Wall shear stress (WSS) has been suggested as a potential biomarker in various cardiovascular diseases and it can be estimated from phase-contrast Magnetic Resonance Imaging (PC-MRI) velocity measurements. We present a new parametric sequential method for MRI-based WSS quantification consisting of a geometry identification and a subsequent approximation of the velocity field. The new method was validated on well controlled, high resolution in vitro measurements of turbulent stationary flows and physiological pulsatile flows in phantoms. Basic applicability to in vivo was demonstrated using 2D PC-MRI data of the ascending aorta of three volunteers.
Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Australian E-health Research Centre, CSIRO（澳大利亚电子健康研究中心，联邦科学与工业研究组织）；The University of Queensland（昆士兰大学）；Queensland Children's Hospital（昆士兰儿童医院）；Siemens Healthcare Pty Ltd（西门子医疗有限公司）；Siemens Healthineers AG（西门子医疗股份公司）
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Tianjin University（天津大学）
针对深度学习定量 MRI 的精度与不确定性问题，提出训练和推理均使用 Monte Carlo dropout 并平均多次预测的方法，在小数据集下显著提升 FA、MD 图精度，还能生成辅助诊断的置信图。
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine（贝纳德与伊蕾娜·施瓦茨生物医学成像中心，放射学系，纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（数学科学学院，纽约大学）；Center for Advanced Imaging Innovation and Research (CAI 2 R), Department of Radiology, NYU Grossman School of Medicine（先进成像创新与研究中心（CAI 2 R），放射学系，纽约大学格罗斯曼医学院）
Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
Institute of Machine Learning in Biomedical Imaging , Helmholtz Munich , Germany；School of Computation, Information \& Technology , Technical University of Munich , Germany；School of Medicine \& Health, Institute for Diagnostic；Interventional Neuroradiology , Technical University of Munich , Germany；School of Medicine \& Health, TUM-Neuroimaging Center , Technical University of Munich , Germany；Philips GmbH Market DACH , Germany；School of Medicine \& Health, Clinic of Neurology , Technical University of Munich , Germany；School of Biomedical Engineering \& Imaging Sciences , King’s College London , UK
Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers
AImotion Bavaria, Technische Hochschule Ingolstadt（AImotion Bavaria，英戈尔施塔特应用技术大学）；TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；TUM School of Natural Sciences, Technical University of Munich（慕尼黑工业大学自然科学学院）
In vivo quantification of 3D displacement in sacral soft tissues under compression: Relevance of 2D US-based measurements for pressure ulcer risk assessment
