Diffusion MRI (dMRI) based tractography, although a unique and indispensable in-vivo technique to study the structural connectivity of the brain, is heavily disputed due to unfavorable validation results. This underscores the need for research to advance the tracking techniques and to go beyond the currently available algorithms. In this talk, I will present a new propagation based tracking approach that respects the rules of differential geometry. This improves the commonly used streamline model that is based on piece-wise linear segments. This approach also incorporates a mechanism to take into account the topographic regularity of fiber bundles, a neuroanatomical property of the brain. The main intention of the talk is to discuss what we can learn from this new tool and how to use it to study structural connectivity of brain.

Dogu Baran Aydogan earned his Ph.D. degree in Biomedical Engineering at Tampere University of Technology, Tampere, Finland in 2014. He is currently a postdoctoral scholar at the Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Neuroimaging and Informatics Institute, conducting research on structural connectivity of the brain using diffusion MRI images. His research focuses on the development of novel tractography techniques. He was a winner of the Young Scientist Award in MICCAI 2016. He has also earned multiple awards in international tractography competitions including the ISMRM 2017 Traced and ISBI 2018 Votem challenges.

EAP response function - new theory to characterize and optimize efficiency of diffusion MRI

Diffusion MRI (dMRI) has the unique ability to noninvasively quantify the Brownian motion characteristics of water molecules, and thus infer structural tissue features at microscopic spatial scales that are otherwise inaccessible through conventional millimeter-scale MRI. When applied to the brain, dMRI has emerged as an especially important tool for quantifying tissue microstructural characteristics that change as a result of factors such as brain development, plasticity, and pathology. Diffusion MRI is also used for reconstructing the the white matter pathways that connect different brain regions. However, long scan times continue to be a major challenge for dMRI . While spending an hour or more in the scanner is acceptable for ex-vivo tissue analysis or for motivated, healthy volunteers, it becomes challenging for in-vivo analysis for a number of important subject populations (such as younger children and sick individuals). In practice, scan times are constrained for human subjects, and studies have to make inferences working with the few samples acquired. The overall performance is directly impacted by the efficiency of the dMRI protocol, which consists of the sampling scheme used to acquire dMRI data, as well as the parameter estimation method used to make inferences from measured data. This work addresses the problem of making dMRI experiments as efficient as possible, including: (i) optimizing the scanner sampling protocol to maximize the amount of relevant information contained in the data, (ii) optimizing the parameter estimation protocol to maximize the amount of information extracted from the measured data, and (iii) developing statistical models that can be used to suppress noise contamination.

Divya Varadarajan is an Electrical Engineering Ph.D. student working under the supervision of Prof. Justin P. Haldar at the Biomedical Imaging Laboratory, University of Southern California. Her research focuses on developing signal processing theory and methods for Diffusion MRI. She received an M.S. in Electrical Engineering from the University of Southern California in 2013, and a B.E. in Telecommunication Engineering from the PES Institute of Technology, India in 2007.

Important Features of a Pipeline for Processing Diffusion Weighted Images

We will address aspects related to the quality of Diffusion MRI acquisitions (e.g. low spatial resolution and image distortions that typically accompany single shot echo planar imaging (SS- EPI). We will discuss the impact of EPI artifacts on Diffusion MRI derived metrics and review strategies for correcting residual EPI related distortions via non-linear image registration. A number of excellent strategies for controlling and correcting EPI distortions, eddy current related distortions, and image misregistration caused by subject motion have been proposed in the last few years. We will also examine artifacts affecting accuracy and reproducibility of Diffusion MRI originating from thermal noise, system induced artifacts, and the so called "physiological noise". Physiological noise originates from subject motion, cardiac pulsation, partial volume contamination from cerebral-spinal fluid, and, possibly, respiratory motion and blood flow induced pseudo-diffusion effects. There is clearly a need for improving the resolution, reliability, and overall quality of diffusion tensor MRI acquisitions. The challenge is to achieve this goal by maintaining a reasonably short scan time. Well-designed Diffusion MRI processing pipelines, however, can contribute significantly to improving the accuracy and reproducibility of Diffusion MRI derived metrics.

Dr. Pierpaoli obtained an his M.D. from the University of Milan, Italy in 1989, the European Board Certification in Neurology in 1993, and a PhD. in Neuroscience in 1997. His research has been aimed at understanding the anatomical substrates of normal brain development and neurological disorders elucidating relationships between physiological function and brain structure, architecture and organization. Dr. Pierpaoli and his colleagues performed the first diffusion tensor imaging (DTI) studies of the human brain and received the NIH Award of Merit for this research. He has continued working in the field of diffusion MRI by proposing new metrics to describe the diffusion process, including anisotropy measures and the widely used directionally encoded color maps. Recently he and his group have been focusing on the effects of "physiological noise" in clinical MRI studies, proposing a number of approaches for increasing their accuracy and reproducibility. Diffusion tensor magnetic resonance is a successful example of the journey of neuroimaging from "bench" to "bedside" showing that by developing new imaging methods it is possible to achieve fundamental scientific insights on the structural organization of the brain in health and disease. Dr Pierpaoli has led the DTI activities related to The NIH MRI Study of Normal Brain Development (www.nih-pediatricmri.org) and guided the development of a dedicated software for analysis of MRI data from pediatric subjects (www.tortoisedti.org). He is fellow of the International Society of Magnetic Resonance in Medicine.