Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast enhanced (DCE) imaging.
Theory and Methods: The problem considered here requires recovering hundred-gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (1) A compressed representation using multi-scale low rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (2) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory resolved reconstruction for pulmonary imaging.
Results: The proposed technique shows transient dynamics that are not seen in gating based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features.
Conclusion: We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.
Machine Learning Based Radiomics for Glial Tumor Classification and Comparison with Volumetric Analysis
University of Michigan（密歇根大学）；Izmir University of Economics（伊兹密尔经济大学）；Ege University School of Medicine（埃格大学医学院）
目的;本研究的目的是通过将机器学习应用于多模态MRI特征与体积分析进行比较，将神经胶质肿瘤无创地分为II级，III级和IV级类别。方法;我们回顾性研究了57例胶质瘤患者的3 T MRI增强前后T1加权、T2加权、FLAIR图像和ADC图。使用ITK-SNAP开源工具的半自动分割将肿瘤分割为增强和非增强部分、肿瘤坏死、囊肿和水肿。测量肿瘤总体积、增强-非增强肿瘤、水肿、坏死体积及其占肿瘤总体积的比例。训练的支持向量机（SVM）分类器和人工神经网络（ANN）进行了标记的数据，旨在回答感兴趣的问题。通过ROC分析计算预测的特异性、敏感性和AUC。使用Kruskall Wallis评估组间连续测量的差异，并对多重比较进行事后Dunn校正。结果;当我们比较两组之间的体积比时，IV级和II-III级胶质瘤之间存在统计学显著差异。IV级胶质瘤水肿和肿瘤坏死体积比高于II级和III级。体积比分析不能成功区分II级和III级肿瘤。然而，SVM和ANN正确地分类每个组，准确率高达98%和96%。结论;将机器学习方法应用于MRI特征可以用于在临床环境中无创且更容易地对脑肿瘤进行分类。
Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory（先进成像与放射治疗信息学（AIRT）实验室）；The Medical Artificial Intelligence and Automation (MAIA) Laboratory（医学人工智能与自动化（MAIA）实验室）；Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤科）；Department of Medical Physics, Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心医学物理系）
Time-resolved aortic 3D shape reconstruction from a limited number of cine 2D MRI slices
Institute for Biomedical Engineering, University Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院生物医学工程研究所）；Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich（苏黎世大学心脏中心苏黎世大学医院心脏病科）；Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich（苏黎世大学苏黎世大学医院诊断与治疗放射科）
GRASP MRI: A Decade of Innovation from Bench to Bedside
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Bernard and Irene Schwartz Center for Biomedical Imaging（伯纳德和艾琳·施瓦茨生物医学成像中心）；Center for Advanced Imaging Innovation and Research (CAI2R)（先进成像创新与研究）
