Computerised planning of the acquisition of cardiac MR images

Computerized Medical Imaging and Graphics 28 (2004) 411–418 www.elsevier.com/locate/compmedimag

Computerised planning of the acquisition of cardiac MR images Clare E. Jacksona, Matthew D. Robsonb, Jane M. Francisb, J. Alison Noblea,* a

Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Parks Road, Oxford OK1 3PJ UK b University of Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, UK Received 30 December 2003; accepted 29 March 2004

Abstract A method to automatically plan acquisition of magnetic resonance images aligned with the cardiac axes is presented. Localiser images are acquired with a mean short axis orientation calculated from a group of (nZ50) adult patients. These images are segmented using the expectation maximisation algorithm. The borders of the ventricular blood pools are found and used to provide an estimate of the orientation of the cardiac axes. These estimated orientations are compared with corresponding manually aligned orientations. The method has been tested on nZ12 volunteers showing an error of within 128 which is sufficiently accurate for clinical use. q 2004 Elsevier Ltd. All rights reserved. Keywords: Magnetic resonance imaging; Cardiac axes orientation; Image acquisition

1. Introduction Cardiovascular magnetic resonance (CMR) imaging is now regarded as a reference standard for analysis of left ventricular volumes and mass [1]. Correct alignment of the imaging planes with the cardiac planes is very important and a challenge, as previous studies using planes aligned with the axes of the body were shown to be ‘suboptimal’ [2]. Alignment of the imaging planes with the cardiac axes requires specialist knowledge of cardiac anatomy and many radiologists and technicians find it difficult to plan these images in a time-efficient and reproducible manner [1]. To our knowledge, the problem of automated cardiac positioning has presently only been addressed by a single group. Lelieveldt et al. proposed a method to automatically orient short axis (SA) CMR images using fuzzy implicit surface templates [1,3,4]. The Lelieveldt approach used non-gated acquisition which will introduce an inconsistency between the localised position and the practical position of the heart in subsequent scans, further the Lelieveldt approach only localised the SA and not the other important planes of the heart. Their technique was evaluated by * Corresponding author. Tel.: C44-1865-280934; fax: C44-1865280922. E-mail address: [email protected] (J.A. Noble). 0895-6111/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.compmedimag.2004.03.006

comparison with reference standard SA images calculated by manually drawing left ventricle contours on a stack of manually aligned SA images and then fitting a straight line to the centroids of these contours. Lelieveldt used fuzzy implicit surface templates of all the organs in the thorax to locate the cardiac axes. Our investigations of this method found it to be very computationally intensive, which is a serious problem for rapid feedback to the scanner, so we have not followed that route. In order to provide a more useful and accurate determination of the cardiac location we have addressed each of these limitations. Lelieveldt used localiser images that were aligned with the axes of the scanner. However, we present an approach where the localiser images are already approximately aligned with the SA of the heart (in a preanalysis step). Unlike Lelieveldt’s localisers, we use breathhold scans, providing images that give cardiac positions that are the same as for the subsequent diagnostic breath-hold scans. Our method provides estimates of the horizontal and vertical long axes as well as the SA of the heart and is based on less computationally intensive techniques than the ones used by Lelieveldt. The resulting images from our method are compared with corresponding manually oriented images instead of calculated reference orientations, providing a clearer comparison with the clinical approach. We therefore, hypothesise that the improvements we have

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implemented will improve the positioning accuracy of our method and hence provide a clinically useful tool.

2. Methods

from the data stored on the scanner. These patients had a variety of heart conditions representative of a cross-section of cases seen in a CMR unit, including post-operative coronary artery bypass graft patients, patients with hypertrophic cardiomyopathy and patients with poor left ventricular function.

2.1. Study population 2.3. Location of left and right ventricular blood pools Twelve healthy volunteers were imaged (7 male and 5 female) aged 27G4 years (range 22–37 years) and with a body mass index of 23G5 (range 17–35 kg/m2). Heart rates were 73G11 (range 58–90 beats/min) during image acquisition. 2.2. Image acquisition Images were acquired using a 1.5 T Siemens Sonata MR system. A 20 slice acquisition was used with a 280! 340 mm field of view, a 1.8!1.8 mm in-plane resolution and a 7.5 mm slice thickness with a 7.5 mm gap between slices. A ‘trueFISP’ sequence was used with a 608 flip angle. The RF reception was on two elements of the spine array coil and six channels of the anterior phased array coil. ECG gating was used and breath-hold commands issued via the intercom system. A multiple slice imaging approach was used with a predefined mid-ventricular SA slice orientation and position. This pre-defined positioning was calculated from images from a group (nZ50) of adult patients selected at random

Using training datasets (not used in the evaluation) from a single volunteer we constructed a method to determine the location of the blood pools. A Gaussian mixture model was fitted to one of the localiser images using the Expectation Maximisation (EM) algorithm as described in Ye et al. [5]. The mixture model was initialised with five Gaussians but a Gaussian was automatically deleted if the number of pixels it represented was 2 or less. The results of applying the EM algorithm to three example localiser images can be seen in Fig. 1. In each case, the number of Gaussians has been reduced to four but the blood pools are represented by the Gaussian with the highest mean in the middle image and the second highest mean in the other two images. The right hand image illustrates a case where the right ventricular blood pool region has merged with a nearby region with a similar grey level. This is due to the right ventricular free wall being substantially thinner than the left ventricular wall. The lower and upper thresholds for grey levels which belong to each tissue classification were then calculated from the parameters of the Gaussians. The image was

Fig. 1. Segmentation using EM algorithm on three example images.

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Fig. 2. Location of the left (LV) and right (RV) ventricles using mathematical morphological operations.

smoothed by replacing each pixel value with the average value of its eight neighbours. Pixels were classified as belonging to one of the Gaussians depending on their value. The user was then asked to select the Gaussian which represented the blood pools. A set of morphological filtering operations (see Fig. 2) was applied to the blood pool regions using empirically determined parameters derived from a training set of images. These operations are used to identify the left (LV) and right ventricle (RV) regions. The pixels in the remaining images of the localiser sequence were then classified as belonging to one of the Gaussians using the parameters found for this image and the blood pool regions were found. The centroid of the LV region was found automatically and then the regions in the neighbouring images with centroids closest in three dimensions to the LV centroid in this image were classified as LV regions. The remaining blood pool regions were eroded then dilated to separate the RV region from neighbouring blood pools. The region with the nearest centroid to that in the starting image was then classified as the RV region. This was repeated for the next nearest neighbours and, in this way, LV and RV regions were found in all the images in the localiser sequence. Boundaries of the regions found for a localiser sequence can be seen in Fig. 3. All the boundaries were then displayed on top of the relevant localiser images so that the user could then choose to reject images where the regions have not been correctly located.

2.4. Calculation of the axis orientations The normal to the SA was then found by fitting a straight line to the centroids of all the selected LV regions. The centre of the middle SA slice was set to lie on this line and to be in the middle of the points used for the fit. For each image, the point on the RV contour was found which is the farthest away from the SA normal. The horizontal long axis (HLA) was then defined as being at 908 to the SA and passing through the RV in the direction where it is widest. The direction in which the RV is widest was found by calculating the average direction over all the selected images. The vertical long axis (VLA) was then positioned so it is at 908 to the SA and at 658 to the HLA (i.e. the nominal values found in Section 3.1). The centres of the HLA and VLA slices were also defined as being in the same position as the centre of the middle SA slice. This point was set to be the origin of the heart axes.

3. Results 3.1. Defining the nominal acquisition angles of images aligned with the cardiac axes Cardiac scan data from 50 adult patients were analysed. Average orientations and positions of the mid-ventricular SA, the horizontal long axis (HLA) and the vertical long

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Fig. 3. Example left (white) and right (light grey) ventricle contours on a set of 20 localiser images.

axis (VLA) were found. These anatomical slice positions were determined according to generally accepted standards [6] by an expert cardiac technologist with more than five years experience. The average angles between pairs of axes were also found. These angles can be seen in Tables 1, 2 and 3. The angles between the SA and the HLA and the SA and the VLA were z908, as would be expected, and the average angle between the HLA and VLA was z658. In order to adequately cover the heart with the localiser images, it was decided that the stack of 20 localiser images should be acquired with the middle of the stack at L (left)Z 50 mm, P (posterior)ZK50 mm, H (head)ZK10 mm from the centre of the scanner. The angle of these localisers was set to the mean SA orientation. Localisers with these parameters were found to include the left and right ventricles of hearts of all 12 volunteers studied and provided images that showed a reasonable approximate SA. 3.2. Repeatability measurements The repeatability of axis positions found using our method was tested using 10 sets of localiser images taken of

the same volunteer. The volunteer was taken completely out of the scanner and re-positioned between acquisitions. Axis orientations were found using both the standard manual method and our semi-automatic method. Table 1 MeanGstandard deviation of angles between axes Angle (8) SA/HLA HLA/VLA SA/VLA

87.9G2.9 67.5G11.1 83.8G4.6

Table 2 MeanGstandard deviation of image positions Position (mm) SA HLA VLA

Left (x)

Posterior (y)

Head (z)

53.1G19.9 37.8G23.5 29.3G18.8

K52.7G18.9 K38.1G18.5 K30.6G19.9

K10.4G33.0 1.1G31.1 2.0G33.0

Image positions are in relation to the centre of the scanner. If z is the direction pointing towards the patient/volunteer’s head along the scanner bore, x and y are the corresponding right handed coordinates pointing to the left and posterior of the patient, respectively.

C.E. Jackson et al. / Computerized Medical Imaging and Graphics 28 (2004) 411–418 Table 3 Average axis orientations

SA HLA VLA

Siemens orientation

Angle variation (8)

SOC38.4OT23.2 TOC33.6OS-0.9 COS-42.8OT-1.5

9.1 9.6 10.9

The angles of the images are given as Siemens double oblique orientations, for example, SOC38.4OT23.2 means sagittal (S) tilted towards coronal (C) by 38.48 then towards transversal (T) by 23.28.

The standard manual acquisition method is described by Pennell in [6] and Francis in [7] and follows the published imaging standards given in [8]. As an additional check of the semi-automated processing, left and right ventricular contours were drawn manually on the localiser images and the axis orientations were calculated from these contours. This check attempted to determine how much of the difference between the manual and semi-automatic axis orientations was due to errors in the contour detection. Table 4 shows the differences in angle and position between the calculated and manually positioned axes for both semiautomatically located and manually drawn contours. As could be expected, the difference in angle was slightly less for the manually drawn contours. Manually drawing the contours is, however, approximately 100 times slower than the automated technique. Fig. 4 shows the distribution of axis orientations from the three methods. It can be seen that, in general, the automatically aligned orientations are

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grouped close to the orientations calculated from contours drawn on the localiser images. This is especially the case for the SA and indicates that the difference in SA orientation between the standard manual method and our semiautomatic method is not just due to errors in the contour detection. The position of the volunteer in the scanner was slightly different for each set of localiser images so variations in the axis positions between localisers were expected. Table 5 gives the average variation of the axis positions from their average position over all 10 image sets for all three methods of axis alignment. In general, the semi-automatically calculated axis positions varied more than the manually aligned axis positions. The variation of the semi-automatically calculated axis positions when contours were drawn manually was, however, comparable to the variation of the manually aligned axis positions. 3.3. Data from 12 volunteers The method was tested on 12 normal volunteers. Axis orientations were found independently by both using the commonly used manual planning method and then using the automated method for each volunteer. Example images are shown in Fig. 5 for one volunteer. Left and right ventricular contours were then drawn on the localiser images and used to calculate the axes positions. Results are shown in Table 6.

Table 4 MeanGstandard deviation of differences in angle and position between semi-automatically calculated and manually drawn LV and RV contours for 10 sets of localiser images of the same volunteer Axis (8)

Position (mm)

Contours used

SA

HLA

VLA

x/L

y/P

z/H

Total

Semi-automatic Manually drawn

11.9G3.2 12.3G3.4

6.2G1.4 2.8G1.5

9.4G2.8 8.3G2.8

15.9G6.3 18.0G6.4

30.0G21.4 44.2G21.8

61.7G10.1 76.3G10.5

70.5G19.4 90.0G20.8

Fig. 4. Comparison of distributions of axis orientations from 10 sets of localiser images of the same volunteer. Standard manually aligned axis orientations are shown as well as automatically aligned orientations where the contours have been found semi-automatically and where they have been drawn manually on the localiser images. Diagrams are centred on the average axis position for the manually aligned axes with concentric circles representing increasing angular differences from this in increments of 58.

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Table 5 MeanGstandard deviation of difference in angle between the calculated axis positions and their average orientation for 10 localiser images of the same volunteer Average difference in angle from average axis position (8) Manually aligned Automatically aligned Manually drawn contours

SA

HLA

VLA

3.1G1.4

2.0G1.3

4.8G2.2

4.8G4.1

4.3G1.8

4.8G4.0

3.4G2.8

2.3G0.8

2.8G3.0

4. Discussion In this work we presented a semi-automated method for the alignment of MR images with the cardiac axes.

This method was tested using 10 sets of localiser images of the same volunteer and then using localiser images of 12 different volunteers. The results we obtained are comparable to those achieved by Danilouchine and Lelieveldt in [1] and [4], where a deviation between a fully automatic method and a calculated SA orientation based on manually drawn contours of 12.28 [1] and 8.38 [4] were achieved. Danilouchine and Lelieveldt calculated the reference SA orientation by drawing endocardial contours in end-diastolic SA images and then fitting a line to the centres of gravity of these contours using a least squares approach. This is very similar to the method we use here to determine the SA orientation from the semi-automatically found contours. Therefore, we would expect the SA orientations calculated from the manually drawn contours to be equivalent to Danilochine and Lelieveldt’s reference standard orientation. As can be seen in Table 6, the average difference between the SA orientations calculated from manually drawn

Fig. 5. Manually and semi-automatically aligned images for the same volunteer.

Table 6 Average differences in angle and position between calculated and manually oriented axes for semi-automatically calculated and manually drawn LV and RV contours for sets of localiser images for 12 volunteers Axis (8) Contours used Semi-automatic

Axis difference

Manual Average Manually drawn Manual Average Difference manual axis/average axis Difference manually drawn/semiautomatic contours

Position (mm)

SA

HLA

VLA

x/L

y/P

z/H

Total distance

11.5G5.0 18.1G7.7 8.0G4.3 15.8G5.8 14.2G4.5 6.9G5.9

16.9G11.6 21.0G12.0 11.5G10.3 16.7G9.0 17.0G4.6 9.4G13.0

15.1G10.9 19.2G12.6 11.3G10.0 11.4G10.0 11.7G8.7 14.5G10.8

13.5G8.7 39.0G15.1 16.6G5.7 37.8G13.7 37.7G13.3 13.9G9.0

25.1G8.6 33.8G13.9 29.0G5.7 37.8G12.2 33.1G17.3 13.9G9.0

23.9G8.3 35.2G11.6 29.7G4.8 41.8G10.3 27.3G11.9 13.9G9.0

37.2G14.3 62.5G23.4 44.7G9.0 67.9G20.8 57.2G24.5 24.0G15.5

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contours and those calculated from semi-automatically found contours was 6.98 for our method, which is better than the previous methods if we use their evaluation criteria. The choice of ‘gold-standard’ for the SA, HLA and VLA is critical for evaluation of these methods. As we have demonstrated in this work, the HLA is far from perpendicular to the VLA as might be expected from their names. Further, the SA is less than 908 from either the VLA or HLA (see Table 1). Consequently, naive geometric assumptions based on the names of these slices cannot be made. As illustrated in Fig. 5, the acquired images from the manual and semi-automatic methods look visibly similar. The results in Table 6 from the manually drawn contours show that improvements could be made through better detection of the right ventricular blood pool boundaries. Further work will focus on this. At present the clinical potential of this approach has been demonstrated, but a much larger study is required to prove its reliability and repeatability for a range of cardiac diseases. In the context of these additional evaluations we should also determine the resultant left ventricular volumes and ejection fractions when using this localisation approach compared to those measurements when the slices are positioned expertly.

5. Summary Cardiac MR has been demonstrated to be a highly effective clinical tool but its use is limited to a small number of centres at present [9]. In a cardiac MR examination, clinical analysis is performed using images aligned with the cardiac axes. Alignment of the imaging planes with the cardiac axes requires specialist knowledge of cardiac anatomy and takes up a large fraction of the examination time. A method to automatically plan the acquisition of images aligned with the cardiac axes is presented. Initially the average axes orientations and positions (relative to the centre of the scanner) of images acquired from a group (nZ50) of adult patients are calculated. For each patient a stack of 20 localiser images with the mean SA orientation are acquired with the centre of the stack at the mean SA position. The cardiac orientation is determined purely from this stack of images using the following steps. The localiser images are automatically segmented using the EM algorithm. The borders of the left and right ventricle blood pools are then found by analysing the properties of the segmented regions. Data points on these borders are used to provide an estimate of the orientation and position of the required aligned cardiac images. Once the key cardiac orientations have been determined, images

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of these slice planes can then be acquired. To evaluate this automated approach these images can be compared with corresponding images acquired using the standard manual method of slice positioning. The resultant semiautomatic approach requires z2 min on a standard desktop computer. The method has been tested on nZ12 volunteers showing an error of within 128 which is consistent with other published methods and the images are visibly similar to those acquired using the manual slice positioning technique.

Acknowledgements CEJ is supported by MRC grant G9802587.

References [1] Lelieveldt BPF, van der Geest RJ, Lamb HJ, Kayser HWM, Reiber JHC. Automated observer-independent acquisition of cardiac short-axis MR images: a pilot study. Radiology 2001;221(2):537–42. [2] Cranney GB, Lotan CS, Dean L, Baxley W, Bouchard A, Pohost GM. Left ventricular volume measurement using cardiac axis nuclear magnetic resonance imaging. Circulation 1990;82(1):154–63. [3] Lelieveldt BPF, Sonka M, Bolinger L, Scholz TD, Kayser H, van der Geest R, Reiber JHC. Anatomical modeling with fuzzy implicit surface templates. Comput Vis Image Understanding 2000; 80:1–20. [4] Danilouchine MG, Westenberg JJM, Lamb HJ, Reiber JHC, Lelieveldt BPF. Accuracy of fully automatic vs manual planning of cardiac MR acquisitions Lecture notes in computer science, 2879 2003 p. 961–962. [5] Ye X, Noble JA. High resolution LV segmentation of MR images of mouse heart based on a partial-pixel effect and EM-MRF algorithm Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI’02) 2002. [6] Pennell DJ. Ventricular volume and mass by CMR. J Cardiovasc Mag Reson 2002;4(4):507–13. [7] Francis JM How to do a left ventricular function study. http://www. cardiov.ox.ac.uk/ocmr/lvfunction.htm. [8] Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan T, Verani MS. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 2002;105(4):539–42. [9] Budinger T, Berson A, McVeigh E, Pettigrew RI, Pohost GM, Watson J, Wickline SA. Magnetic resonance imaging of the cardiovascular system. J Cardiovas Mag Reson 1999;1:53–8.

Clare E. Jackson received her Bachelors degree in Physics from Imperial College, University of London in 1996, her Masters degree from Aberdeen University in 1997 and her PhD, in the field of Nuclear Medicine, from Birmingham University in 2001. She then worked for one year as an Engineer for Oxford Magnet Technology on problems associated with the shimming of high field MRI magnets. Currently, she is a Research Assistant in the Department of Engineering Science, University of Oxford. Her research interests are MR imaging, image segmentation and processing.

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Matthew D. Robson completed his Bachelors degree in Natural Sciences at Cambridge in 1991 specialising in physics, and went on to complete a PhD working on the analysis of MRI data. He worked as a Research Associate at the Department of Diagnostic Radiology at Yale University until 1998 studying problems associated with rapid MRI in the heart and brain. In 1999 Matthew returned to the UK to work for Surrey Medical Imaging Systems, which was subsequently bought out by Marconi Medical systems. Within this organisation he worked as 3Tesla Product Manager successfully pushing forward this new technology into the clinical marketplace. Dr Robson has been managing the technical aspects of research at the University of Oxford Centre for Clinical Magnetic Resonance Research for the past 3 years, and has been engaged in a broad range of research areas in the human heart and brain.

Jane M. Francis is currently the chief technologist in OCMR (Oxford Centre for Clinical Magnetic Resonance Research) and joined the staff in January 2002. She previously worked at The Royal Brompton Hospital in London and has been involved in both clinical and research cardiovascular magnetic resonance techniques for the past seven years. She is a member of SMRT (Section for Magnetic resonance technologists) of ISMRM (International Society for Magnetic Resonance in Medicine) and was a member of the programme committee in 2001 and won third prize in the clinical focus section in 2000 and joint 2nd prize in the research section in 2003. She is also a founder member of the technologists committee of SCMR (Society for Cardiovascular Magnetic Resonance and is currently president elect and will take over the presidents role in 2004. She has been joint author on a number of peer reviewed papers and has presented oral and poster abstracts nationally and internationally within the field of cardiac MR and regularly takes part in organised cardiac MR study days.

J. Alison Noble received a BA (First Class Honours) and PhD in Engineering Science from the University of Oxford, England, in 1986 and 1989 respectively. She is a Professor of Engineering Science and Director of the Oxford Medical Vision Laboratory. Professor Noble has worked in cardiac image analysis for the past 9 years and published around 125 peer-reviewed articles in computer vision and its application in manufacturing and medicine.