Accelerating Magnetic Resonance Imaging (MRI) reduces scan time but often degrades image quality. While Implicit Neural Representations (INRs) show promise for MRI reconstruction, they struggle at high acceleration factors due to weak prior constraints, leading to structural loss and aliasing artefacts. To address this, we propose PrIINeR, an INR-based MRI reconstruction method that integrates prior knowledge from pre-trained deep learning models into the INR framework. By combining population-level knowledge with instance-based optimization and enforcing dual data consistency, PrIINeR aligns both with the acquired k-space data and the prior-informed reconstruction. Evaluated on the NYU fastMRI dataset, our method not only outperforms state-of-the-art INR-based approaches but also improves upon several learning-based state-of-the-art methods, significantly improving structural preservation and fidelity while effectively removing aliasing artefacts.PrIINeR bridges deep learning and INR-based techniques, offering a more reliable solution for high-quality, accelerated MRI reconstruction. The code is publicly available on https://github.com/multimodallearning/PrIINeR.
Harvard Medical School（哈佛医学院）；The University of Sydney（悉尼大学）；The University of New South Wales（新南威尔士大学）
The structure and variability of the brain's connections can be investigated via prediction of non-imaging phenotypes using neural networks. However, known neuroanatomical relationships between input features are generally ignored in network design. We propose TractGraphCNN, a novel, anatomically informed graph CNN framework for machine learning tasks using diffusion MRI tractography. An EdgeConv module aggregates features from anatomically similar white matter connections indicated by graph edges, and an attention module enables interpretation of predictive white matter tracts. Results in a s…
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃森哲-埃尔朗根-纽伦堡大学模式识别实验室）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院放射学研究所）；Institut für Informationsverarbeitung, Leibniz Universität Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Radiology, Harvard Medical School and Massachusetts General Hospital（哈佛医学院放射科和麻省总医院）
Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Univeristat Politècnica de Catalunya, Barcelona, Spain（巴塞罗那理工大学）；Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland（拉索恩大学医院放射科）；University of Lausanne (UNIL), Lausanne, Switzerland（洛桑大学）；CIBM Center for Biomedical Imaging, Lausanne, Switzerland（瑞士拉索恩生物医学成像中心）；BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain（巴塞罗那大学BCNatal胎儿医学研究中心）；Department Woman-Mother-Child, CHUV, Lausanne, Switzerland（瑞士拉索恩大学医院妇女-母亲-儿童部门）
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated gro…
Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias. However, segmentation and scar/fibrosis identification from LGE-CMR is an intensive manual process prone to large inter-observer variability. Here, we present a novel fully-automated anatomically-informed deep learning solution for left ventricle (LV) and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR. The technology invo…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
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
We formulate a physics-informed compressed sensing (PICS) method for the reconstruction of velocity fields from noisy and sparse phase-contrast magnetic resonance signals. The method solves an inverse Navier-Stokes boundary value problem, which permits us to jointly reconstruct and segment the velocity field, and at the same time infer hidden quantities such as the hydrodynamic pressure and the wall shear stress. Using a Bayesian framework, we regularize the problem by introducing a priori information about the unknown parameters in the form of Gaussian random fields. This prior information is…
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography
Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；University of Electronic Science and Technology of China（电子科技大学）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；The University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Boston University School of Medicine（波士顿大学医学院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
Eindhoven University of Technology（埃因霍温理工大学）；King’s College London（伦敦国王学院）；Philips Healthcare（飞利浦医疗）
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perf…
Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
School of Electronics & Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；School of Biomedical Engineering and Imaging Sciences, Kings College London（伦敦国王学院生物医学工程与影像科学学院）
Rethinking Convergence in Deep Learning: The Predictive-Corrective Paradigm for Anatomy-Informed Brain MRI Segmentation
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Key Laboratory of Signal Detection and Processing, Xinjiang University（新疆大学信号检测与处理重点实验室）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Institute for Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；Center for Biomedical Artificial Intelligence (bAIome)（生物医学人工智能中心）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学部门）；Department of Neurology（神经内科部门）
Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks
Electrical and Computer Engineering Department, University of Illinois Chicago（伊利诺伊大学芝加哥分校电子与计算机工程系）；Machine and Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）；Department of Radiology, University of Chicago（芝加哥大学放射学系）
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）
University of Leeds（利兹大学）；INRIA Saclay（法国国家信息与自动化研究所萨克莱分部）；Ecole Polytechnique（巴黎综合理工学院）
In silico tissue models enable evaluating quantitative models of magnetic resonance imaging. This includes validating and sensitivity analysis of imaging biomarkers and tissue microstructure parameters. We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. We extend previous studies accounting for the cardiomyocyte shape variability, water exchange between the cardiomyocytes (intercalated discs), myocardial microstructure disarray, and four sheetlet orientations. In the first stage of the method, cardiomyocytes and sheetlets are generated by consider…
Stroke Locus Net: Occluded Vessel Localization from MRI Modalities
Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar（电气工程系，工程学院，卡塔尔大学，多哈，卡塔尔）；Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar（数学与统计学系，艺术与科学学院，卡塔尔大学，多哈，卡塔尔）
本研究提出端到端深度学习流水线 Stroke Locus Net，结合 nnUNet 分割、动脉图谱与 pGAN 生成，仅用 MRI 扫描实现闭塞血管定位，为更快速的中风诊断提供潜力。
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks…
Clinical Graph-Mediated Distillation for Unpaired MRI-to-CFI Hypertension Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Research Convergence Institute（研究融合院）；Department of AI Systems Engineering（人工智能系统工程系）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Thomas Jefferson High School for Science and Technology（托马斯·杰斐逊科技高中）
Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48% of al brain and Central Nervous System (CNS) cancers. It is estimated that annually over 13,000 deaths occur in the US due to GBM, making it crucial to have early diagnosis systems that can lead to predictable and effective treatment. The most common treatment after GBM diagnosis is chemotherapy, which works by sending rapidly dividing cells to apoptosis. However, this form of treatment is not effective when the MGMT promoter sequence is methylated, and instead leads to severe side effects decreasing patient survivabi…
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achiev…
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clini…
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an unsupervised deep-neural-network architecture for motion compensated quantitative DWI (qDWI) analysis. Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network. We simultaneously estimate the qDWI param…
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. The p…
MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany（德国癌症研究中心（DKFZ）海德堡，医学影像计算部，德国）；Medical Faculty, Heidelberg University, Germany（海德堡大学医学学院，德国）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany（放射肿瘤学系学习组，海德堡大学医院，海德堡，德国）
Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport
School of Information Science and Technology, ShanghaiTech University, Shanghai, China（信息科学与技术学院，上海科技大学）；School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（生物医学工程学院，上海交通大学）
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…
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；School of Biomedical Engineering and the National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERCAMRT), Shanghai Jiao Tong University（上海交通大学生物医学工程学院和先进磁共振诊断与治疗国家工程研究中心）
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, United States Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Germany Department of Imaging Physics, Delft University of Technology, Netherlands TUM School of Computation, Information；Technology, Technische Universität München, Germany Department of Surgery, Taipei Veterans General Hospital, Taiwan
Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive l…
Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）
Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations…
Brightness-Invariant Tracking Estimation in Tagged MRI
Johns Hopkins University（约翰霍普金斯大学）；University of Maryland School of Medicine（马里兰大学医学院）；Uniformed Services University of the Health Sciences（统一服务卫生科学大学）；The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.（亨利·M·杰克逊军事医学促进基金会）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Washington University（华盛顿大学）；University of South Carolina（南卡罗来纳大学）
GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
University of Pennsylvania（宾夕法尼亚大学）；University of Melbourne（墨尔本大学）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）；CSIRO Health and Biosecurity（联邦科学与工业研究组织健康与生物安全部）；CSIRO（联邦科学与工业研究组织）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Washington（华盛顿大学）；Wake Forest School of Medicine（维克森林医学院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Indiana University（印第安纳大学）；University of Southern California（南加利福尼亚大学）
Accelerating MRI with Longitudinally-informed Latent Posterior Sampling
Electrical Engineering , Stanford University , California , USA（电气工程，斯坦福大学，加利福尼亚，美国）；Radiology , Stanford University , California , USA（放射学，斯坦福大学，加利福尼亚，美国）
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, and the School of Computation, Information and Technology, TUM（慕尼黑工业大学(TUM)医疗与医学人工智能教席、TUM大学医院、计算信息与技术学院）；Neuroimaging Technology Research Center, Department of Radiology and Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射与生物医学影像系神经影像技术研究中心）；Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences（中国科学院精密测量科学与技术创新研究院）；Department of Radiology, Stanford University（斯坦福大学放射学系）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心(MCML)）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
University of Oulu（奥卢大学）；Harvard Medical School（哈佛医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the development of various acceleration methods, typically through heuristically undersampling the MRI measurement domain known as k-space. Recently, deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e.g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to…
