Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep learning-based approaches have demonstrated potential in expediting MRI processes, these methods usually rely on known sampling patterns and exhibit limited generalisability to novel patterns. In the paper, we propose a sampling-pattern-agnostic MRI reconstruction method via a diffusion model through adaptive consistency enforcement. Our approach effectively reconstructs high-fidelity images with varied under-sampled acquisitions, generalising across contrasts and acceleration factors regardless of sampling trajectories. We train and validate across all contrasts in the MICCAI 2024 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset for the ``Random sampling CMR reconstruction'' task. Evaluation results indicate that our proposed method significantly outperforms baseline methods.
Vendor-agnostic 4D Phase Contrast MRI: a complete open-source pipeline for velocities, displacement, and strain analysis
University of Basel（巴塞尔大学）；University Hospital of Basel（巴塞尔大学医院）；Science and Research Centre Koper（科珀科学与研究中心）；Alma Mater Europaea University（阿尔玛·马特欧洲大学）；Universitty Children’s Hospital, University of Basel（巴塞尔大学儿童医院）
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University（纳扎尔巴耶夫大学智能系统与人工智能研究所）
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In addition to a conventional multi-modal network, we also present an input agnostic architecture that allows diagnosis with either sMRI or DTI s…
Cornell University（康奈尔大学）；Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved with regularized least-squares. Recently, deep learning has been used to amortize this optimization by training reconstruction networks on a dataset of under-sampled measurements. Here, a crucial design choice is the regularization function(s) and corresponding weight(s). In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic rec…
University College London（伦敦大学学院）；Massachusetts General Hospital and Harvard Medical School（马萨诸塞总医院和哈佛医学院）；Technical University of Denmark（丹麦技术大学）；Massachusetts Institute of Technology（麻省理工学院）
We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities. Classical Bayesian methods address this segmentation problem with unsupervised intensity models, but require significant computational resources. In contrast, learning-based methods can be fast at test time, but are sensitive to the data available at training. Our proposed learning method, SynthSeg, leverages a set of training segmentations (no intensity images require…
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies
Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院Athinoula A.马蒂诺斯生物医学影像中心）；CSAIL, Massachusetts Institute of Technology (MIT)（麻省理工学院计算机科学与人工智能实验室）
Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Department of Imaging Neuroscience, University College London（影像神经科学系，伦敦大学学院）；Institute of Cognitive Neuroscience, University College London（认知神经科学研究所，伦敦大学学院）
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framewor…
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
University Children’s Hospital Zurich（苏黎世大学儿童医院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Medical University of Vienna（维也纳医科大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；King's College London（伦敦国王学院）；University Hospital Erlangen（埃尔朗根大学医院）；Spanish National Research Council (CSIC)（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）
CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
Department of Computer Science and Software Engineering, Concordia University（计算机科学与软件工程系，康科迪亚大学）；Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
Department of Computing and Mathematical Sciences, Caltech（计算与数学科学部，加利福尼亚理工学院）；Chandra Family Department of Electrical and Computer Engineering, UT Austin（查纳德家庭电子与计算机工程系，德克萨斯大学奥斯汀分校）
本文提出一种基于由粗到细扩散模型的交替最小化框架，通过优先重建低频分量来联合校正非刚性运动并重建欠采样 k 空间数据，在 64 倍欠采样下仍表现良好。
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled traini…
This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose add…
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
1 Division of Biomedical Imaging, KTH Royal Institute of Technology, Huddinge, Sweden 2 Athinoula A.\ Martinos Center for Biomedical Imaging, Charlestown, USA 3 Department of Radiology, Massachusetts General Hospital, Boston, USA 4 Department of Radiology, Harvard Medical School, Boston, USA 5 Computer Science \& Artificial Intelligence Laboratory, MIT, Cambridge, USA
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；MS Center Amsterdam（阿姆斯特丹多发性硬化中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Leiden University（莱顿大学）；Delft University of Technology（代尔夫特理工大学）
Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varyin…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Score-based diffusion models provide a powerful way to model images using the gradient of the data distribution. Leveraging the learned score function as a prior, here we introduce a way to sample data from a conditional distribution given the measurements, such that the model can be readily used for solving inverse problems in imaging, especially for accelerated MRI. In short, we train a continuous time-dependent score function with denoising score matching. Then, at the inference stage, we iterate between numerical SDE solver and data consistency projection step to achieve reconstruction. Ou…
A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA（放射科，Athinoula A. Martinos生物医学成像中心，麻省总医院，查尔斯顿，马萨诸塞州，美国）；Department of Radiology, Harvard Medical School, Boston, MA USA（放射科，哈佛医学院，波士顿，马萨诸塞州，美国）；MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, MA USA（麻省理工学院计算机科学与人工智能实验室，剑桥，马萨诸塞州，美国）；Epione team, Inria, Sophia Antipolis, France（Epione团队，Inria，索菲亚安蒂波利斯，法国）；UCL Centre for Medical Image Computing, London, United Kingdom（伦敦大学学院医学图像计算中心，伦敦，英国）；Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland（神经科学与生物医学工程系，阿alto大学，埃斯波，芬兰）；Department of Computer Science, Aalto University, Espoo, Finland（计算机科学系，阿alto大学，埃斯波，芬兰）
Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能辅助影像引导诊断与治疗研究所，慕尼黑技术大学（TUM）及慕尼黑技术大学医院，慕尼黑，德国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与健康领域研究所，慕尼黑技术大学（TUM）及慕尼黑技术大学医院，慕尼黑，德国）；Department of Radiation Oncology, TUM University Hospital, Munich, Germany（放射肿瘤科，慕尼黑技术大学医院，慕尼黑，德国）；Chair for Computer Vision and Artificial Intelligence, Technical University of Munich (TUM), Munich, Germany（计算机视觉与人工智能研究所，慕尼黑技术大学（TUM），慕尼黑，德国）；Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA（斯坦福大学人工智能在医学与影像中心，斯坦福大学，斯坦福，CA，美国）；Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑合作伙伴站点，慕尼黑，德国）；Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany（放射医学研究所（IRM），辐射科学部门（DRS），海德堡慕尼黑研究中心，慕尼黑，德国）
Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco（加利福尼亚大学旧金山分校放射与生物医学影像系智能成像中心）；Department of Bioengineering, University of California, Berkeley（加利福尼亚大学伯克利分校生物工程系）；Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco（加利福尼亚大学旧金山分校生物工程与治疗科学系）；Department of Medical Sciences and Technology, Indian Institute of Technology, Madras（印度理工学院马德拉斯分校医学科学与技术系）；Bay Area Institute of Computation, Altos Labs（Altos Labs湾区计算研究所）
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
School of Information Science and Technology, Fudan University, Shanghai, China（复旦大学信息科学与技术学院）；Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, Australia（墨尔本大学信息科技学院数据科学与人工智能系）；Nuffield Department of Clinical Neurosciences, University of Oxford, London, UK（牛津大学临床神经科学系）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China（电子科技大学机械与电子工程学院）；Department of Radiology, Sichuan Provincial People’s Hospital, Chengdu, China（四川省人民医院放射科）；Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China（东南大学医学院中大医院放射科）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）；Sensetime Research, Shanghai, China（商汤研究）
McGill University（麦吉尔大学）；Centre for Intelligent Machines (CIM)（智能机器中心 (CIM)）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, accurate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline advers…
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）
Diffusion magnetic resonance imaging is a noninvasive imaging technique that can indirectly infer the microstructure of tissues and provide metrics which are subject to normal variability across subjects. Potentially abnormal values or features may yield essential information to support analysis of controls and patients cohorts, but subtle confounds affecting diffusion MRI, such as those due to difference in scanning protocols or hardware, can lead to systematic errors which could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonizati…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Washington University in St. Louis（圣路易斯华盛顿大学）；Yonsei University College of Medicine（延世大学医学院）
In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow - an approach we term Intentional Deep Overfit Learning (IDOL). Implementing the IDOL framework in any task in radiotherapy consists of two training stages: 1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and 2) intentionally overfitting this general model to a small training…
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Southwest Hospital（西南医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods, supervised training is performed using artifact-free reference images and their corresponding undersampled pairs. The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference. While such a training strategy can maintain a …
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院 Athinoula A. Martinos 生物医学影像中心）；Harvard Medical School（哈佛医学院）；Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for div…
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Towards contrast-agnostic soft segmentation of the spinal cord
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec Artificial Intelligence Institute（魁北克人工智能研究所）；Translational Imaging in Neurology (ThINK) Basel（神经学转化成像（ThINK）巴塞尔）；Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel（生物医学工程系，医学学院，巴塞尔大学医院和巴塞尔大学）；Department of Neurology, University Hospital Basel（神经病学系，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel（临床神经免疫学和神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学）；Neuroradiology Department, Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano（神经放射科，瑞士南部神经中心，卢加诺地区医院）；Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine（康复医学系，科罗拉多大学医学院物理治疗项目）；Division of Pain Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine（疼痛医学科，麻醉学、围手术期和疼痛医学部，斯坦福大学医学院）；Functional Neuroimaging Unit, CRIUGM, University of Montreal（功能神经影像单元，CRIUGM，蒙特利尔大学）
