A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and an experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to current state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.

[1]  S. Confort-Gouny,et al.  Diffusion-weighted imaging in normal fetal brain maturation , 2007, European Radiology.

[2]  Joachim Hornegger,et al.  Real‐time optical motion correction for diffusion tensor imaging , 2011, Magnetic resonance in medicine.

[3]  Baba C. Vemuri,et al.  Symmetric Positive 4th Order Tensors & Their Estimation from Diffusion Weighted MRI , 2007, IPMI.

[4]  M. Gamerre,et al.  In vivo MRI of the fetal brain , 2004, Neuroradiology.

[5]  Stephen T. C. Wong,et al.  A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation , 2010, NeuroImage.

[6]  Andrea Righini,et al.  Demonstration of acute ischemic lesions in the fetal brain by diffusion magnetic resonance imaging , 2002, Annals of neurology.

[7]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[8]  Richard B. Darlington,et al.  Web-based method for translating neurodevelopment from laboratory species to humans , 2007, Neuroinformatics.

[9]  Christopher D. Kroenke,et al.  Diffusion MR imaging characteristics of the developing primate brain , 2005, NeuroImage.

[10]  Rayleigh The Problem of the Random Walk , 1905, Nature.

[11]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[12]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[13]  C. Studholme,et al.  Diffusion-Weighted Imaging in Fetuses with Severe Congenital Heart Defects , 2011, American Journal of Neuroradiology.

[14]  Daniel Rueckert,et al.  Diffusion tensor imaging (DTI) of the brain in moving subjects: Application to in‐utero fetal and ex‐utero studies , 2009, Magnetic resonance in medicine.

[15]  Arthur W. Toga,et al.  MRI resolution enhancement using total variation regularization , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[17]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[18]  Susan Blaser,et al.  Abnormal fetal cerebral laminar organization in cobblestone complex as seen on post-mortem MRI and DTI , 2009, Pediatric Radiology.

[19]  Ashok Panigrahy,et al.  Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns. , 2004, Magnetic resonance imaging.

[20]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[21]  P. Basser,et al.  Comprehensive approach for correction of motion and distortion in diffusion‐weighted MRI , 2004, Magnetic resonance in medicine.

[22]  C. Studholme,et al.  Normative Apparent Diffusion Coefficient Values in the Developing Fetal Brain , 2009, American Journal of Neuroradiology.

[23]  Ross T. Whitaker,et al.  Rician Noise Removal in Diffusion Tensor MRI , 2006, MICCAI.

[24]  D. V. van Essen,et al.  Microstructural Changes of the Baboon Cerebral Cortex during Gestational Development Reflected in Magnetic Resonance Imaging Diffusion Anisotropy , 2007, The Journal of Neuroscience.

[25]  Colin Studholme,et al.  Reconstruction of Scattered Data in Fetal Diffusion MRI , 2010, MICCAI.

[26]  Sylviane Confort-Gouny,et al.  MR imaging of acquired fetal brain disorders , 2003, Child's Nervous System.

[27]  C. Studholme Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping. , 2011, Annual review of biomedical engineering.

[28]  Colin Studholme,et al.  Intersection Based Motion Correction of Multislice MRI for 3-D in Utero Fetal Brain Image Formation , 2010, IEEE Transactions on Medical Imaging.

[29]  James R. Moore,et al.  Correction for distortion of echo‐planar images used to calculate the apparent diffusion coefficient , 1996, Magnetic resonance in medicine.

[30]  Jean-Luc Daire,et al.  Microstructural development of human brain assessed in utero by diffusion tensor imaging , 2006, Pediatric Radiology.

[31]  Dong-Hyun Kim,et al.  Diffusion‐weighted imaging of the fetal brain in vivo , 2008, Magnetic resonance in medicine.

[32]  Daniel Rueckert,et al.  MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies , 2007, IEEE Transactions on Medical Imaging.

[33]  Colin Studholme,et al.  On Super-Resolution for Fetal Brain MRI , 2010, MICCAI.

[34]  M. Miller,et al.  Anatomical Characterization of Human Fetal Brain Development with Diffusion Tensor Magnetic Resonance Imaging , 2009, The Journal of Neuroscience.

[35]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

[36]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[37]  Jean-Philippe Ranjeva,et al.  White matter maturation of normal human fetal brain. An in vivo diffusion tensor tractography study , 2011, Brain and behavior.

[38]  Colin Studholme,et al.  SLIMMER: SLIce MRI motion estimation and reconstruction tool for studies of fetal anatomy , 2011, Medical Imaging.

[39]  Colin Studholme,et al.  A unified approach for motion estimation and super resolution reconstruction from structural Magnetic Resonance imaging on moving subjects , 2013 .

[40]  Gregor Kasprian,et al.  In utero tractography of fetal white matter development , 2008, NeuroImage.

[41]  Carl-Fredrik Westin,et al.  Spatial normalization of diffusion tensor MRI using multiple channels , 2003, NeuroImage.