Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus of this paper is to determine the utility of the monotone operator learning (MOL) framework in the parallel MRI setting. The MOL algorithm alternates between a gradient descent step using a monotone convolutional neural network (CNN) and a conjugate gradient algorithm to encourage data consistency. The benefits of this approach include similar guarantees as compressive sensing algorithms including uniqueness, convergence, and stability, while being significantly more memory efficient than unrolled methods. We validate the proposed scheme by comparing it with different unrolled algorithms in the context of accelerated parallel MRI for static and dynamic settings.
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
Brain Tumor Classification on MRI in Light of Molecular Markers
Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Department of Electrical \& Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA University of Maryland, Baltimore County, MD, USA；Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy…
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤科和Winship癌症研究所，埃默里大学）；Department of Radiation and Cellular Oncology, The University of Chicago（放射肿瘤学与细胞肿瘤学部，芝加哥大学）；Department of Electrical and Computer Engineering, Georgia Institute of Technology（电气与计算机工程系，佐治亚理工学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）；Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆凯特琳癌症中心）
Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery. In this work, we introduce a novel monotone operator learning framework to overcome some of the challenges associated with current unrolled frameworks, including high memory cost, lack of guarantees on robustness to perturbations, and low interpretability. Unlike current unrolled architectures that use finite number of iterations, we use the deep equilibrium (DEQ) framework to iterate the algorithm to convergence and to evaluate the gradient of the convolutional neural network …
Computational imaging has been revolutionized by compressed sensing algorithms, which offer guaranteed uniqueness, convergence, and stability properties. Model-based deep learning methods that combine imaging physics with learned regularization priors have emerged as more powerful alternatives for image recovery. The main focus of this paper is to introduce a memory efficient model-based algorithm with similar theoretical guarantees as CS methods. The proposed iterative algorithm alternates between a gradient descent involving the score function and a conjugate gradient algorithm to encourage …
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
Xiamen University（厦门大学）；Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University-Neusoft Medical Magnetic Resonance Imaging Joint Research and Development Center, Xiamen University（福建省等离子体与磁共振重点实验室、厦门大学-东软医疗磁共振成像联合研发中心、厦门大学）；Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University（河南省人民医院暨郑州大学人民医院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；United Imaging Healthcare（联影医疗）；Philips Healthcare（飞利浦医疗）；Zhongshan Hospital, Fudan University (Xiamen Branch)（复旦大学附属中山医院（厦门分院））；Zhongshan Hospital Affiliated to Xiamen University（厦门大学附属中山医院）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；Department of Neurosurgery, Zhongshan Hospital, Fudan University (Xiamen Branch)（复旦大学附属中山医院（厦门分院）神经外科）；Department of Neurology, Zhongshan Hospital, Fudan University（复旦大学附属中山医院神经内科）；Department of Applied Marine Physics and Engineering, Xiamen University（厦门大学应用海洋物理与工程系）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Biomedical Research Institute, Henan Academy of Sciences（河南省科学院生物医学研究所）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-欧洲大都会区纽伦堡综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Universität Würzburg（维尔茨堡大学）；Peking University（北京大学）；Universitätsklinikum Würzburg（维尔茨堡大学医院）
Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions
Department of Mathematics, Lehigh University（数学系，莱维大学）；Department of Mathematics, University of California, San Diego（数学系，加州大学圣地亚哥分校）；Department of Mathematical Sciences, Worcester Polytechnic Institute（数学科学系，沃斯堡技术学院）；Center for Computing Research, Sandia National Laboratories（计算研究中心，沙ia国家实验室）
