Norman Arthur Lapin¹, Xiaodong Wen¹, and Janaka Wansapura^1
1Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States

The challenge of conducting diffusion tensor imaging in the presence of systemic contrast agent has been noted clinically. Here, we investigate primed infusion as a means of stabilizing contrast enhancement in the mouse brain during image acquisition. Our results show that with infusion-sustained contrast enhancement, fractional anisotropy (FA) was significantly elevated in whole brain and corpus callosum ROIs, but not in hippocampus ROI. Contrast enhancement significantly increased tractography metric of relative mean fiber-track length in corpus callosum versus hippocampus. Thus, contrast enhancement may have a preferential effect on anisotropic brain regions and could potentially be applied to amplify FA selectively.

Chris Moore¹, Joe Stickley¹, Abigael Clough², Claire Nelder², Robert Chuter¹, Ananya Choudhury³, Damien McHugh¹, and Michael Dubec^1,4
1CMPE, The Christie NHS Foundation Trust, Manchester, United Kingdom, ²Radiotherapy Services, The Christie NHS Foundation Trust, Manchester, United Kingdom, ³Research and Development, The Christie NHS Foundation Trust, Manchester, United Kingdom, ⁴University of Manchester, Manchester, United Kingdom

MR-Linacs enable daily functional imaging to be performed in patients being treated with radiotherapy. In this work, the diffusion sequence developed by the MRL Consortium biomarkers working group underwent technical validation, showing accurate and repeatable phantom ADC measurements. Daily, whole-prostate ADC mapping was also performed in patients undergoing treatment for prostate cancer on an MRL. No significant differences were observed in whole-prostate ADC during the course of treatment for conventional and stereotactic ablative body radiotherapy (SABR) treatments. Absence of significant change in ADC could be related to the fact that patients were treated with neo-adjuvant hormone therapy prior to radiotherapy.

Viljami Sairanen^1
1BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, University of Helsinki, Helsinki, Finland

Investigation of brain structure of infants or other uncooperative patients using diffusion-weighted MRI is challenging due to subject motion artefacts. This work proposes a novel robust augmentation to current state-of-the-art multi-shell multi-tissue (MSMT) constrained spherical deconvolution (CSD) pipeline that accounts for these artefacts in both response function estimation and in deconvolution. A proof-of-concept is shown using multi-shell infant dataset and it is compared against the normal MSMT-CSD. The results indicate that motion artefacts can result in incorrect tissue type segmentations and fiber orientation distribution (FOD) estimates which could affect negatively any following analysis such as tractography or microstructural brain modelling.

Tengfei Xue^1,2, Fan Zhang¹, Chaoyi Zhang², Yuqian Chen^1,2, Yang Song³, Nikos Makris¹, Yogesh Rathi¹, Weidong Cai², and Lauren Jean O’Donnell^1
1Harvard Medical School, Boston, MA, United States, ²The University of Sydney, Sydney, Australia, ³University of New South Wales, Sydney, Australia

We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), which performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is developed for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. SupWMA obtains highly consistent and accurate SWM parcellation results on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions.

Yuqian Chen^1,2, Chaoyi Zhang², Yang Song³, Tengfei Xue^1,2, Nikos Makris¹, Yogesh Rathi¹, Weidong Cai², Fan Zhang¹, and Lauren Jean O'Donnell^1
1Harvard Medical School, Boston, MA, United States, ²The University of Sydney, Sydney, Australia, ³The University of New South Wales, Sydney, Australia

We propose a novel unsupervised deep learning framework for white matter fiber clustering. Self-supervised learning is adopted to enable joint deep embedding and cluster assignment. Anatomical information is incorporated into the neural network to improve anatomical coherence. In addition, outlier removal is performed to further improve cluster quality. Our method is evaluated on three datasets and showed superior performance in terms of cluster compactness, anatomical coherence and generalization across subjects compared to several state-of-the-art algorithms.

Fan Zhang¹, William M Wells III¹, and Lauren J O'Donnell^1
1Harvard Medical School, Boston, MA, United States

We present a novel deep learning method, DDMReg, for accurate dMRI registration. In dMRI registration, the goal is to align brain anatomical structures while ensuring local fiber orientations consistency with the underlying white matter anatomy. DDMReg is an unsupervised method for deformable dMRI registration, without the need of non-rigidly pre-registered images and the corresponding deformation field as ground truth. We propose a novel registration architecture that leverages not only whole-brain information but also tract-specific fiber orientation information. We perform comparisons with four state-of-the-art methods on several independently acquired datasets. Experimental results show that DDMReg obtains highly improved registration performance.

Jin Gao^1,2, Weiguo Li^2,3, Richard Magin³, and Danilo Erricolo^1
1Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, ²Research Resources Center, University of Illinois at Chicago, Chicago, IL, United States, ³Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States

Multiple components analysis as a convenient tool to model multiexponential relaxations has been well developed in the applications of T₂-weighted MRI. Nevertheless, this analysis is rarely used in diffusion-weighted MRI data and the lack of systematic studies makes the situation worse. Building on our previous studies, to validate the performance of the analysis becomes critical to carry out the ultra-high b-value DWI technique in vivo or translate to the clinical applications. We, therefore, proposed this phantom study to validate the stability and robustness of the multiple components analysis applied in the ultra-high b-value DWI technique.

Fan Zhang¹, Suheyla Cetin-Karayumak¹, Steve Pieper¹, Yogesh Rathi¹, and Lauren J O'Donnell^1
1Harvard Medical School, Boston, MA, United States

We present a large-scale harmonized dMRI study where we have performed successful white matter(WM) tractography parcellation across ~10k subjects from the ABCD study. We first assess the effects of data harmonization on WM parcellation, followed by an evaluation of WM parcellation using the entire harmonized dataset. We show that after harmonization, more anatomical tracts are identified and their FA values are closer to the harmonization reference data. We also demonstrate highly successful WM parcellation, where overall 99.9% of tracts(73 per subject) are identified. The parcellated WM tracts, as well as their diffusion measures, will be made available to the public.

Thomas Jochmann¹, Jens Haueisen¹, Dejan Jakimovski^2,3, Robert Zivadinov^2,3, and Ferdinand Schweser^2,3
1Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, ²Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Sources of MRI phase contrast include magnetic susceptibility, chemical exchange, and tissue microstructure. The novel DEEPOLE QUASAR method disentangles frequency sources and yields separate maps for magnetic susceptibility and non-susceptibility frequency shifts.

In this work, we validated the method in vivo and performed the first quantitative study of non-susceptibility frequency in the human brain. We found substantial non-susceptibility contributions in WM and GM. The quantification of non-susceptibility in the human brain provides new ground for theories on the origins of frequency contrast.

Jorge Campos Pazmiño¹, Véronique Fortier^1,2, and Ives Levesque^1,2,3
1Medical Physics Unit, McGill University, Montreal, QC, Canada, ²Medical Imaging, McGill University Health Centre, Montreal, QC, Canada, ³Research Institute of the McGill University Health Centre, Montreal, QC, Canada

We have designed a novel QSM algorithm that addresses some of the limitations of existing techniques that combine the background removal and dipole inversion steps in a single step. We propose that the solution to the direct inversion problem can be aided by an iterative k-space algorithm and the inclusion of a priori information that represents feature-based and voxel-fidelity-based constraints. The considered approach, when compared with other techniques, resulted in a more accurate depiction of the susceptibility in high susceptibility deep gray matter (dGM) structures without sacrificing performance in regions like the cortex of the brain. 

Reza Rahmanzadeh^1,2, Po-Jui Lu^1,2, Hyeong-Geol Shin ³, Matthias Weigel^1,2, Thanh D. Nguyen⁴, Yi Wang⁴, Francesco La Rosa ^5,6, Meritxell Bach Cuadra ^5,6, Ernst-Wilhelm Radue¹, Jens Kuhle², Ludwig Kappos², Jongho Lee ³, and Cristina Granziera^1,2
1Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ²Neurologic Clinic and Policlinic, Switzerland, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ³Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, ⁴Department of Radiology, Weill Cornell Medical College, New York, NY, United States, ⁵Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁶Radiology Department, Center for Biomedical Imaging (CIBM), Lausanne University and University Hospital, Lausanne, Switzerland

Most multiple sclerosis (MS) white matter lesions (WMLs) appears hyperintense in quantitative susceptibility mapping (QSM). In the present study, we investigated the comparative contribution of myelin loss and iron deposit to QSM hyperintensity using a susceptibility source separation algorithm disentangling QSM- positive and QSM-negative susceptibility sources. Our results show that in most MS WMLs, demyelination is the source of QSM hyperintensity.