Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work,along with the simulator developed, pave the way towards more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.
Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to characterize brain tissue microstructure employing tissue-specific biophysical models. A current limitation, however, is that most of the proposed models are based on the assumption of negligible water exchange between the intra- and extracellular compartments, which might not be valid in various brain tissues, including unmyelinated axons, gray matter, and tumors. The purpose of this work is to quantify the effect of membrane permeability on the estimates of two popular models neglecting exchange, and compare their performance …
Accuracy and precision of measurements are important for patient follow up in oncology but, unfortunately, partial volume effects introduce an undesired variability between observers. Super resolution techniques (SR) combine multiple acquisitions of an object into a single image richer in details. Herein, the use of SR for reducing variability is investigated in the specific context of prostate measurements. Prostate is typically imaged by T2-weighted MRI in three perpendicular low resolution images, each of them presenting partial volume effects in the direction of the slice selection gradien…
Birth of the Coil: another Milestone towards a fully reproducible low-field MRI scanner for head-imaging
Istituto Nazionale di Ricerca Metrologica (INRiM)（意大利国家计量研究所）；Institute of Biomedical Imaging, Graz University of Technology（格拉茨工业大学生物医学成像研究所）；Physikalisch-Technische Bundesanstalt (PTB) Braunschweig and Berlin（德国联邦物理技术研究院）；Universidad Comunera（科穆内拉大学）；University of Alberta（阿尔伯塔大学）；Cross Cancer Institute（交叉癌症研究所）
MRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter, and uncover its origins by Monte Carlo simulations in realistic substrates from 3d electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependen…
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beihang University（北京航空航天大学）；United-Imaging Healthcare（联影医疗）；Tsinghua University（清华大学）；Beijing Chaoyang Hospital, Capital Medical University（首都医科大学附属北京朝阳医院）；Zhejiang University of Technology（浙江工业大学）；University of Electronic Science and Technology of China（电子科技大学）
Northeastern University（东北大学）；Erasmus MC（伊拉斯姆斯医学中心）；University of Edinburgh（爱丁堡大学）
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using c…
To synthesize diffusion MR measurements from Monte-Carlo simulation using tissue models with sizes comparable to those of scan voxels. Larger regions enable restricting structures to be modeled in greater detail and improve accuracy and precision in synthesized diffusion-weighted measurements. We employ a localized intersection checking algorithm during substrate construction and dynamical simulation. Although common during dynamics simulation, a dynamically constructed intersection map is also applied here during substrate construction, facilitating construction of much larger substrates than…
Multi-echo Chemical Shift Encoded methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water-fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant, such as for neuromuscular disorders, because of the spatial dependence of th…
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Mark and Mary Stevens Neuroimaging and Informatics Institute（马克和玛丽·史蒂文斯神经成像与信息学研究所）；Keck School of Medicine of USC（南加州大学凯克医学院）；Murdoch Children’s Research Institute（默多克儿童研究所）；Turner Institute for Brain and Mental Health（特纳脑与心理健康研究所）；Monash University（莫纳什大学）；Royal Children’s Hospital（皇家儿童医院）
Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 -- Ex vivo imaging: added value and acquisition
