 ppm for venous blood and 0.05 for arterial blood, based on Y = 63.3% and 98% for venous and arterial blood, respectively and Hct = 40% [18]. Based on the SNR calculations, the smallest detectable vein was determined for each condition using Eq. (5). SWI images were simulated by creating positive phase masks (as discussed in Reichenbach et al. [1]) and multiplying this a number of times (m) with the magnitude information. The effect of administration of gadolinium was simulated based on the estimated concentration in the blood after the first pass of a single dose (0.1 ml/kg body weight). This was simulated for both arterial and venous blood, over the same range of echo times and voxel aspect ratios, using the susceptibility value for gadolinium XGd = 3.4 × 10−4 mol−1 l−1 . The scan parameters which allowed detection of the smallest veins, with minimal dependence on orientation and least sensitivity to arterial blood, were chosen. Three patients with confirmed MS were then scanned using the newly optimised sequences, as well as the ones previously used to demonstrate perivenous lesion orientation (see Tallantyre et al. [10,12]). Lesions were identified on the 3 T FLAIR image as areas of discrete hyperintensity, and lesion volumes recorded. The FLAIR image for each patient was registered to each T∗2 -weighted image using NeuROI [19] and lesion masks were then transformed onto the T∗2 images using the registration parameters. It was then determined which of the lesions were visible on each T∗2 image and the presence or absence of a central vessel was recorded. Vessels were hypointense and were counted only if they appeared linear in at least one plane and were completely surrounded by hyperintense signal in at least one plane.
Fig. 1. Radii of smallest detectable veins in magnitude T∗2 -weighted images acquired at 3 T and 7 T, using isotropic 0.512-mm3 and 0.125-mm3 voxels, as a function of TE. Values for veins perpendicular and parallel to the static field are shown, and a weighted average taken over 10 orientations to the static magnetic field, between 0◦ and 90◦ .
4. Results 4.1. Measurement of SNR The calculated SNR values for white-matter at 3 T and 7 T are shown in Table 2, for all voxel volumes used. 4.2. Simulations The radii of the smallest detectable veins in images acquired using isotropic voxels of side 0.8 mm (3 T) and 0.5 mm (7 T) (as used in Tallantyre et al. [10,12]) are shown as a function of TE for veins parallel and perpendicular to the static magnetic field in Fig. 1. The results for a weighted average orientation (average taken over 10 orientations from 0◦ to 90◦ to B0 , and weighted by sin , based on the assumption that vessels are randomly oriented) are also shown. It is clear that the sensitivity can be increased by altering the TE (an increase in TE at 3 T would yield higher sensitivity to veins; this would be achieved with a decrease in TE at 7 T). The results also demonstrate the dependence of the technique on vessel orientation. This orientation dependence is shown further in Figs. 2 and 3. Fig. 2 shows the radius of the smallest detectable vessel in previously used imaging sequences [10,12] (20-ms TE, 0.8-mm and 0.5-mm isotropic voxels at 3 T and 7 T, respectively) as a function of the vessel’s orientation to the static magnetic field. Vessels oriented perpendicular to the static magnetic field are considerably more likely to be detected than those parallel to it; this effect is particularly pronounced at 7 T. This bias towards vessels oriented perpendicular to the field can be altered by changing the shape of the voxel: the minimum radii for detectable veins are shown in Fig. 3, as a function of orientation
Table 2 SNR in white-matter as a function of TE and voxel volume in 3 T and 7 T T∗2 -weighted images. TE (ms)
Voxel volume 3T
50 40 35 30 25 20 15 10 5
7T
0.512 mm3
0.343 mm3
0.216 mm3
0.125 mm3
0.125 mm3
0.091 mm3
0.064 mm3
6.43 7.31 8.14 8.69 9.7 10.64 11.7 12.96 14.21
5.27 5.99 6.66 7.11 7.94 8.71 9.58 10.6 11.63
4.18 4.75 5.29 5.64 6.3 6.91 7.6 8.41 9.23
3.18 3.61 4.02 4.29 4.79 5.26 5.78 6.4 7.02
3.89 5.03 6.56 7.72 8.96 10.21 12.36 14.52 17.29
3.32 4.3 5.6 6.59 7.65 8.72 10.56 12.39 14.76
2.78 3.6 4.7 5.52 6.41 7.31 8.85 10.39 12.37
J.E. Dixon et al. / European Journal of Radiology 82 (2013) 719–727
Fig. 2. Radii of smallest detectable veins in magnitude T∗2 -weighted images acquired at 3 T and 7 T with the protocol used by Tallantyre et al. [10,12] (20-ms TE, 0.512-mm3 and 0.125-mm3 isotropic voxels at 3 T and 7 T, respectively) as a function of the orientation of the vein with respect to the static magnetic field, B0 .
with respect to the static field, for a range of voxel shapes (varying slice thickness and in-plane resolution, to keep voxel volume constant at 0.125 mm3 as in Figs. 1 and 2) in 7 T images using a 20-ms TE. An increase in slice thickness (and corresponding increase in in-plane resolution) reduces the sensitivity to veins perpendicular to the field, and therefore careful choice of voxel shape allows the bias towards these veins to be minimised. The effect of administration of gadolinium on the sensitivity of this technique to veins and arteries is shown in Fig. 4. The radii
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Fig. 3. Radii of smallest detectable veins as a function of orientation to the static magnetic field, B0 , for a range of voxel shapes (varying slice thickness and in-plane resolution, to keep voxel volume constant at 0.125 mm3 as in Figs. 1 and 2) in 7 T images using a 20-ms TE. The protocol used in Tallantyre et al. [10,12] used 0.5-mm slices (isotropic voxels).
of smallest detectable veins are shown (a weighted average calculated over 10 orientations between 0◦ and 90◦ to B0 , with error bars showing the range over the different orientations) as a function of slice thickness (keeping voxel volume constant at 0.125 mm3 for 7 T and 0.512 mm3 for 3 T) for a range of TEs (10–25 ms at 7 T, 20–40 ms at 3 T, in steps of 5 ms). The radii of smallest detectable arteries for each TE are shown as circles on each graph. These values are shown for 7 T (top) and 3 T (bottom), with no exogenous contrast agent (left) and after a simulated single dose of gadolinium (0.1 mmol/kg)
Fig. 4. Radii of smallest detectable vessels (weighted average taken over 10 orientations between 0◦ and 90◦ to the static field) as a function of slice thickness (for 0.125-mm3 voxels at 7 T and 0.512-mm3 voxels at 3 T) for a range of echo times (10–25 ms at 7 T, 20–40 ms at 3 T). Shown for veins (bars) and arteries (circles) with error bars showing upper and lower limits for veins over the range of orientations, with no exogenous contrast agent (left) and after a simulated single dose of gadolinium (right) at 3 T (top) and 7 T (bottom).
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Fig. 6. Radii of smallest detectable veins in 7 T images using chosen parameters after optimisation (0.32 × 0.32 × 0.90 mm3 voxels; 15-ms TE) as a function of orientation for SWI images made using positive phase masks (see Section 3 for explanation), with the number of multiplications of the phase mask (m) ranging from 0 (magnitude image) to 4.
4.3. Images The high in-plane resolution of the optimised images appeared to be of benefit for the visualisation of both lesions and veins. An example of the images obtained using isotropic and optimised acquisitions at 7 T is shown in Fig. 7. As predicted, the optimised sequence improved the detection of veins perpendicular to the imaging plane, as can be seen in Fig. 7H. Fig. 5. Radii of smallest detectable veins as a function of voxel aspect ratio, at 3 T (A) and 7 T (B), for a range of voxel volumes (0.216–0.512 mm3 at 3 T, and 0.064–0.125 mm3 at 7 T).
(right). The results show a slight increase in sensitivity to veins following administration of gadolinium; however, the corresponding increase in visibility of arteries is much larger, making the sensitivity of the technique to arteries similar to its sensitivity to veins. Thus, gadolinium is helpful to visualise the whole vasculature tree, but the discrimination between veins and arteries is lost. In addition, the visibility of arteries is shown to be more dependent on voxel aspect ratio than that of veins in the same image. At both field strengths, decreasing the voxel volume resulted in an increase in sensitivity, despite the loss of SNR (Fig. 5). At 7 T, a reduction in voxel volume of 27% (to 0.091 mm3 ) yielded higher sensitivity to veins than the original resolution (0.125 mm3 ). A further reduction (to 0.064 mm3 ) resulted in a reduction in sensitivity. At 3 T, the effect on sensitivity of a reduction in voxel volume is dependent on the voxel aspect ratio. For isotropic voxels, a 58% decrease in resolution to 0.216 mm3 allowed detection of veins smaller than those detectable using 0.343-mm3 or 0.512-mm3 voxels, while for highly anisotropic voxels, the lower resolution yielded higher sensitivity. 0.216-mm3 isotropic voxels gave the highest predicted sensitivity; however, this resulted in considerable bias towards detection of vessels perpendicular to the B0 field. When using a voxel aspect ratio of 0.35, which results in less dependence on vessel orientation, a voxel volume of 0.343 mm3 yielded highest sensitivity. The final parameters chosen, based on consideration of all the results above, were voxel sizes of 0.32 × 0.32 × 0.90 mm3 and 0.57 × 0.57 × 1.05 mm3 at 7 T and 3 T, respectively, with echo times of 15 ms and 25 ms. The radii of smallest detectable veins in SWI images created using these chosen parameters are shown in Fig. 6. It is apparent that the use of the phase mask does not increase the visibility of these very small veins at this field strength.
4.4. Vessel detection A total of 214 lesions were detected on the 3 T FLAIR images of 3 patients (average 71 per patient, range 41–114; mean lesion volume 138 mm3 ). The previously used 3 T and 7 T T∗2 -weighted sequences (isotropic voxels, 20-ms TE) allowed detection of 53% and 66% of these lesions, respectively; the optimised sequences demonstrated 71% (3 T) and 72% (7 T), respectively. A vein could be visualised in 56% of visible lesions in the previously used 3 T sequences (average across patients 55%, range 46–68%) and 89% using the equivalent 7 T sequence (mean 87%, range 81–92%). This is similar to results in Tallantyre et al. [10] in which 45% of lesions showed a detectable vessel at 3 T, and 87% at 7 T. This sensitivity was increased by the optimisation: 82% of visible lesions at 3 T contained a detectable vessel using the optimised sequence (mean 80%, range 75–83%) and 95% at 7 T (mean 95%, range 90–98%). The proportion of lesions containing visible vessels on each image is shown for each sequence in Table 3, and represented graphically in Fig. 8. As would be expected, larger lesions were more likely to contain a visible vessel (Fig. 9); however, vessels were detected in at least 70% of detectable lesions with volume smaller than 5 mm3 on both 7 T scans. In 3 T images using the optimised sequence, vessels could be seen in 50% of lesions smaller than 5 mm3 . 5. Discussion We have demonstrated clearly the increase in sensitivity that can be obtained by altering the echo time at both 3 T and 7 T (Fig. 1). Ultra-high-field imaging is of benefit when seeking to visualise small veins: vessels with cross-sectional areas 4 times smaller can be detected at 7 T compared to 3 T, using isotropic voxels. This figure also shows the higher sensitivity to veins perpendicular to
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Fig. 7. Example T∗2 -weighted images of Patient 3 (male, RRMS, age 27 years, EDSS 1.5, disease duration 8 years) acquired using the previously used [10,12] 3 T protocol (A and E; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (B and F; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (C and G; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (D and H; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels).
B0 compared to those parallel to the field. This bias towards detection of veins with particular orientation can be exploited or minimised significantly without increasing scan time, by altering the voxel aspect ratio, as shown in Fig. 3. However, the benefit of this must be weighed against the ability to easily view the image in all three perpendicular planes; furthermore, when altering voxel shape to reduce orientation dependence, the imaging plane must itself be kept perpendicular to B0 . The administration of exogenous contrast agents such as gadolinium increases the phase effect around blood vessels, increasing their visibility; it also increases the dependence on vessel orientation. Fig. 4 shows clearly that administration of gadolinium, while slightly increasing the sensitivity to small veins, would significantly increase the detection of arteries within lesions and may therefore reduce the efficacy of the technique for differentiating between lesions caused by demyelination and those caused by ischaemia. It is recommended for this technique, then,
Fig. 8. Total number of lesions detected (in 3 patients), and proportion of those lesions containing a detectable vessel for images acquired using the previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels). Labels show percentage of detectable lesions containing visible vessel(s).
that the inherent susceptibility around veins in high-field images is exploited without the introduction of exogenous contrast agents. When all vessel orientations are taken into account, the creation of susceptibility-weighted images to increase vessel visibility as discussed by Haacke et al., appears not to be beneficial for the detection of very small veins such as these, with a diameter much smaller than a pixel (Fig. 6). This is because the increase in noise produced by multiplication of the phase mask outweighs the increase in contrast surrounding veins. Furthermore, multiplication by the phase mask increases orientation dependence and is therefore not preferred for this application. Typical images acquired using the optimised sequences are shown in Fig. 7, alongside images acquired with the previously used [10,12] sequences. The benefit of imaging at ultra-high-field is apparent and, as can be seen in Fig. 7H, the optimisation improves
Fig. 9. Percentage of detectable lesions containing a visible vessel as a function of lesion volume as outlined on 3 T FLAIR images, for the previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels).
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Table 3 Numbers of lesions detected using previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used 7 T protocol [10,12] (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels). Patient 1 – male, SPMS, age = 44 years, EDSS = 6.5, disease duration = 19 years 59 lesions 3 T ISO
3 T OPT
7 T ISO
7 T OPT
Detected Containing vein C S M No vein A N
34 23 7 7 9 11 1 10
41 34 20 4 10 7 2 5
37 30 19 1 10 7 4 3
42 38 24 2 12 4 3 1
Percentage containing vein
68%
83%
81%
90%
3 T OPT
7 T ISO
7 T OPT
Patient 2 – female, SPMS, age = 62 years, EDSS = 6.0, disease duration = 14 years 41 lesions 3 T ISO Detected Containing vein C S M No vein A N
24 11 2 6 3 13 4 9
28 21 6 13 2 7 5 2
26 23 10 7 6 3 2 1
27 26 7 8 11 1 0 1
Percentage containing vein
46%
75%
88%
96%
3 T OPT
7 T ISO
7 T OPT
Patient 3 – male, RRMS, age = 27 years, EDSS = 1.5, disease duration = 8 years 114 lesions 3 T ISO Detected Containing vein C S M No vein A N
55 29 19 7 3 26 8 18
84 70 52 11 7 14 4 10
78 72 59 2 11 6 2 4
85 83 71 7 5 2 2 0
Percentage containing vein
53%
83%
92%
98%
3 T ISO
3 T OPT
7 T ISO
7 T OPT
113 63 28 20 15 50 13 37
153 125 78 28 19 28 11 17
141 125 88 10 27 16 8 8
154 147 102 17 28 7 5 2
Total 214 lesions
Detected Containing vein C S M No vein A N Percentage containing vein
56%
visualisation of veins parallel to the static magnetic field (perpendicular to the imaging plane). The number of lesions visible on T∗2 -weighted images using the original sequences (53% at 3 T and 66% at 7 T) was lower than previously reported [10] (89% and 94% respectively), despite a similar mean voxel volume; it is not clear why this is the case. However, the proportion of lesions containing visible vessels was similar to those previously reported (56% and 89% cf. 45% and 87%). The optimisations discussed here resulted in a much higher sensitivity to vessels; this improvement was most pronounced
82%
89%
95%
at 3 T, where the proportion of lesions containing detectable vessels increased from 56% to 82% – yielding a similar sensitivity to the previously used 7 T sequence which has been shown to allow discrimination between MS lesions and asymptomatic white-matter lesions. The percentage of FLAIR lesions detected on T∗2 -weighted images at 7 T increased from 66% to 72%, and the proportion of perivenous lesions increased from 89% to 95%. The likelihood of detecting a vein within a lesion in 7 T imaging appears to be less dependent on lesion volume than at lower field.
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While identifying lesions and veins in optimised 7 T images, the authors noticed many small lesions which had not been detected on the 3 T FLAIR; furthermore, many large lesions on the FLAIR images appeared on the 7 T T∗2 -weighted image to be a collection of smaller lesions (this has been demonstrated previously using 7 T MP-RAGE images in Mistry et al. [20]). Further work to improve the reliability of the lesion map may therefore improve this technique.
Acknowledgements
6. Conclusions
[1] Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 1997;204:272–7. [2] Reichenbach JR, Essig M, Haacke EM, et al. High-resolution venography of the brain using magnetic resonance imaging. MAGMA 1998;6:6–69. [3] Haacke EM, Xu Y, Cheng YCN, Reichenbach JR. Susceptibility-weighted imaging (SWI). Magn Reson Med 2004;52:612–8. [4] Fazekas F, Offenbacher H, Fuchs S, et al. Criteria for an increased specificity of MRI interpretation in elderly subjects with suspected multiple sclerosis. Neurology 1988;38(12):1822–5. [5] Buss O. Beitrag zue Aetiologie und Pathologie der multiplen Sklerose des Hirns und Rückenmarks. Dtsch Arch Klin Med 1889;45:555. [6] Tan IL, van Schijndel RA, Pouwels PJW, et al. MR venography of multiple sclerosis. Am J Neuroradiol 2000;21:1039–42. [7] Hammond KE, Lupo JM, Xu D, et al. Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. NeuroImage 2008;39(4):1682–92. [8] Ge Y, Zohrabian VM, Grossman RI. Seven-Tesla magnetic resonance imaging: new vision of microvascular abnormalities in multiple sclerosis. Arch Neurol 2008;65(6):812–6. [9] Tallantyre EC, Brookes MJ, Dixon JE, Morgan PS, Evangelou N, Morris PG. Demonstrating the perivascular distribution of MS lesions in vivo with 7-Tesla MRI. Neurology 2008;70(22):2076–8. [10] Tallantyre EC, Morgan PS, Dixon JE, et al. A comparison of 3 T and 7 T in the detection of small parenchymal veins within MS lesions. Invest Radiol 2009;44(9):491–4. [11] Lummel N, Boeckh-Behrens T, Schoepf V, Burke M, Brückmann H, Linn J. Presence of a central vein within white matter lesions on susceptibility weighted imaging: a specific finding for multiple sclerosis? Neuroradiology 2011;53(5):311–7. [12] Tallantyre EC, Dixon JE, Donaldson I, et al. Ultra-high-field imaging distinguishes MS lesions from asymptomatic white matter lesions. Neurology 2011;76(6):534–9. [13] Xu Y, Haacke EM. The role of voxel aspect ratio in determining apparent vascular phase behaviour in susceptibility weighted imaging. Magn Reson Imaging 2006;24:155–60. [14] Deistung A, Rauscher A, Sedlacik J, Stadler J, Witoszynskyj S, Reichenbach JR. Susceptibility weighted imaging at ultra high magnetic field strengths: theoretical considerations and experimental results. Magn Reson Med 2008;60:1155–68. [15] Yablonskiy DA, Haacke EM. Theory of NMR signal behaviour in magnetically inhomogeneous tissues: the static dephasing regime. Magn Reson Med 1994;32:749–63. [16] Philips Healthcare, personal communication. [17] Peters AM, Brookes MJ, Hoogenraad FG, et al. T∗2 measurements in human brain at 1.5, 3 and 7 T. Magn Reson Imaging 2007;25:748–53. [18] Blockley NP, Jiang L, Gardener AG, Ludman CN, Francis ST, Gowland PA. Field strength dependence of R1 and R*2 relaxivities of human whole blood to prohance, vasovist, and deoxyhemoglobin. Magn Reson Med 2008;60:1313–20. [19] Tench, C. NeuROI. Available at: http://www.nottingham.ac.uk/scs/divisions/ clinicalneurology/software/neuroi.aspx. [20] Mistry N, Tallantyre EC, Dixon JE, et al. Focal multiple sclerosis lesions abound in ‘normal appearing white matter’. Mult Scler 2011;17(11): 1313–23.
The sensitivity of T∗2 -weighted imaging to blood vessels and the dependence of this on vessel orientation are determined by many things, including the volume and shape of the voxels acquired, the TE used, the administration of contrast agent such as gadolinium, and the multiplication of phase information into the image. Here, SNR has been measured in T∗2 -weighted MR images at 3 and 7 T and these measurements have been used to calculate a threshold for the signal change required in order to detect a voxel containing a small vein or artery. The signal change due to a vessel within a voxel in these images has been simulated over a range of TEs, voxel volumes and voxel aspect ratios, for vessels at a range of orientations to the static magnetic field, and the radius of the smallest detectable vessel predicted. Optimal sequence parameters have been predicted to allow detection of small veins within MS lesions, and hence discrimination between lesions containing veins and those containing small arteries, or no vessel. The chosen parameters at 7 T were a TE of 15 ms, and voxel volume of 0.32 × 0.32 × 0.9 mm3 . At 3 T, a voxel volume of 0.57 × 0.57 × 1.05 mm3 was chosen, with a TE of 25 ms. In both cases, the imaging slab is to be oriented perpendicular to the static field. The administration of gadolinium and the creation of SWI images are not thought to be of benefit to this technique. These optimised sequences have been demonstrated and compared to sequences previously used in the investigation of the perivenous nature of MS lesions [10] and the differentiation between MS lesions and asymptomatic WM lesions [12], and show a significant improvement in the number of vessels being detected. 7 T imaging is superior to 3 T for detection of both lesions and vessels; however, the optimisations discussed here yield a sensitivity at 3 T comparable to that of the previously used 7 T sequence, suggesting that this technique could be implemented at 3 T. Further work to determine the efficacy of the optimised technique at both field strengths for differentiation between MS and other WM lesions would be of benefit. Conflict of interest The authors have no conflicts of interest.
This study was funded by research grants from the UK Multiple Sclerosis Society (grant number 919) and Medical Research Council (grant number G0700584), and a charitable donation from Helen Foster and Eleanor Duthie in support of MS research. References
Contents lists available at SciVerse ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Optimisation of T∗2 -weighted MRI for the detection of small veins in multiple sclerosis at 3 T and 7 T Jennifer Elizabeth Dixon a,1 , Ashley Simpson b,2 , Niraj Mistry b,2 , Nikos Evangelou b,2 , Peter Gordon Morris a,∗ a b
Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, NG7 2RD, UK Academic Division of Clinical Neurology, University of Nottingham, Queen’s Medical Centre, Nottingham, NG7 2UH, UK
a r t i c l e
i n f o
Article history: Received 24 August 2011 Accepted 19 September 2011 Keywords: Multiple sclerosis MRI Magnetic susceptibility Small veins Diagnosis 7T
a b s t r a c t T∗2 -weighted magnetic resonance imaging at 7 T has recently been shown to allow differentiation between white-matter multiple sclerosis lesions and asymptomatic white-matter lesions, by the presence or absence of a detectable central blood vessel. The aim of the present work is to improve the technique by increasing the sensitivity to veins at both 3 T and 7 T, and to assess the benefit of ultra-high-field imaging. Signal-to-noise ratio (SNR) measurements and simulations are used to compare the sensitivity of magnitude T∗2 -weighted and susceptibility-weighted images for the detection of small veins (<1 pixel in diameter), both with and without the use of gadolinium. The simulations are used to predict the optimal scanning parameters in order to increase the sensitivity to these veins at both field strengths, and to reduce the inherent dependence on vessel orientation. The sensitivities of the sequences at both field strengths are compared, theoretically and experimentally, in order to quantify the benefit of imaging at ultra-high-field. Subjects with multiple sclerosis (MS) are scanned at both field strengths, using the optimised sequence parameters, as well as those used in previously published work, and the optimisation is shown to improve the detection of veins within lesions. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction There has been a great deal of recent interest in magnetic resonance imaging (MRI) at ultra-high-field (7 T). The higher signal-to-noise ratio (SNR) available at 7 T permits scanning at much higher spatial resolution than is possible at conventional field strengths (i.e. 1.5 T, 3 T), and the different relaxation times lead to enhanced contrast. In particular, the decrease in T 2 relative to T2 at 7 T greatly improves contrast in T∗2 -weighted imaging (T 2 is the refocusable component of the transverse relaxation, T∗2 , while T2 denotes the random, unrefocusable component). T∗2 -weighting, using gradient-echo acquisitions with long echo times to exploit differences in magnetic susceptibility, enables clear visualisation of iron-rich structures and small veins in the brain. The depiction of veins with diameters smaller than a pixel can be achieved, without the need for exogenous contrast agent, at field
∗ Corresponding author. Tel.: +44 115 9514747; fax: +44 115 9515166. E-mail addresses: [email protected] (J.E. Dixon), [email protected] (A. Simpson), [email protected] (N. Mistry), [email protected] (N. Evangelou), [email protected] (P.G. Morris). 1 Tel.: +44 115 9514747; fax: +44 115 9515166. 2 Tel.: +44 115 9709735; fax: +44 115 9709738. 0720-048X/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ejrad.2011.09.023
strengths as low as 1.5 T and relies on the paramagnetic nature of deoxyhaemoglobin [1]. In addition to the magnitude information, the phase of the MR signal in a T∗2 -weighted acquisition can be filtered to create an image, and the contrast in the magnitude image can be increased by multiplying it by a positive phase mask to create what are known as susceptibility-weighted images (SWI) [1–3], and by creating minimum intensity projections (mIPs) [1]. T∗2 -weighted imaging, and specifically the ability to visualise small veins, is of particular interest in the study of multiple sclerosis (MS). White-matter (WM) MS lesions are easily visible on MRI at conventional field strength, and so MRI is the main diagnostic test used in MS. However, although MRI is very sensitive to these WM abnormalities, its inherent specificity for MS lesions is poor. This is suggested to be due, in part, to the difficulties in differentiating between MS lesions (caused by demyelination) and lesions associated with ageing, vascular disease or hypertension, using current imaging methods [4]. Consequently, diagnostic criteria are necessary to refine the specificity of MRI findings by requiring lesions to be disseminated in space (according to anatomical locations) and time, often requiring follow-up scans to demonstrate a new lesion. Histological studies of MS have long since shown a close spatial relationship between MS lesions and small parenchymal veins [5] (in contrast, it is possible that ischaemic lesions, caused by lack of oxygenation, could be centred on narrow or blocked small arteries).
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However, in vivo study of this was previously limited by the difficulties in visualising both lesions and small veins in a single image. More recently, T∗2 -weighted MRI has proved useful in the detection of both lesions and veins. In 1999, Tan et al. [6] first investigated the perivenous nature of MS lesions in vivo, using T∗2 -weighted imaging to visualise lesions and SWI to detect veins, with the aid of gadolinium as a contrast agent, and found 94 of 95 detected lesions were centred on veins. In 2008, Hammond et al. [7] used T∗2 -weighted imaging at 7 T with a high in-plane resolution and 2-mm slice thickness to publish the first phase images of MS lesions, depicting veins within them in the same image. Ge et al. used magnitude T∗2 -weighted images with a similar voxel shape to find that all 80 lesions detected in 2 patients followed a vessel [8], while Tallantyre et al. used isotropic voxels (0.3 mm3 ) to image 8 patients and found a central vessel in 73 of 89 lesions (82%) [9]. However, in subsequent imaging of 1 patient at 3 T, Ge reported that the perivenous nature of the lesions was poorly detected. More recently, Tallantyre et al. compared T∗2 -weighted magnitude imaging at 3 T and 7 T and found that 3 T imaging detected veins in 45% of visible lesions, compared with 87% at 7 T [10]. WM lesions in elderly healthy volunteers showed a central vein in only 8% of lesions at 7 T, suggesting that perivenous lesion location could be a marker of demyelination. However, Lummel et al. found no significant difference in the likelihood of a lesion being perivenous between subjects with MS and a control group (80% vs. 78.2%) when using susceptibility-weighted angiography (SWAN) at 3 T, with 0.5-mm in-plane resolution and 2.6-mm slice thickness [11]. In this study, lesions smaller than 3 mm were excluded. In contrast, in 2011, Tallantyre et al. used 0.5-mm isotropic voxels in 7 T T∗2 -weighted imaging to find a significant difference in the proportion of perivenous lesions between MS and control groups [12] (80% vs. 19%, p < 0.001) when including lesions of all sizes (the mean lesion volume was 0.095 ml and 0.094 ml for the MS and control group, respectively). The proportion of lesions containing detectable veins was consistently higher in the MS group (53–100%) than in the control group (0–34%). It is clear that the choice of imaging parameters could have substantial impact on the ability of a technique to detect small veins. It is therefore important to establish the optimal scanning parameters, and to determine the sensitivity of the sequence to the detection of small veins, if the significance of the MRI findings is to be properly assessed. There is a substantial literature regarding the optimisation of T∗2 -weighted imaging for the detection of small veins. In 1998, Reichenbach et al. published empirical optimisations of T∗2 -weighted imaging at 1.5 T, and showed that, for veins with diameters larger than the in-plane voxel size, multiplication of the magnitude image by a phase mask (SWI) improves the visibility of veins [2]. Haacke et al. suggested that contrast in SWI may be increased, without a corresponding increase in acquisition time, by increasing the number of phase mask multiplications (m) rather than increasing the echo time (TE) [3]. In 2006, Xu and Haacke published theoretical and empirical optimisations of SWI and investigated the role of voxel aspect ratio on phase contrast [13]. This was added to by Deistung et al., who presented numerical simulations and experimental results for SWI at 7 T, comparing the signal in magnitude, phase and SWI, and suggesting optimal imaging parameters for visualisation of veins [14]. The authors confirmed earlier suggestions [15] of the importance of the orientation of the vessel with respect to the static field, and stated that visibility of very small vessels parallel to B0 may be reduced at 7 T. In the present work, SNR measurements and simulations are used to compare the sensitivity of magnitude (T∗2 -weighted) and SWI for the detection of small veins (<1 pixel in diameter), both with and without the use of gadolinium. The simulations
are used to predict the optimal scanning parameters in order to increase the sensitivity to these veins, at 3 T and 7 T, and to reduce the dependence on vessel orientation. The sensitivities of the sequences at both field strengths are compared, theoretically and experimentally, in order to quantify the benefit of imaging at ultra-high-field. Subjects with MS are scanned at both field strengths, using the optimised sequence parameters, as well as those used in previous work [10,12]. Optimisation of T∗2 -weighted imaging involves calculation of the optimal TE to be used at each field strength. Extending the TE allows the evolution of T∗2 contrast, which highlights the differences in susceptibility between blood and surrounding tissue, increasing the contrast between the two. However, the magnitude of the MR signal decays over the echo time so that a longer TE will result in less available signal, and hence a lower SNR. These considerations must be balanced so that the overall contrast-to-noise ratio (CNR) between the veins and tissue is maximised, enabling the detection of smaller vessels. As T∗2 decreases with increasing field strength, the TEs can be shortened at high-field, relative to lower fields, decreasing scan time and increasing SNR. A balance must also be sought between SNR and voxel size. Because of the small cross-section of the veins it is desired to detect, high resolution is required to visualise them; however, reducing the voxel size results in a loss in SNR, so that the vessels are less likely to be detected in the noisier image. The dependence of the susceptibility effect on the orientation of the vein with respect to the static magnetic field means that veins with an orientation perpendicular to the static magnetic field are more likely to be detected than the ones oriented parallel to it. With the aid of simulations, voxel volume and aspect ratio can be adjusted to minimise this effect, potentially increasing the accuracy of the technique. 2. Theory Modelling a vein as a cylinder of infinite length with radius rv , the change in resonant frequency (dω) for a spin inside a vein oriented at an angle to the static magnetic field (B0 ) can be calculated as [15]: dω =
dX 1 ω0 cos2 − 2 3
(1)
where ω0 is the Larmor frequency and dX is the susceptibility difference between the intra- and extra-vascular compartments. For a spin outside the vein, at a position r, ˚ from the centre of the vein, the change in frequency is given by: dω =
2
d rv ω0 2 r
sin2 cos(2˚)
(2)
The total signal within a voxel containing a vein (Sv ) after the echo time, TE, can then be calculated as a proportion of the initial amplitude of the signal (S0 ), by summing the contributions from all individual spin isochromats, j: S0 idωjTE (−TE/T∗2j ) e e N N
Sv =
(3)
j
whereas the signal from a voxel that does not contain a vein (Sn ) can be simply calculated by: ∗
Sn = S0 e−(TE/T2 (tissue))
(4)
where T∗2j is the transverse relaxation time at the position of the isochromat (i.e. of blood or WM) and T∗2 (tissue) is the transverse relaxation time in the tissue of interest (in this case, WM).
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For a vein to be considered detectable, we assume that the difference in signal caused by the vein must be greater than twice the standard deviation of the noise. This gives a threshold of Sn − Sv 2 > Sn SNR
(5)
For simulated susceptibility-weighted images, a similar threshold is used: SWIn − SWIv 2 > SWIn SNRSWI
(6)
where SWIv and SWIn are the signal in the SWI image for voxels with and without a vessel, respectively, and SNRSWI is the signal-to-noise ratio of the SWI image. SWI images are calculated according to SWI = Sh = Spm
(7)
where S is the signal in the magnitude image, p is the positive phase mask, and m is the number of multiplications of the phase mask. The value of the positive phase mask is calculated according to:
p=
1 1−
ϕ
− ≤ ϕ ≤ 0
(8)
0≤ϕ≤
According to Haacke et al. [3], the noise in the multiplied phase mask, dh, is given by dh =
m ϕ 1− n
m−1 dϕ
and dϕ =
dS S
Eq. (6) can then be rearranged to give:
1−
S − S n v S1
1−
pm − pm n
v
pm n
pn + m/ 2 > SNRmag pn
(9)
where SNRmag is the SNR in the magnitude image, and pv and pn correspond to the value of the positive phase mask for a voxel with and without a vessel, respectively. 3. Materials and methods Three patients with clinically definite MS were recruited: 2 male, 1 female; 2 secondary-progressive MS (SPMS), 1 relapsingremitting MS (RRMS); mean age 44 (range 27–62); mean disease duration 13.7 years (range 8–19); expanded disability status scale (EDSS) scores 1.5, 6.0 and 6.5. All subjects gave their informed consent and the study received ethical approval from the local research ethics committee. Two healthy volunteers (one male, one female, ages 26 and 31) were recruited by local advertisement for in vivo SNR measurements. All subjects gave their informed consent and the study received ethical approval from the local research ethics committee. 3.1. MR scanning 3 T scans were acquired using a Philips Acheiva 3 T system with a whole-body gradient set, whole-body transmit coil and 8-channel SENSE RF receive coil. Scanning at 7 T was performed using a Philips Acheiva 7 T scanner with whole-body gradient set, head-only transmit coil and 16-channel SENSE RF receive coil (NovaMedical). All sequences used a three-dimensional turbo-gradient-echo acquisition, with four overlapping and interleaved imaging stacks to reduce acquisition time. The original (pre-optimisation)
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sequences were those used in Tallantyre et al. [11,13] [TE = 20 ms, TR = 150 ms, flip angle = 14◦ , SENSE factor 2 (RL), EPI factor 3]. At 3 T, 0.8-mm isotropic voxels were used, with stacks overlapping by 3 mm to cover a volume of 192 × 164 × 95.2 mm3 in 7.2 min. At 7 T, 0.5-mm isotropic voxels were used, with stacks overlapping by 5 mm to cover a volume of 192 × 164 × 85 mm3 in 8.8 min. The TE and voxel shape/volume were varied for SNR measurements as described below, keeping all other parameters the same. The final optimised sequences, which were used to scan MS patients used 15-ms TE and 0.32 × 0.32 × 0.9 mm3 voxels (7 T) and 25-ms TE, 0.55 × 0.55 × 1.05 mm3 voxels (3 T). A fluid-attenuated inversion-recovery (FLAIR) sequence [256 × 204 × 140 mm3 FOV, 1 × 1 × 2.5 mm3 voxel size, TSE factor 27, 125-ms TE, 11-s TR, 2800-ms TI, 120◦ refocussing pulses, acquisition time 6 min] was also used to obtain images of the 3 MS patients. This was used to identify lesions. 3.2. Measurement of SNR: the dynamic noise scan method The traditional method of measuring SNR (the ratio of the magnitude of the signal in a sample to the standard deviation of the noise in a region of interest (ROI) outside the sample) is not possible using multi-element SENSE reconstruction, as the noise is not uniformly distributed throughout the image, and hence a background region is not representative of the SNR characteristics of an ROI within the image. An alternative approach, known as the ‘dynamic noise scan’ method [16], creates a second image with no RF excitation (and hence no signal) but is using the same parallel imaging processing. ‘Clean’ T∗2 -weighted images were acquired using the usual protocol, but with SENSE factor (R) set to 1.0 and CLEAR reconstruction turned off, in order to produce images with a constant noise level. The images were acquired with two dynamics – the second with no RF excitation – to create a magnitude image and a noise-only image. Two ROIs were selected in the WM and grey matter (GM) of the magnitude image, and the average signal in each was recorded. The standard deviation of the signal in the corresponding ROIs on the noise-only image was then recorded. Because this noise has a Rayleigh distribution, a correction was performed to determine the standard deviation of the normally distributed (Gaussian) noise, using Eq. (10): SDcorr =
SDmeans
2 − /2
≈ 1.526 SDmeans
(10)
The SNR in these ‘clean’ scans was then calculated by dividing the signal from the magnitude image by the corrected standard deviation of the noise in the corresponding ROI. A second scan is then performed with the required SENSE factor (2.0 in this case) so that the geometry factor (g) can be calculated. The SNR of this scan is then found from: SNRRED =
SNRCLEAN √ g R
(11)
where g is the average geometry factor inside the ROI. This method is too time-consuming to be performed in vivo for all the voxel sizes and TEs required for this study. The measurement was therefore performed in vivo using 1 × 1 × 1 mm3 voxels over the range of TEs required (5–50 ms), on two healthy volunteers. Measurements of SNR were then performed over a range of 10 voxel sizes using a phantom (with 20-ms TE), in order to confirm the effect of a change in resolution on the SNR, and the results then scaled to measurements obtained in vivo over a smaller range of voxel sizes using the same TE. The SNR vs. TE curve was then scaled to each voxel volume. In this way, SNR was determined for a range of TEs (5–50 ms) and voxel sizes (0.043–0.512 mm3 ).
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Table 1 T∗2 values (in ms) for venous and arterial blood and white-matter at 3 and 7 T. Refs. [17,18].
Venous blood Arterial blood White-matter
3T
7T
16 148 53.2
7.4 72 27.7
3.3. Simulations The signal change due to the presence of a vein in a voxel was then simulated for a range of vein radii (1–700 m), and orientations in the static field, using the theory discussed above. This was repeated over the range of echo times and voxel volumes and shapes (eight different aspect ratios for each shape). T∗2 values for WM were taken from Peters et al. [17] and those for venous and arterial blood were calculated according to Blockley et al. [18] (see Table 1). Susceptibility values for arterial and venous blood were calculated according to Xblood = 2.26 × Hct (1 − Y), where Hct is the haematocrit and Y is the blood oxygen saturation. This gives values of 0.36 ppm for venous blood and 0.05 for arterial blood, based on Y = 63.3% and 98% for venous and arterial blood, respectively and Hct = 40% [18]. Based on the SNR calculations, the smallest detectable vein was determined for each condition using Eq. (5). SWI images were simulated by creating positive phase masks (as discussed in Reichenbach et al. [1]) and multiplying this a number of times (m) with the magnitude information. The effect of administration of gadolinium was simulated based on the estimated concentration in the blood after the first pass of a single dose (0.1 ml/kg body weight). This was simulated for both arterial and venous blood, over the same range of echo times and voxel aspect ratios, using the susceptibility value for gadolinium XGd = 3.4 × 10−4 mol−1 l−1 . The scan parameters which allowed detection of the smallest veins, with minimal dependence on orientation and least sensitivity to arterial blood, were chosen. Three patients with confirmed MS were then scanned using the newly optimised sequences, as well as the ones previously used to demonstrate perivenous lesion orientation (see Tallantyre et al. [10,12]). Lesions were identified on the 3 T FLAIR image as areas of discrete hyperintensity, and lesion volumes recorded. The FLAIR image for each patient was registered to each T∗2 -weighted image using NeuROI [19] and lesion masks were then transformed onto the T∗2 images using the registration parameters. It was then determined which of the lesions were visible on each T∗2 image and the presence or absence of a central vessel was recorded. Vessels were hypointense and were counted only if they appeared linear in at least one plane and were completely surrounded by hyperintense signal in at least one plane.
Fig. 1. Radii of smallest detectable veins in magnitude T∗2 -weighted images acquired at 3 T and 7 T, using isotropic 0.512-mm3 and 0.125-mm3 voxels, as a function of TE. Values for veins perpendicular and parallel to the static field are shown, and a weighted average taken over 10 orientations to the static magnetic field, between 0◦ and 90◦ .
4. Results 4.1. Measurement of SNR The calculated SNR values for white-matter at 3 T and 7 T are shown in Table 2, for all voxel volumes used. 4.2. Simulations The radii of the smallest detectable veins in images acquired using isotropic voxels of side 0.8 mm (3 T) and 0.5 mm (7 T) (as used in Tallantyre et al. [10,12]) are shown as a function of TE for veins parallel and perpendicular to the static magnetic field in Fig. 1. The results for a weighted average orientation (average taken over 10 orientations from 0◦ to 90◦ to B0 , and weighted by sin , based on the assumption that vessels are randomly oriented) are also shown. It is clear that the sensitivity can be increased by altering the TE (an increase in TE at 3 T would yield higher sensitivity to veins; this would be achieved with a decrease in TE at 7 T). The results also demonstrate the dependence of the technique on vessel orientation. This orientation dependence is shown further in Figs. 2 and 3. Fig. 2 shows the radius of the smallest detectable vessel in previously used imaging sequences [10,12] (20-ms TE, 0.8-mm and 0.5-mm isotropic voxels at 3 T and 7 T, respectively) as a function of the vessel’s orientation to the static magnetic field. Vessels oriented perpendicular to the static magnetic field are considerably more likely to be detected than those parallel to it; this effect is particularly pronounced at 7 T. This bias towards vessels oriented perpendicular to the field can be altered by changing the shape of the voxel: the minimum radii for detectable veins are shown in Fig. 3, as a function of orientation
Table 2 SNR in white-matter as a function of TE and voxel volume in 3 T and 7 T T∗2 -weighted images. TE (ms)
Voxel volume 3T
50 40 35 30 25 20 15 10 5
7T
0.512 mm3
0.343 mm3
0.216 mm3
0.125 mm3
0.125 mm3
0.091 mm3
0.064 mm3
6.43 7.31 8.14 8.69 9.7 10.64 11.7 12.96 14.21
5.27 5.99 6.66 7.11 7.94 8.71 9.58 10.6 11.63
4.18 4.75 5.29 5.64 6.3 6.91 7.6 8.41 9.23
3.18 3.61 4.02 4.29 4.79 5.26 5.78 6.4 7.02
3.89 5.03 6.56 7.72 8.96 10.21 12.36 14.52 17.29
3.32 4.3 5.6 6.59 7.65 8.72 10.56 12.39 14.76
2.78 3.6 4.7 5.52 6.41 7.31 8.85 10.39 12.37
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Fig. 2. Radii of smallest detectable veins in magnitude T∗2 -weighted images acquired at 3 T and 7 T with the protocol used by Tallantyre et al. [10,12] (20-ms TE, 0.512-mm3 and 0.125-mm3 isotropic voxels at 3 T and 7 T, respectively) as a function of the orientation of the vein with respect to the static magnetic field, B0 .
with respect to the static field, for a range of voxel shapes (varying slice thickness and in-plane resolution, to keep voxel volume constant at 0.125 mm3 as in Figs. 1 and 2) in 7 T images using a 20-ms TE. An increase in slice thickness (and corresponding increase in in-plane resolution) reduces the sensitivity to veins perpendicular to the field, and therefore careful choice of voxel shape allows the bias towards these veins to be minimised. The effect of administration of gadolinium on the sensitivity of this technique to veins and arteries is shown in Fig. 4. The radii
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Fig. 3. Radii of smallest detectable veins as a function of orientation to the static magnetic field, B0 , for a range of voxel shapes (varying slice thickness and in-plane resolution, to keep voxel volume constant at 0.125 mm3 as in Figs. 1 and 2) in 7 T images using a 20-ms TE. The protocol used in Tallantyre et al. [10,12] used 0.5-mm slices (isotropic voxels).
of smallest detectable veins are shown (a weighted average calculated over 10 orientations between 0◦ and 90◦ to B0 , with error bars showing the range over the different orientations) as a function of slice thickness (keeping voxel volume constant at 0.125 mm3 for 7 T and 0.512 mm3 for 3 T) for a range of TEs (10–25 ms at 7 T, 20–40 ms at 3 T, in steps of 5 ms). The radii of smallest detectable arteries for each TE are shown as circles on each graph. These values are shown for 7 T (top) and 3 T (bottom), with no exogenous contrast agent (left) and after a simulated single dose of gadolinium (0.1 mmol/kg)
Fig. 4. Radii of smallest detectable vessels (weighted average taken over 10 orientations between 0◦ and 90◦ to the static field) as a function of slice thickness (for 0.125-mm3 voxels at 7 T and 0.512-mm3 voxels at 3 T) for a range of echo times (10–25 ms at 7 T, 20–40 ms at 3 T). Shown for veins (bars) and arteries (circles) with error bars showing upper and lower limits for veins over the range of orientations, with no exogenous contrast agent (left) and after a simulated single dose of gadolinium (right) at 3 T (top) and 7 T (bottom).
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Fig. 6. Radii of smallest detectable veins in 7 T images using chosen parameters after optimisation (0.32 × 0.32 × 0.90 mm3 voxels; 15-ms TE) as a function of orientation for SWI images made using positive phase masks (see Section 3 for explanation), with the number of multiplications of the phase mask (m) ranging from 0 (magnitude image) to 4.
4.3. Images The high in-plane resolution of the optimised images appeared to be of benefit for the visualisation of both lesions and veins. An example of the images obtained using isotropic and optimised acquisitions at 7 T is shown in Fig. 7. As predicted, the optimised sequence improved the detection of veins perpendicular to the imaging plane, as can be seen in Fig. 7H. Fig. 5. Radii of smallest detectable veins as a function of voxel aspect ratio, at 3 T (A) and 7 T (B), for a range of voxel volumes (0.216–0.512 mm3 at 3 T, and 0.064–0.125 mm3 at 7 T).
(right). The results show a slight increase in sensitivity to veins following administration of gadolinium; however, the corresponding increase in visibility of arteries is much larger, making the sensitivity of the technique to arteries similar to its sensitivity to veins. Thus, gadolinium is helpful to visualise the whole vasculature tree, but the discrimination between veins and arteries is lost. In addition, the visibility of arteries is shown to be more dependent on voxel aspect ratio than that of veins in the same image. At both field strengths, decreasing the voxel volume resulted in an increase in sensitivity, despite the loss of SNR (Fig. 5). At 7 T, a reduction in voxel volume of 27% (to 0.091 mm3 ) yielded higher sensitivity to veins than the original resolution (0.125 mm3 ). A further reduction (to 0.064 mm3 ) resulted in a reduction in sensitivity. At 3 T, the effect on sensitivity of a reduction in voxel volume is dependent on the voxel aspect ratio. For isotropic voxels, a 58% decrease in resolution to 0.216 mm3 allowed detection of veins smaller than those detectable using 0.343-mm3 or 0.512-mm3 voxels, while for highly anisotropic voxels, the lower resolution yielded higher sensitivity. 0.216-mm3 isotropic voxels gave the highest predicted sensitivity; however, this resulted in considerable bias towards detection of vessels perpendicular to the B0 field. When using a voxel aspect ratio of 0.35, which results in less dependence on vessel orientation, a voxel volume of 0.343 mm3 yielded highest sensitivity. The final parameters chosen, based on consideration of all the results above, were voxel sizes of 0.32 × 0.32 × 0.90 mm3 and 0.57 × 0.57 × 1.05 mm3 at 7 T and 3 T, respectively, with echo times of 15 ms and 25 ms. The radii of smallest detectable veins in SWI images created using these chosen parameters are shown in Fig. 6. It is apparent that the use of the phase mask does not increase the visibility of these very small veins at this field strength.
4.4. Vessel detection A total of 214 lesions were detected on the 3 T FLAIR images of 3 patients (average 71 per patient, range 41–114; mean lesion volume 138 mm3 ). The previously used 3 T and 7 T T∗2 -weighted sequences (isotropic voxels, 20-ms TE) allowed detection of 53% and 66% of these lesions, respectively; the optimised sequences demonstrated 71% (3 T) and 72% (7 T), respectively. A vein could be visualised in 56% of visible lesions in the previously used 3 T sequences (average across patients 55%, range 46–68%) and 89% using the equivalent 7 T sequence (mean 87%, range 81–92%). This is similar to results in Tallantyre et al. [10] in which 45% of lesions showed a detectable vessel at 3 T, and 87% at 7 T. This sensitivity was increased by the optimisation: 82% of visible lesions at 3 T contained a detectable vessel using the optimised sequence (mean 80%, range 75–83%) and 95% at 7 T (mean 95%, range 90–98%). The proportion of lesions containing visible vessels on each image is shown for each sequence in Table 3, and represented graphically in Fig. 8. As would be expected, larger lesions were more likely to contain a visible vessel (Fig. 9); however, vessels were detected in at least 70% of detectable lesions with volume smaller than 5 mm3 on both 7 T scans. In 3 T images using the optimised sequence, vessels could be seen in 50% of lesions smaller than 5 mm3 . 5. Discussion We have demonstrated clearly the increase in sensitivity that can be obtained by altering the echo time at both 3 T and 7 T (Fig. 1). Ultra-high-field imaging is of benefit when seeking to visualise small veins: vessels with cross-sectional areas 4 times smaller can be detected at 7 T compared to 3 T, using isotropic voxels. This figure also shows the higher sensitivity to veins perpendicular to
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Fig. 7. Example T∗2 -weighted images of Patient 3 (male, RRMS, age 27 years, EDSS 1.5, disease duration 8 years) acquired using the previously used [10,12] 3 T protocol (A and E; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (B and F; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (C and G; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (D and H; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels).
B0 compared to those parallel to the field. This bias towards detection of veins with particular orientation can be exploited or minimised significantly without increasing scan time, by altering the voxel aspect ratio, as shown in Fig. 3. However, the benefit of this must be weighed against the ability to easily view the image in all three perpendicular planes; furthermore, when altering voxel shape to reduce orientation dependence, the imaging plane must itself be kept perpendicular to B0 . The administration of exogenous contrast agents such as gadolinium increases the phase effect around blood vessels, increasing their visibility; it also increases the dependence on vessel orientation. Fig. 4 shows clearly that administration of gadolinium, while slightly increasing the sensitivity to small veins, would significantly increase the detection of arteries within lesions and may therefore reduce the efficacy of the technique for differentiating between lesions caused by demyelination and those caused by ischaemia. It is recommended for this technique, then,
Fig. 8. Total number of lesions detected (in 3 patients), and proportion of those lesions containing a detectable vessel for images acquired using the previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels). Labels show percentage of detectable lesions containing visible vessel(s).
that the inherent susceptibility around veins in high-field images is exploited without the introduction of exogenous contrast agents. When all vessel orientations are taken into account, the creation of susceptibility-weighted images to increase vessel visibility as discussed by Haacke et al., appears not to be beneficial for the detection of very small veins such as these, with a diameter much smaller than a pixel (Fig. 6). This is because the increase in noise produced by multiplication of the phase mask outweighs the increase in contrast surrounding veins. Furthermore, multiplication by the phase mask increases orientation dependence and is therefore not preferred for this application. Typical images acquired using the optimised sequences are shown in Fig. 7, alongside images acquired with the previously used [10,12] sequences. The benefit of imaging at ultra-high-field is apparent and, as can be seen in Fig. 7H, the optimisation improves
Fig. 9. Percentage of detectable lesions containing a visible vessel as a function of lesion volume as outlined on 3 T FLAIR images, for the previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used [10,12] 7 T protocol (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels).
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Table 3 Numbers of lesions detected using previously used [10,12] 3 T protocol (3 T ISO; 20-ms TE, 0.8 × 0.8 × 0.8 mm3 voxels), optimised 3 T protocol (3 T OPT; 25-ms TE, 0.57 × 0.57 × 1.05 mm3 voxels), previously used 7 T protocol [10,12] (7 T ISO; 20-ms TE, 0.5 × 0.5 × 0.5 mm3 voxels) and optimised 7 T protocol (7 T OPT; 15-ms TE, 0.32 × 0.32 × 0.90 mm3 voxels). Patient 1 – male, SPMS, age = 44 years, EDSS = 6.5, disease duration = 19 years 59 lesions 3 T ISO
3 T OPT
7 T ISO
7 T OPT
Detected Containing vein C S M No vein A N
34 23 7 7 9 11 1 10
41 34 20 4 10 7 2 5
37 30 19 1 10 7 4 3
42 38 24 2 12 4 3 1
Percentage containing vein
68%
83%
81%
90%
3 T OPT
7 T ISO
7 T OPT
Patient 2 – female, SPMS, age = 62 years, EDSS = 6.0, disease duration = 14 years 41 lesions 3 T ISO Detected Containing vein C S M No vein A N
24 11 2 6 3 13 4 9
28 21 6 13 2 7 5 2
26 23 10 7 6 3 2 1
27 26 7 8 11 1 0 1
Percentage containing vein
46%
75%
88%
96%
3 T OPT
7 T ISO
7 T OPT
Patient 3 – male, RRMS, age = 27 years, EDSS = 1.5, disease duration = 8 years 114 lesions 3 T ISO Detected Containing vein C S M No vein A N
55 29 19 7 3 26 8 18
84 70 52 11 7 14 4 10
78 72 59 2 11 6 2 4
85 83 71 7 5 2 2 0
Percentage containing vein
53%
83%
92%
98%
3 T ISO
3 T OPT
7 T ISO
7 T OPT
113 63 28 20 15 50 13 37
153 125 78 28 19 28 11 17
141 125 88 10 27 16 8 8
154 147 102 17 28 7 5 2
Total 214 lesions
Detected Containing vein C S M No vein A N Percentage containing vein
56%
visualisation of veins parallel to the static magnetic field (perpendicular to the imaging plane). The number of lesions visible on T∗2 -weighted images using the original sequences (53% at 3 T and 66% at 7 T) was lower than previously reported [10] (89% and 94% respectively), despite a similar mean voxel volume; it is not clear why this is the case. However, the proportion of lesions containing visible vessels was similar to those previously reported (56% and 89% cf. 45% and 87%). The optimisations discussed here resulted in a much higher sensitivity to vessels; this improvement was most pronounced
82%
89%
95%
at 3 T, where the proportion of lesions containing detectable vessels increased from 56% to 82% – yielding a similar sensitivity to the previously used 7 T sequence which has been shown to allow discrimination between MS lesions and asymptomatic white-matter lesions. The percentage of FLAIR lesions detected on T∗2 -weighted images at 7 T increased from 66% to 72%, and the proportion of perivenous lesions increased from 89% to 95%. The likelihood of detecting a vein within a lesion in 7 T imaging appears to be less dependent on lesion volume than at lower field.
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While identifying lesions and veins in optimised 7 T images, the authors noticed many small lesions which had not been detected on the 3 T FLAIR; furthermore, many large lesions on the FLAIR images appeared on the 7 T T∗2 -weighted image to be a collection of smaller lesions (this has been demonstrated previously using 7 T MP-RAGE images in Mistry et al. [20]). Further work to improve the reliability of the lesion map may therefore improve this technique.
Acknowledgements
6. Conclusions
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The sensitivity of T∗2 -weighted imaging to blood vessels and the dependence of this on vessel orientation are determined by many things, including the volume and shape of the voxels acquired, the TE used, the administration of contrast agent such as gadolinium, and the multiplication of phase information into the image. Here, SNR has been measured in T∗2 -weighted MR images at 3 and 7 T and these measurements have been used to calculate a threshold for the signal change required in order to detect a voxel containing a small vein or artery. The signal change due to a vessel within a voxel in these images has been simulated over a range of TEs, voxel volumes and voxel aspect ratios, for vessels at a range of orientations to the static magnetic field, and the radius of the smallest detectable vessel predicted. Optimal sequence parameters have been predicted to allow detection of small veins within MS lesions, and hence discrimination between lesions containing veins and those containing small arteries, or no vessel. The chosen parameters at 7 T were a TE of 15 ms, and voxel volume of 0.32 × 0.32 × 0.9 mm3 . At 3 T, a voxel volume of 0.57 × 0.57 × 1.05 mm3 was chosen, with a TE of 25 ms. In both cases, the imaging slab is to be oriented perpendicular to the static field. The administration of gadolinium and the creation of SWI images are not thought to be of benefit to this technique. These optimised sequences have been demonstrated and compared to sequences previously used in the investigation of the perivenous nature of MS lesions [10] and the differentiation between MS lesions and asymptomatic WM lesions [12], and show a significant improvement in the number of vessels being detected. 7 T imaging is superior to 3 T for detection of both lesions and vessels; however, the optimisations discussed here yield a sensitivity at 3 T comparable to that of the previously used 7 T sequence, suggesting that this technique could be implemented at 3 T. Further work to determine the efficacy of the optimised technique at both field strengths for differentiation between MS and other WM lesions would be of benefit. Conflict of interest The authors have no conflicts of interest.
This study was funded by research grants from the UK Multiple Sclerosis Society (grant number 919) and Medical Research Council (grant number G0700584), and a charitable donation from Helen Foster and Eleanor Duthie in support of MS research. References