MRI and fMRI Optimizations and Applications

MRI and fMRI Optimizations and Applications PA Ciris and R Todd Constable, Yale University School of Medicine, New Haven, CT, USA ã 2015 Elsevier Inc. All rights reserved.

Abbreviations BOLD Blood oxygenation level dependent CBF Cerebral blood flow CBV Cerebral blood volume DSI Diffusion spectrum imaging DTI Diffusion tensor imaging EPI Echo planar imaging fMRI Functional magnetic resonance imaging HARDI High angular resolution diffusion imaging

Introduction Magnetic resonance imaging (MRI) has revolutionized brain imaging since the first MR images were collected showing excellent soft tissue contrast with high spatial resolution. The contrast characteristics obtainable with MRI were unprecedented compared to existing modalities at the time, and basic T1 and T2 contrast imaging approaches have since been extended to depict iron and myelin content, diffusion, perfusion, functional activation, and functional connectivity. Within each of these realms, there are numerous applications to understanding the neurophysiology of the brain and the normal and abnormal distribution of the parameters these methods measure, as a function of age, gender, genotype, or disease. In this article, we review some of the more recent developments in acquisition strategies for different brain imaging applications with emphasis on the information such methods can provide, and some of their strengths and weaknesses. The article is divided into three sections covering highresolution anatomical imaging (morphometry), microstructural imaging (based on diffusion imaging), and functional imaging (chiefly blood oxygenation level-dependent (BOLD) contrast but also considering measures of cerebral blood flow (CBF) and blood volume (CBV)).

MRI Magnetic resonance imaging ODF Orientation distribution function PDF Probability density function RF Radio frequency SNR Signal-to-noise ratio TR Repetition time VASO Vascular space occupancy VERVE Venous refocusing for volume estimation

performance, in addition to providing an accurate brain volume for registration of multiple subjects. Submillimeter anatomical scans are now being obtained at 7 T or higher. At such high field strengths, additional steps are often needed to account for radio frequency (RF) inhomogeneities on both transmit and receive sides of the equation (Adriany et al., 2005; Lutti et al., 2012; Van de Moortele et al., 2005), compensation for susceptibility effects, and the resultant loss of signal or geometric distortion. High-resolution acquisitions also benefit from prospective motion correction (Schulz et al., 2012). T* 2 mapping (Cohen-Adad et al., 2012 at 7 T, Budde, Shajan, Hoffmann, Ugurbil, & Pohmann, 2011 at 9.4 T), susceptibility mapping (Deistung et al., 2013; Schafer et al., 2012), and frequency difference mapping (Wharton & Bowtell, 2012) methods have provided exquisite depictions of neuroanatomy, as well as insights into iron and/or myelin concentrations and orientations of underlying structures. The measurement of myelin content in the cortex has recently garnered great attention, and there is much interest in examining the relationship between functional activity as measured by functional magnetic resonance imaging (fMRI) and myelin content (Barazany & Assaf, 2012; Bock et al., 2013; Lutti et al., 2012).

Advances in Morphometry (Structural Imaging)

Microstructural Imaging (Diffusion Imaging)

Most brain MR imagers now operate at 3 T, and at this field strength, obtaining high-resolution structural volume acquisitions of the brain with outstanding gray-to-white matter contrast, with high signal-to-noise ratios (SNRs) and voxel dimensions around 1 mm3, is considered routine. Parallel imaging advances (Griswold et al., 2002; Pruessmann, Weiger, Scheidegger, & Boesiger, 1999; Sodickson & Manning, 1997) have allowed such acquisitions to be easily completed in 5– 10 min, and advances such as the PROPELLER sequence (Pipe, 1999; Pipe & Zwart, 2006) have allowed such acquisitions to become fairly robust to motion. Obtaining high-resolution volume scans allows for measures of morphometric features that can be associated with functional activity or behavioral

Water molecules inside tissues experience random motion due to thermal energy, the distribution of which is strongly influenced by the local environment, such as cellular membranes, myelin sheaths, and macromolecules (Le Bihan, 1995). Diffusion MRI uses strong bipolar gradients to magnetically label water molecules: diffusion leads to signal attenuation, revealing the underlying displacement and microstructure. The distribution of such microscopic displacements within an MRI voxel can be described by a diffusion probability density function (PDF) (Callaghan, 1991). The diffusion PDF, along an orientation over a given diffusion time, can be recovered from the Fourier transform of signal attenuation at multiple diffusion encoding gradient areas, that is, q-values (Callaghan,

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1991; Cory & Garroway, 1990; Wedeen, Hagmann, Tseng, Reese, & Weisskoff, 2005). Diffusion tensor imaging (DTI) has extensively been used to study white matter anisotropy (Basser, Mattiello, & LeBihan, 1994; Douek, Turner, Pekar, Patronas, & Le Bihan, 1991) and neural fiber architecture (Conturo et al., 1999; Mori, Crain, Chacko, & van Zijl, 1999; Wedeen, 1996). DTI provides gross fiber orientation and quantitative indices such as fractional anisotropy and diffusivity, but is unable to resolve multiple fiber orientations within a voxel (Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, 2000; Lazar et al., 2003; Mori & van Zijl, 2002; Tournier, Calamante, Gadian, & Connelly, 2004), which can lead to major tract reconstruction artifacts (Tuch et al., 2002; Wiegell, Larsson, & Wedeen, 2000). Complex fiber architectures, such as branching and crossing patterns within a voxel, are ubiquitous in the brain (Schmahmann & Pandya, 2006), and their accurate identification requires finer sampling of the diffusion PDF with strong gradients in many directions, that is, high angular resolution diffusion imaging (HARDI). Diffusion spectrum imaging (DSI) (Callaghan, Eccles, & Xia, 1988; Lin, Wedeen, Chen, Yao, & Tseng, 2003; Tuch et al., 2002; Wedeen et al., 2000, 2005) acquires multiple q-values and diffusion orientations on a lattice in q-space, such that a 3D Fourier transform yields the 3-D spin-displacement PDF. The radial projection of the spin-displacement PDF along a certain orientation gives the orientation distribution function (ODF), and the maxima of the ODF identify fiber orientations. Intermediate methods that improve identification of complex fiber architecture include Q-ball imaging (Tuch, Reese, Wiegell, & Wedeen, 2003) and related methods that balance assumptions and compromises between acquisition time and hardware requirements (Jansons & Alexander, 2003; Tournier et al., 2004; Zhan, Stein, & Yang, 2004). Typical whole-brain acquisition durations can be as long as 10 min for Q-ball (i.e., 60 directions) and 45 min for DSI (i.e., 257 directions) (Setsompop et al., 2012). Although reconstruction errors are possible with every method, there is a steady increase in performance from DTI to HARDI methods through DSI (Gigandet, 2009; Wedeen et al., 2008). DSI can accurately show anatomical fiber crossings in the optic chiasm, centrum semiovale, and brain stem; fiber intersections in the gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex (Wedeen et al., 2008). DSI in ex vivo specimens (515 directions) has determined that fiber pathways of the forebrain are organized as a highly curved 3-D grid derived from the principal axes of development (Wedeen et al., 2012). Stronger gradients are desirable for shorter diffusion gradient durations and diffusion encoding times, leading to shorter echo times and higher SNR, as well as higher b-values and angular resolution. Multichannel coils are desirable for improved SNR, shorter acquisitions through parallel imaging, and multiple slice encoding, as well as accompanying improvements in spatial resolution and decreases in echo planar imaging (EPI) susceptibility artifacts. A gradient strength of 300 mT m
1 (200 mT m
1 s
1) has recently been achieved by the Human Connectome Project; substantial gains in the sensitivity and efficiency of HARDI or DSI acquisitions with accompanying improvements in the accuracy and resolution of tractography have been demonstrated using a combination of simultaneous

multislice acquisition and compressive sampling reconstruction with a custom 64-channel brain array at 3 T (Setsompop et al., 2013). Such developments could enable new insights into connectional neuroanatomy and the operation of neural networks, as demonstrated by the ability to probe the distribution of axonal diameters in vivo, as a noninvasive index of action potential conduction velocity (McNab et al., 2013).

Functional Imaging (fMRI) Since the earliest work of Ogawa et al. demonstrating the BOLD phenomena (Ogawa, Lee, Kay, & Tank, 1990), gradient-echo imaging has been the standard imaging approach for the vast majority of functional imaging studies. Numerous works have shown that spin-echo imaging can offer more specificity in terms of localizing activation to the capillary bed while avoiding oxygenation changes in larger draining veins (Constable, Kennan, Puce, McCarthy, & Functional, 1994), but the sensitivity given up for this slight gain in spatial specificity is typically too great for most studies, unless imaging at very high field strengths (Budde, Shajan, Zaitsev, Scheffler, & Functional, 2013; De Martino et al., 2012; Norris, 2012; Olman et al., 2012). Introducing diffusion weighting in fMRI can also improve localization of neuronal activity by removing undesired vascular influences or introduce perfusion contrast based on intravoxel incoherent motion (Song, 2012; Song, Woldorff, Gangstead, Mangun, & McCarthy, 2002; Song, Wong, Tan, & Hyde, 1996). Spatial specificity in group studies is necessarily limited by the correspondences across subjects in terms of both anatomical features and functional features within those anatomical structures. The typical BOLD imaging sequence is a gradient-echo EPI sequence, used in the single shot mode, where an entire data matrix for a slice of 64  64 points is acquired following a single excitation pulse. EPI is used because whole-brain coverage can easily be obtained with repetition times (TRs) of 3 s or less. With standard EPI acquisitions, there is a trade-off between the number of slices required and the minimum TR available, and in the past, very short TRs could only be used if a limited number of slices were obtained (Constable & Spencer, 2001, among others). Recently, however, using the concepts of parallel imaging whereby the receiver coils allow one to separate otherwise overlapping data, simultaneous multislice imaging (multiband and multiplexed imaging) approaches have been introduced that allow anywhere from three to six times, relative to standard EPI, the maximum number of slices to be obtained in a fixed TR (Feinberg, Reese, & Wedeen, 2002; Feinberg & Setsompop, 2013; Feinberg et al., 2010; Larkman et al., 2001). This allows much thinner slices to be acquired while maintaining whole-brain coverage with short TRs, now of 1 s or less. TRs as short as around 600 ms have been shown to greatly benefit fMRI statistical power (Constable & Spencer, 2001). The slight loss of thermal SNR with this reduced TR is more than compensated for by the reduction in physiological noise achieved by obtaining many more samples within a functional run or resting-state period. Potentially, even faster sequences are on the horizon with the advent of nonlinear magnetic field gradients for spatial encoding (Hennig et al., 2008; Stockmann,

INTRODUCTION TO ACQUISITION METHODS | MRI and fMRI Optimizations and Applications

Ciris, Galiana, Tam, & Constable, 2010; Stockmann et al., 2012; Tam, Stockmann, Galiana, & Constable, 2012).

Other Measures of Brain Activity Local neuronal electric currents produce transient weak local magnetic fields. These magnetic field perturbations cause signal attenuation in MRI, providing a more direct measurement of brain activity compared to BOLD. However, despite promising initial results in phantoms (Bodurka & Bandettini, 2002) and in vitro (Petridou et al., 2006), sufficient sensitivity to measure these changes has not yet been demonstrated in vivo (Chu et al., 2004; Huang, 2013; Luo, Jiang, & Gao, 2011; Mandelkow et al., 2007; Parkes, de Lange, Fries, Toni, & Norris, 2007; Tang, Avison, Gatenby, & Gore, 2008). Functional imaging generally relies on a mismatch between local changes in tissue oxygen supply and demand, and different aspects of this relationship can be measured with MRI. Both CBV and CBF changes with activation and MR pulse sequences have been developed to measure these physiological parameters. Intravascular injection of contrast agents can be used to measure both CBF and CBV. CBF can be determined from the delivery of a bolus of contrast to vasculature; however, similar to all bolus tracking methods, this approach suffers from the difficulty of accurate characterization of the arterial input function. CBV can be determined by scaling the difference in signal intensities before and after contrast injection, or from the time integral of the signal time course during the first pass of contrast. Some limitations of these approaches are the short blood half-life of gadolinium-DTPA and the lack of approved iron-oxide agents with longer half-lives for human studies. Furthermore, injections are typically not suitable for functional studies with complex stimulation paradigms. Some noninvasive measurement approaches are reviewed in the succeeding text.

Cerebral Blood Flow While BOLD signal changes are considered large if they are on the order of 1–2%, the CBF changes associated with brain activation can be as large as 40% (Feng et al., 2004; Li et al., 2000; Mark & Pike, 2012), making this a potentially attractive mechanism to target for activation mapping. However, all CBF measurement sequences require the subtraction of two slightly different acquisitions (an acquisition with spin labeling and a second acquisition without labeling), followed by the typical subtraction of activation and control conditions (Aguirre, Detre, Zarahn, & Alsop, 2002; Wong, Buxton, & Frank, 1997). This double subtraction leads to very poor SNR in the maps in addition to requiring doubling the effective TR in order to obtain pairs of images to subtract. For these reasons, CBF mapping is not routinely used, although it can be very important for examining changes in baseline brain activity levels as a function of drug or disease. CBF is also an essential element of calibrated BOLD experiments relating changes in BOLD, CBF, and CBV to changes in the cerebral metabolic rate of oxygen consumption (Buxton, 2012; Buxton, Wong, & Frank, 1998; Buxton, Frank, et al., 1998; Kim & Ogawa, 2012). Approaches for measuring CBF include both pulsed arterial spin labeling and continuous arterial spin labeling. While both

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approaches use endogenous water as a freely diffusible tracer, the latter somewhat more efficient, it requires a separate coil for the labeling and is generally more difficult to implement. In either of these sequences, if one uses a relatively long TE, then both CBF and BOLD contrasts can be obtained simultaneously, and some researchers have taken advantage of such an approach. FAIR (Kwong, Chesler, Weisskoff, & Rosen, 1994), EPISTAR (Edelman et al., 1994), and PICORE (Wong et al., 1997) variants of arterial spin labeling (ASL) refer to differences in the geometry of labeling, control, and imaging regions, which give very similar perfusion results despite differences in static tissue contrast (Wong et al., 1997), while QUIPSII (quantitative imaging of perfusion using a single subtraction – version II; Wong et al., 1997) and Q2TIPS (QUIPSS II with thin-slice TI1 periodic saturation; Luh, Wong, Bandettini, & Hyde, 1999) refer to the management of bolus transit durations between labeling and imaging regions.

Cerebral Blood Volume Upon brain activation, CBV increases from a baseline value of 5–7% of voxel volume by approximately 20%, leading to changes in voxel volume comparable to changes in BOLD signal (1–2%). Although these CBV changes appear to localize well with changes in neural activity (Kim et al., 2012), CBV measurement is typically invasive or difficult especially in humans and rarely performed. Noninvasive MRI approaches for CBV quantification in humans include the following: Vascular space occupancy (VASO) can determine relative changes in total CBV by assuming a certain baseline CBV level (Lu, Golay, Pekar, & Van Zijl, 2003; Scouten & Constable, 2007, 2008); inflow VASO (iVASO) and iVASO with dynamic subtraction can determine the absolute arteriolar contribution to CBV by acquiring data at multiple TI times that are approximately matched to arterial transit times and fitting to a biophysical model (Donahue et al., 2010; Hua, Qin, Pekar, & van Zijl, 2011); venous refocusing for volume estimation (VERVE) can determine relative predominantly venous contributions to CBV based on the T2 dependence of partially deoxygenated blood on refocusing rate and oxygen saturation (Chen & Pike, 2009; Stefanovic & Pike, 2005); and finally, absolute total CBV can be determined from VASO acquisitions at multiple TI values at rest and during activation and fitting to a biophysical model (Ciris, Qiu, & Constable, 2013; Glielmi, Schuchard, & Hu, 2009; Gu, Lu, Ye, Stein, & Yang, 2006).

CBV–CBF Relationship and Compartmental Effects CBV and oxygenation changes in arterial, capillary, and venous compartments impact BOLD differently. Venous CBV has the largest impact on BOLD due to large oxygenation changes on the venous side (Griffeth & Buxton, 2011). Arterial CBV is dominant during respiratory manipulations (Ito, Ibaraki, Kanno, Fukuda, & Miura, 2005) and responds rapidly during functional activation (Drew, Shih, & Kleinfeld, 2011; Hillman et al., 2007; Kim, Hendrich, Masamoto, & Kim, 2007; Vazquez, Fukuda, Tasker, Masamoto, & Kim, 2010). Venous CBV increases slowly with extended stimulus durations (Drew et al., 2011), reaching a magnitude similar to the arterial CBV change (Kim & Kim, 2011). At steady state, the relative change

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in venous CBV is about half that of total CBV (Lee, Duong, Yang, Iadecola, & Kim, 2001). After stimulus offset, arterial CBV rapidly decreases to baseline or exhibits a small prolonged poststimulus undershoot (also observed in CBF (Jin & Kim, 2008) and in surface arteriolar vessel diameters (Drew et al., 2011)), while venous CBV recovers slowly without undershoot. This complicated mechanism may further involve changes in arterial/arteriolar deoxyhemoglobin (Hillman et al., 2007), oxygen saturation, and CBV, potentially significant enough to impact the BOLD signal change (Vazquez et al., 2010). Significant capillary CBV changes have also been suggested (Hillman et al., 2007; Krieger, Streicher, Trampel, & Turner, 2012; Stefanovic et al., 2008; Tian et al., 2010), given that capillaries are the major source of oxygen extraction and closer to the activation site (Krieger et al., 2012). Many fMRI calibration studies assume that CBV ¼ 0.8CBF0.38 based on measurements of total CBV in macaques during respiratory manipulation using PET (Grubb, Raichle, Eichling, & TerPogossian, 1974), with the underlying assumption that the BOLD-induced fractional change in venous CBV is the same as the fractional change in total CBV (Buxton, 2012; Buxton, Frank, et al., 1998; Buxton, Wong, & Frank, 1998; Kim & Ogawa, 2012). The exponent relates to the degree to which rising perfusion is accommodated through blood flow velocity versus volume increases (diameter change or recruitment), which may differ across functional challenges, brain regions, and species (Ito, Takahashi, Hatazawa, Kim, & Kanno, 2001; Jones, Berwick, & Mayhew, 2002; Kida, Rothman, & Hyder, 2007; Mandeville et al., 1999; Wu, Luo, Li, Zhao, & Li, 2002), with gender (Ciris, Qiu, & Constable, 2012) or age. Smaller exponents (0.18–0.23) have been suggested based on measurements of the relative venous contribution to CBV using VERVE in humans (Chen & Pike, 2009). The time- and compartmentdependent behavior may further depend on other parameters such as stimulus strength and spatial extent (Kim & Kim, 2010) and should be considered in the interpretation of BOLD results.

Additional Considerations Sinuses and other air-tissue interfaces cause large susceptibility gradients especially near orbitofrontal and inferior temporal regions of the brain. EPI acquisitions are associated with extended readout durations (along the phase encoding direction), exacerbating erroneous phase accumulation in these regions, leading to severe image artifacts such as geometric distortions, signal intensity variations, and complete signal loss. These artifacts can be partially corrected or compensated for using additional acquisitions, that is, field mapping (Aksit, Derbyshire, & Prince, 2007; Jezzard & Balaban, 1995), point spread function mapping (Dragonu et al., 2013; Robson, Gore, & Constable, 1997; Zaitsev, Hennig, & Speck, 2004; Zeng & Constable, 2002), and z-shimming (Constable, 1995; Constable & Spencer, 1999) methods (in-plane or through-plane). Shortening the readout duration (along the phase encoding direction), by using parallel imaging methods or multishot acquisitions, is very effective for reducing artifact severity. Similarly high-bandwidth acquisitions reduce the length of the readout window and hence geometric distortion, but at the cost of decreased SNR. Spiral-in/spiral-out (Glover & Law,

2001) or more recent asymmetrical spin-echo spiral (Brewer, Rioux, D’Arcy, Bowen, & Beyea, 2009) acquisitions have also been shown to contain artifacts and recover BOLD activations in severely affected regions. Asymmetrical spin-echo acquisitions are hybrid spin-echo/gradient-echo acquisitions, where the tuning between these can range anywhere from pure spinecho to pure gradient-echo or a combination in between. The spin-echo sequences retain sensitivity to microscopic BOLD effects but refocus static field inhomogeneities including signals from larger veins. A combination of gradient and asymmetrical echoes attempts to retain the high sensitivity of gradient-echoes to the BOLD effect while reducing the static field effects (Stables, Kennan, & Gore, 1998; Weisskoff, Zuo, Boxerman, & Rosen, 1994), and such sequences have even been combined with z-shimming (Heberlein & Hu, 2004). Although EPI slices are acquired in milliseconds and wholebrain volumes are acquired in seconds, fMRI acquisitions typically extend to several minutes for sufficient functional contrast and statistical power, and subject motion remains an important consideration. Postprocessing is typically used to register images at slightly different head positions, and some motion-compensated EPI sequences have been developed, such as PROPELLER-EPI (Kramer, Jochimsen, & Reichenbach, 2012). In addition to simple displacements, susceptibility effects from air-tissue interfaces and resulting distortions and artifacts are different at each head position, such that even prospective motion correction may not completely eliminate effects of motion. Motion of air-tissue interfaces as far as the lungs can cause significant susceptibility effects in the brain, and real-time correction of respiratory motion-induced susceptibility effects may be necessary, especially at high fields (van Gelderen, de Zwart, Starewicz, Hinks, & Duyn, 2007). Realtime monitoring of motion and updating of the scan prescription is slowly becoming a reality and may become widely available in the near future (Maclaren, Herbst, Speck, & Zaitsev, 2013; Maclaren et al., 2012). We have not discussed acquisition strategies from the point of view of a paradigm, but there are numerous subtleties that should be considered in designing experiments that can accurately probe the cognitive function of interest (Savoy, 2005). Block paradigms are designed to establish a ‘steady state’ of neuronal and hemodynamic change, and since transitions between activation and control conditions are minimal, these sequences are more efficient and effective at detecting very small changes in brain activity. Event-related designs typically require assumptions of linearity across multiple stimuli, more complicated postprocessing, and higher temporal resolution (Josephs, Turner, & Friston, 1997), however, can enable paradigms precluded by block designs. Advantages of event-related designs include the ability to perform priming experiments and the ability to examine the neurophysiological response to individual stimuli. Spatial and temporal patterns in the spontaneous activity of the brain can also be investigated without the use of paradigms (functional connectivity, resting-state fMRI; Biswal, Yetkin, Haughton, & Hyde, 1995; Constable et al., 2013). Such data can be used to examine functional networks within the brain and potentially even lead to atlases of minimal functional subunits (Shen, Papademetris, & Constable, 2010; Shen, Tokoglu, Papademetris, & Constable, 2013). The extensive flexibility that fMRI provides, through trade-offs

INTRODUCTION TO ACQUISITION METHODS | MRI and fMRI Optimizations and Applications

between spatial resolution, temporal resolution, coverage, SNR, and acquisition duration, can be fully exploited to match requirements of each experimental design.

Summary In summary, many advances continue to be made in the development of high spatial and temporal resolution MRI acquisition strategies. In anatomical scanning, the move to higher field strength has provided unprecedented opportunities for improving spatial resolution while producing images with very high SNR. Advances in diffusion imaging have led to extensive improvements in fiber tracking, and the integration of structural information with functional information is really just beginning. In functional MRI, advances continue to be made in the development of sequences that are maximally sensitive to neuronal activity and minimally sensitive to motion and other sources of artifacts. The field continues to expand rapidly and new applications of MRI are constantly invented.

See also: INTRODUCTION TO ACQUISITION METHODS: Anatomical MRI for Human Brain Morphometry; Diffusion MRI; EchoPlanar Imaging; fMRI at High Magnetic Field: Spatial Resolution Limits and Applications; High-Speed, High-Resolution Acquisitions; Obtaining Quantitative Information from fMRI; Perfusion Imaging with Arterial Spin Labeling MRI; Pulse Sequence Dependence of the fMRI Signal; Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping; Temporal Resolution and Spatial Resolution of fMRI.

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