118 RESULTS Figure 5.11: The MD slices in axial view (left), and overlaid with the results of first registration (middle), and the second round of registration (right) This figure should be viewed in color § 5.3 Diffusion MRI 119 Figure 5.12: Comparison of corpus callosum bundles reconstructed by using manual seeding(middle) and our method The left figure shows the label map for manual seeding Fig 5.12 compares the corpus callosum constructed by manually seeded tractography (middle) and by the proposed automatic fiber-to-DTI registration method As shown, the shape of the deformed corpus callosum model well matches the fibers reconstructed by manually seeded tractography while the tractography result is incomplete, e.g., in the yellow rectangular region, due to early termination Further, even with a carefully delineated seeding region, it is still very difficult to avoid outlier fibers in tractography A bundle of outlier fibers are highlighted in the red rectangle for example Since outlier fibers in our fiber model have been removed by experts, the fibers reconstructed by our method is clean 120 RESULTS To evaluate the consistency of this fiber-to-DTI registration among subjects, we warp backward the FA volume of each subject to the fiber model space and examine the averaged volume Fig 5.13 displays the averaged FA of all the subjects after back-warping As shown, the averaged FA images appear rather blurry without using any registration due to the misalignment among subjects The major skeleton of the WM becomes much clearer after affine registration which compensates variations of size, position, and orientation As expected, the averaged images given by nonrigid fiber-toDTI are the sharpest which demonstrated consistent alignment given by our fiber-to-DTI registration We also computed the pixel-wise standard deviation of back-warped FA volumes to quantitatively assess the group alignment Fig 5.14 shows the statistics of such pixel-wise standard deviation within the brain area By using our nonrigid fiber-to-DTI registration, more points ”move” to the left side indicating the reduction of the standard deviation, i.e., the improvement of group alignments The mean standard deviations are 0.25, 0.20, and 0.18 respectively for no registration, affine fiber-to-DTI registration and nonrigid fiber-to-DTI registration The reduction of mean standard deviations demonstrated that our nonrigid fiber-to-DTI registration method improved the alignment of the FA volumes and thus indicating more accurate intersubject correspondence § 5.3 Diffusion MRI 121 Figure 5.13: The average FA images after back-warping From top to bottom shows sagittal, coronal, and axial views From left to right shows the results using no registration, affine registration, and non-rigid registration 122 RESULTS x 10 No Registration Affine Registration Nonrgid Registration Noumber of pixels 3.5 2.5 1.5 0.5 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Pixel−wise standard deviation 0.4 0.45 0.5 Figure 5.14: The histograms of pixel-wise FA standard deviations within the brain area after back-warping all the subjects to the brain fiber model domain Previous clinical studies (Nitkunan et al., 2008) suggested that DTI measures should have strong correlations with cognitive impairment We therefore quantitatively validate our results by assessing the partial correlations (controlled for age, gender, and education) between the averaged FA and widely used cognitive scores: the Montreal Cognitive Assessment (MoCA, Nasreddine et al (2005)); the Mini-Mental State Examination (MMSE , Folstein et al (1975)); and the Color Trails Test 1&2 (CTT1, CTT2, D’Elia et al (1996)) The correlation tests are performed among all the subjects with valid cognitive scores and there are about 45 subjects for each cognitive score Table 5.3 shows correlations for the average FA along fibers, among the whole brain volumes and the average skeletonised FA by TBSS(Smith et al., 2006) ‘Corr.’ represents the correlation in absolute value, and ‘Sig.’ stands for the t-test significance level Compared with the skeletonished FA § 5.3 Diffusion MRI 123 value of TBSS, our along-fiber FA correlates better with the cognitive scores Among the four cognitive measures, TBSS correlates best with CTT-2 at 0.671 while the correlation between our along fiber measure and CTT-2 is 0.760 which surpassed TBSS by 13.3% Besides, both our along fiber measure and TBSS skeletonised FA remarkably outperform the whole brain average The correlations with cognitive scores are quite low for brain stem, which is known to be responsible for basic life functions like heart-beating and breathing Fig 5.15 shows the average measurements over the whole brain and the fibers reconstructed by our method As shown, the measurements generated by our results better separate the healthy subjects and patients 124 RESULTS MOCA MMSE CTT-1 CTT-2 Along All Fibers Corr Sig 0.601 0.000 0.629 0.000 0.628 0.000 0.760 0.000 Corpus Callosum Corr Sig 0.569 0.000 0.608 0.000 0.660 0.000 0.762 0.000 Corona Radiata Corr Sig 0.551 0.000 0.548 0.000 0.397 0.005 0.647 0.000 Arcuate Region Corr Sig 0.540 0.000 0.577 0.000 0.452 0.001 0.658 0.000 Occipito Frontal Corr Sig 0.601 0.000 0.566 0.000 0.532 0.000 0.708 0.000 Superior Cingulum Corr Sig 0.507 0.000 0.563 0.000 0.533 0.000 0.646 0.000 Brain Stem Corr Sig 0.157 0.275 0.065 0.654 0.148 0.315 0.052 0.735 Whole Brain Corr Sig 0.387 0.006 0.396 0.004 0.317 0.028 0.559 0.000 TBSS Corr Sig 0.544 0.000 0.582 0.000 0.610 0.000 0.671 0.000 Table 5.3: Correlations between MRI scores and cognitive scores For all the entries except ‘TBSS’ and ‘whole brain’, the MRI score is the average FA value along the fibers obtained by the proposed method ‘TBSS’ uses the average of skeletonised FA values (Smith et al., 2006) as the MRI score For ‘whole brain’, the MRI score is the average FA for the entire brain region Brain masks are produced by 3D Slicer § 5.3 Diffusion MRI 125 0.8 0.7 0.6 DTI measures 0.5 0.4 0.3 0.2 0.1 Healthy subjects Patients 0 10 15 20 25 30 35 subjects 0.8 0.7 0.6 DTI measures 0.5 0.4 0.3 0.2 0.1 Healthy subjects Patients 0 10 15 20 25 30 35 subjects Figure 5.15: Comparison of MR measurements between healthy subjects and patients The top figure shows the results using mean FA, while the bottom figure shows measurements along our reconstructed fibers 126 RESULTS Chapter Conclusion and Future Work This chapter concludes the thesis and discusses the limitation of the presented work and the direction of future work Section § 6.1 summarizes the research objectives of this thesis and highlights the technical contributions The limitations of the work and the recommendations of future research are presented in Section § 6.2 § 6.1 Conclusion and Discussion Perfusion MRI and diffusion MRI are important tools for early detection of myocardial and cerebral ischemia respectively Due to patient respiration and arrhythmia, nonrigid registration is important for pixel-wise perfusion signal analysis which is likely to greatly improve early myocardial ischemia diagnosis In brain diffusion MRI analysis, reconstructing brain fibers from DTI is challenging due to the large amount of fiber tracts and the presence of WM lesions This thesis introduced a nonrigid registration method for perfusion MRI sequence calibration and a nonrigid fiber-to-DTI registration 127 BIBLIOGRAPHY 137 Calamante, F., Tournier, J.-D., Jackson, G D., Connelly, A., DEC 2010 Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping NeuroImage 53 (4), 1233–1243 Candes, E J., Li, X., Ma, Y., Wright, J., MAY 2011 Robust Principal Component Analysis? 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Comput Comput Assist Interv pp 351–358 List of Publications Journal Chao Li, Ying Sun, and Ping Chai, ”Pseudo Ground Truth Based Nonrigid Registration of Myocardial Perfusion MRI” Medical Image Analysis, Volume 15, Issue 4, August 2011, Pages 449-59 Chao Li, Xiaotian He, Vincent Mok, Winnie Chu, Jing Yuan, Ying Sun, and Xiaogang Wang, “Analysis of Group Diffusion Studies Using Fiber-to-DTI Registration” Submitted to NeuroImage Conference Chao Li, Xiaotian He, Vincent Mok, Winnie Chu, Jing Yuan, Ying Sun, and Xiaogang Wang,“Free-Form Fibers: a Whole Brain Fiber-to-DTI Registration Method” In: Medical Image 149 150 LIST OF PUBLICATIONS Computing and Computer Assisted Intervention workshop on Computational Diffusion MRI (CDMRI 2011) Xiao Jia, Chao Li, Ying Sun, Ashraf A Kassim, Yijen L Wu, T Kevin Hitchens, and Chien Ho, “Segmentation of Cardiac MRI in A Heart Transplant Study Using Rodent Models,” Special Session on Medical Image Processing, APSIPA ASC, Singapore, Dec 2010 Chao Li and Ying Sun, “Active Image: A Shape and Topology Preserving Segmentation Method Using B-spline Free Form Deformations,” In: International Conference on Image Processing (ICIP 2010), Pages 2221 - 2224 Chao Li and Ying Sun, “Nonrigid registration of myocardial perfusion MRI using pseudo ground truth,” In: the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2009), Pages 165-72 Chao Li, Xiao Jia and Ying Sun, “Improved semi-automated segmentation of cardiac CT and MR images,” In: the 6th IEEE International Symposium on Biomedical Imaging (ISBI 2009), Pages 25 - 28 Xiao Jia, Chao Li, Ying Sun, Ashraf A Kassim, Yijen L Wu, 151 T Kevin Hitchens, and Chien Ho, “A data-driven approach to prior extraction for segmentation of left ventricle in cardiac MR images,” In: the 6th IEEE International Symposium on Biomedical Imaging (ISBI 2009), Pages 831 - 834 Chao Li and Ying Sun, “Automatic quantitative analysis of myocardial perfusion MRI,” In: the 13th International Conference on Biomedical Engineering (ICBME 2008), Pages 381-385 ... Pages 22 21 - 22 24 Chao Li and Ying Sun, ? ?Nonrigid registration of myocardial perfusion MRI using pseudo ground truth,” In: the 12th International Conference on Medical Image Computing and Computer... thesis introduced a nonrigid registration method for perfusion MRI sequence calibration and a nonrigid fiber-to-DTI registration 127 128 CONCLUSION AND FUTURE WORK method for full brain fiber reconstruction... are 0 .25 , 0 .20 , and 0.18 respectively for no registration, affine fiber-to-DTI registration and nonrigid fiber-to-DTI registration The reduction of mean standard deviations demonstrated that our nonrigid