The variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability across multicenter studies. We present a novel image-based harmonization framework for 3D T1-weighted brain MRI, which disentangles anatomical content from scanner- and site-specific variations. The model incorporates a differentiable loss based on the Structural Similarity Index (SSIM) to preserve biologically meaningful features while reducing inter-site variability. This loss enables separate evaluation of image luminance, contrast, and structural components. Training and validation were performed on multiple publicly available datasets spanning diverse scanners and sites, with testing on both healthy and clinical populations. Harmonization using multiple style targets, including style-agnostic references, produced consistent and high-quality outputs. Visual comparisons, voxel intensity distributions, and SSIM-based metrics demonstrated that harmonized images achieved strong alignment across acquisition settings while maintaining anatomical fidelity. Following harmonization, structural SSIM reached 0.97, luminance SSIM ranged from 0.98 to 0.99, and Wasserstein distances between mean voxel intensity distributions decreased substantially. Downstream tasks showed substantial improvements: mean absolute error for brain age prediction decreased from 5.36 to 3.30 years, and Alzheimer's disease classification AUC increased from 0.78 to 0.85. Overall, our framework enhances cross-site image consistency, preserves anatomical fidelity, and improves downstream model performance, providing a robust and generalizable solution for large-scale multicenter neuroimaging studies.
Computer Science and Artificial Intelligence Lab（计算机科学与人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）
We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable. If a predictive machine is made invariant to a set of domains, the accuracy of the output predictions (as measured by mutual information) is limited by the domain with the least amount of information to begin with. If a real label value is highly informative about the source domain, it cannot be accurately predicted by an invariant predictor. These results are simple and intuitive, but we believe that it is beneficial to state them for medical imaging harmonization.
Vanderbilt University（范德堡大学）；Middle Tennessee State University（中田纳西州立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven tech-nique using a neural network design which exploits two categories of data. First, training data were acquired on three squ…
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…
HARP: HARmonizing in-vivo diffusion MRI using Phantom-only training
Department of Psychiatry（精神医学系）；Brigham and Women's Hospital（布里奇沃特医院）；Harvard Medical School（哈佛医学院）；College of Engineering（工程学院）；Northeastern University（东北大学）；National Institute of Standards and Technology（国家标准技术研究院）；University of Utah School of Medicine（犹他大学医学院）；George E. Wahlen Veterans Affairs Medical Center（乔治·E·瓦伦的退伍军人事务医疗中心）；University of Pittsburgh（匹兹堡大学）；Department of Radiology（放射医学系）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术）
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）
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill（放射科与生物医学研究成像中心（BRIC）、北卡罗来纳大学教堂山分校）；Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University（Lampe联合生物医学工程部门、北卡罗来纳大学教堂山分校和北卡罗来纳州立大学）
Clinical-ComBAT: a diffusion-weighted MRI harmonization method for clinical applications
Videos & Images Theory and Analytics Lab (VITAL)（视频与图像理论与分析实验室）；Department of Computer Science（计算机科学系）；Université de Sherbrooke（Sherbrooke大学）；Sherbrooke Connectivity Imaging Lab (SCIL)（Sherbrooke连接成像实验室）；Imeka Solutions inc（Imeka Solutions公司）
New York University Tandon School of Engineering（纽约大学坦登工程学院）
Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, resolution, and noise. To this end, in the absence of paired data, variations of Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. Importantly, these methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which l…
MOX, Department of Mathematics, Politecnico di Milano（MOX数学系，米兰理工大学）；Department of Mathematics, Università di Genova（基因瓦大学数学系）；Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal-Child Sciences, Università di Genova（基因瓦大学神经科学、康复、眼科、遗传学和妇产科学系）；Department of Biomedical and Neuromotor Sciences, University of Bologna（博洛尼亚大学生物医学与神经运动科学系）；IRCCS Institute of Neurological Science of Bologna, Bellaria Hospital（博洛尼亚IRCCS神经科学研究所，贝拉里亚医院）；Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli（神经信息学实验室，IRCCS福蒂贝拉蒂利中心圣约翰迪奥研究所）；Department of Statistical Sciences, Università Cattolica del Sacro Cuore（圣心大学统计学系）；Health Data Science Centre, Human Technopole（人类技术园区健康数据科学中心）
University of Kentucky（肯塔基大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to harmonize CT image data captured using different scanners is vital in cross-center large-scale radiomics studies but remains the boundary to explore. Furthermore, the lack of paired training image problem makes it computationally challenging to adopt existing deep learning models. %developed for CT image standardization. %this problem more challenging. We propose a novel deep learning approach called CVH-CT for harmonizing CT imag…
A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization
Institute of Informatics, School of Management, HES-SO Valais-Wallis（信息学院，管理学院，瓦莱-德瓦尔省高等教育学院）；Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital（拉沃斯大学医院核医学与分子成像部门）；Clinic of Radiology and Nuclear Medicine, University Hospital Basel（巴塞尔大学医院放射科与核医学诊所）；Idiap Research Institute（IDIAP研究机构）；Department of Radiology, Lausanne University Hospital（拉沃斯大学医院放射科）；Groupe 3R, Lausanne-Épalinges Imaging Center（拉沃斯-埃平日斯影像中心，Groupe 3R）；Faculty of Medicine, University of Geneva (UNIGE)（日内瓦大学医学院）；Inselspital Bern, University of Bern（伯尔尼大学医院，Inselspital Bern）；Cantonal Hospital Baselland, Bruderholz（巴塞尔兰州医院，布雷尔霍兹）；Schmerzklinik Basel, Basel（巴塞尔疼痛诊所，巴塞尔）；Diagnostic and Interventional Radiology, University Hospital Zurich（苏黎世大学医院诊断与介入放射学部门）；Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）；Computer Vision Lab, ETH Zuric（苏黎世联邦理工学院计算机视觉实验室）
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain parameters might not generalize well to images acquired with different parameters. Being able to recover such parameters directly from an image could help determine whether a deep learning model is applicable, and could assist with data harmonization and/or domain adaptation. Here, we introduce a neural network model that can predict many complex IAPs used to generate an MR image with high accuracy solely using the im…
University of Basel（巴塞尔大学）；University Hospital Basel（巴塞尔大学医院）
Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longitudinal comparability is essential but can be impaired by these contrast differences, leading to biased results when using automated evaluation tools. This study presents a diffusion model-based approach for contrast harmonization. We use a data set consisting of scans of 18 Multiple Sclerosis patients and 22 healthy controls. Each subject was scanned in two MR scanners of different magnetic field strengths (1.5 T an…
Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm U1216（法国国家健康与医学研究院 U1216）；Grenoble Institut for Neurosciences（格勒诺布尔神经科学研究所）
ImUnity is an original deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D-slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its self-supervised training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition …
Children’s Hospital of Philadelphia（费城儿童医院）；RWTH Aachen University（亚琛工业大学）；Cardiff University（卡迪夫大学）
Diffusion imaging is an important method in the field of neuroscience, as it is sensitive to changes within the tissue microstructure of the human brain. However, a major challenge when using MRI to derive quantitative measures is that the use of different scanners, as used in multi-site group studies, introduces measurement variability. This can lead to an increased variance in quantitative metrics, even if the same brain is scanned. Contrary to the assumption that these characteristics are comparable and similar, small changes in these values are observed in many clinical studies, hence harm…
Histopathology Image Normalization via Latent Manifold Compaction
Knight Cancer Institute, Oregon Health and Science University（骑士癌症研究所，俄勒冈健康与科学大学）；Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University（布雷登-科尔森胰腺护理中心，俄勒冈健康与科学大学）
A Pre-trained Foundation Model Framework for Multiplanar MRI Classification of Extramural Vascular Invasion and Mesorectal Fascia Invasion in Rectal Cancer
Maastricht University（马斯特里赫特大学）；Quibim SL；La Fe Health Research Institute（拉费健康研究所）；La Fe University and Polytechnic Hospital（拉费大学与理工医院）；Sapienza University of Rome（罗马大学）；Université d'Angers（昂热大学）；CHU Angers（昂热大学附属医院）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Harmonization improves data consistency and is central to effective integration of diverse imaging data acquired across multiple sites. Recent deep learning techniques for harmonization are predominantly supervised in nature and hence require imaging data of the same human subjects to be acquired at multiple sites. Data collection as such requires the human subjects to travel across sites and is hence challenging, costly, and impractical, more so when sufficient sample size is needed for reliable network training. Here we show how harmonization can be achieved with a deep neural network that d…
Karolinska Institutet（卡罗林斯卡医学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；University of Brescia（布雷西亚大学）；Masaryk University（马萨里克大学）；St. Anne’s Hospital（圣安妮医院）；University G d’Annunzio of Chieti-Pescara（基耶蒂-佩斯卡拉加布里埃莱·邓南遮大学）
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves…
Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstruction
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine（首尔国立大学医学院癌症生物学跨学科项目）；Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital（首尔国立大学医学院放射科，首尔国立大学医院）；Healthcare AI Research Institute, Seoul National University Hospital（首尔国立大学医院医疗人工智能研究机构）
Hosei University（法政大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reduction algorithm. Our DI-PSS uses skull stripping and CycleGAN-based image transformations that map to a standard brain followed by transformation into a brain image taken with a given reference scanner. Then, our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acqu…
MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations
School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院，伦敦，英国）；deepc GMBH, Munich, Germany（deepc 德国慕尼黑分公司）
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）
This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obtained from two different studies, Cam-CAN and UK Biobank. For the purpose of our investigation, we construct a dataset consisting of brain scans from 592 age- and sex-matched individuals, 296 subjects from each original study. Our results demonstrate that even after careful pre-processing with state-of-the-art neuroimaging pipelines a classifier can easily distinguish between the origin of the data with very high acc…
Acquisition of interpretable domain information during brain MR image harmonization for content-based image retrieval
Dept. of Applied Informatics, Graduate School of Science and Engineering, Hosei University（应用信息科，理学部，法政大学）；Dept. of Radiology and Radiological Science, Johns Hopkins Medicine（放射科和放射科学系，约翰霍普金斯医学中心）
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE)（香港脑心血管健康工程中心）；Department of Informatics, Sokoine University of Agriculture（农业科学学院信息系）；Department of Diagnostic Radiology, The University of Hong Kong（香港大学诊断放射科）；State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong（香港大学脑科学与认知科学国家重点实验室）；Alzheimer’s Disease Research Network, The University of Hong Kong（香港大学阿尔茨海默病研究网络）；Department of Biomedical Engineering, City University of Hong Kong（城市大学生物医学工程系）；Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射学与放射科学系）；City University of Hong Kong Shenzhen Research Institute（城市大学深圳研究 institute）
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Johns Hopkins University（约翰斯·霍普金斯大学）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Institute of Biomedical Engineering（生物医学工程研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学）；Department of Radiation Oncology（放射肿瘤科）；University Medical Center Schleswig-Holstein（什勒斯威-霍斯特大学医院）；Department of Experimental and Clinical Medicine（实验与临床医学系）；Magna Graecia University（马格拉西亚大学）；Department of Information Engineering（信息工程系）；Università Politecnica delle Marche（帕尔米拉理工大学）
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（马萨诸塞州总医院）；Harvard Medical School（哈佛医学院）；Boston University（波士顿大学）；Harvard University（哈佛大学）
Low-field (LF) MRI scanners have the power to revolutionize medical imaging by providing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usually significantly noisier and lower quality than their high-field counterparts. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans to improve diagnostic capability. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested deep learning methods with an average PSNR of 78.83 and SSIM of 0.95…
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Accurate automatic segmentation of brain anatomy from $T_1$-weighted~($T_1$-w) magnetic resonance images~(MRI) has been a computationally intensive bottleneck in neuroimaging pipelines, with state-of-the-art results obtained by unsupervised intensity modeling-based methods and multi-atlas registration and label fusion. With the advent of powerful supervised convolutional neural networks~(CNN)-based learning algorithms, it is now possible to produce a high quality brain segmentation within seconds. However, the very supervised nature of these methods makes it difficult to generalize them on dat…
Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
Department of Computer Engineering, College of Computing and Mathematics, King Fahd University of Petroleum & Minerals (KFUPM)（计算机工程系，计算与数学学院，国王法赫德石油与矿物大学）；SDAIA–KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum & Minerals（人工智能联合研究中心（JRC-AI），国王法赫德石油与矿物大学）；Department of Computer Science and Information Technology, Sir Syed University of Engineering and Technology (SSUET)（计算机科学与信息科技系，西德大学工程与技术学院）；Digital Technology & Innovation Center, Siemens Healthineers, Princeton, NJ 08540, USA（数字技术与创新中心，西门子健康部，美国新泽西州普林斯顿）；Department of Aerospace Engineering, King Fahd University of Petroleum & Minerals (KFUPM)（航空航天工程系，国王法赫德石油与矿物大学）；Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals (KFUPM)（智能交通与物流跨学科研究中心，国王法赫德石油与矿物大学）
Facebook AI Research（脸书人工智能研究院）；NYU School of Medicine（纽约大学医学院）；AIRS Medical；Yonsei University（延世大学）；Siemens Healthineers（西门子医疗）；Siemens Healthcare GmbH（西门子医疗有限公司）；CEA (NeuroSpin) & Inria Saclay (Parietal), Université Paris-Saclay（法国原子能委员会（NeuroSpin）与法国国家信息与自动化研究所萨克雷中心（Parietal），巴黎-萨克雷大学）；Département d’Astrophysique, CEA-Saclay（法国原子能委员会萨克雷天体物理系）；Radboud University Medical Center（拉德堡德大学医学中心）；Amsterdam UMC（阿姆斯特丹大学医学中心）
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a …
