Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods

• URL: http://arxiv.org/abs/2507.16962v1
• Date: Tue, 22 Jul 2025 19:06:02 GMT
• Title: Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods
• Authors: Qinqin Yang, Firoozeh Shomal-Zadeh, Ali Gholipour,
• Abstract summary: "batch effects" or "site effects" obscure true biological signals, reduce statistical power, and impair learning-based models.<n>Image harmonization aims to eliminate or mitigate such site-related biases while preserving meaningful biological information.<n>This review provides a comprehensive overview of key concepts, methodological advances, publicly available datasets, current challenges, and future directions in the field of medical image harmonization.
• Score: 3.515395856924995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern medical imaging technologies have greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imaging sites often exhibit substantial heterogeneity, known as "batch effects" or "site effects". These non-biological sources of variability can obscure true biological signals, reduce reproducibility and statistical power, and severely impair the generalizability of learning-based models across datasets. Image harmonization aims to eliminate or mitigate such site-related biases while preserving meaningful biological information, thereby improving data comparability and consistency. This review provides a comprehensive overview of key concepts, methodological advances, publicly available datasets, current challenges, and future directions in the field of medical image harmonization, with a focus on magnetic resonance imaging (MRI). We systematically cover the full imaging pipeline, and categorize harmonization approaches into prospective acquisition and reconstruction strategies, retrospective image-level and feature-level methods, and traveling-subject-based techniques. Rather than providing an exhaustive survey, we focus on representative methods, with particular emphasis on deep learning-based approaches. Finally, we summarize the major challenges that remain and outline promising avenues for future research.

Related papers

• A Survey of fMRI to Image Reconstruction [21.906165069577895]
  Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception.<n>Recent advancements in deep learning have driven progress, but challenges such as data scarcity, cross-subject variability, and low semantic consistency persist.<n>We introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field.
  arXiv  Detail & Related papers  (2025-02-24T05:53:04Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI [1.049712834719005]
  We present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information.
  arXiv  Detail & Related papers  (2023-09-19T16:08:33Z)
• Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI [4.8296943294326145]
  We propose a one-shot learning method where we utilize neural style transfer for harmonization. At test time, the method uses one image from a clinical site to generate an image that matches the intensity scale of the collaborating sites. Experimental results demonstrate the effectiveness of our method in preserving patient anatomy while adjusting the image intensities to a new clinical site.
  arXiv  Detail & Related papers  (2023-08-21T21:13:30Z)
• Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers [0.293168019422713]
  We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution. Our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level.
  arXiv  Detail & Related papers  (2022-08-03T18:58:45Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging [4.396671464565882]
  We propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia.
  arXiv  Detail & Related papers  (2020-10-11T22:01:37Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.