IEEE transactions on medical imaging最新文献

{"title":"The Ritz Adjoint Method for MRI Pulse Design","authors":"John M. Drago;Georgy D. Guryev;Nicolas Arango;Elfar Adalsteinsson;Bastien Guerin;Lawrence L. Wald","doi":"10.1109/TMI.2026.3709056","DOIUrl":"10.1109/TMI.2026.3709056","url":null,"abstract":"High-field magnetic resonance imaging (MRI) suffers from pronounced magnetic field inhomogeneities and subject-specific field variations, motivating the in-scanner design of individually tailored excitation pulses to exploit the full capabilities of high-field MRI. Contemporary methods may employ piecewise-constant (PWC) waveform parameterizations to design excitation pulses, which require many optimization variables and hinder rapid, in-scanner customization. We represent the radiofrequency (RF) and gradient waveforms in a global waveform basis, demonstrated using a Chebyshev polynomial basis, to reduce problem dimensionality and accelerate convergence, while ensuring waveform smoothness. The adjoint method efficiently computes derivatives of the excitation objective function with respect to the basis coefficients, which each influence an entire waveform. Graphics processing unit (GPU) acceleration of derivative computation further reduces computation time, while system and safety constraints are enforced throughout the optimization. Using this global waveform basis yields an approximate five- to ten-fold speedup in subject-specific (tailored) pulse optimization for non-selective excitations and, in the best case, comparable gains for slice-selective designs, making real-time, subject-specific pulse optimization feasible even for advanced pulse types.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4770-4783"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11592647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148365089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval-Augmented Representation for Semi-Supervised Medical Image Segmentation","authors":"Huan Luo;Qingjie Zeng;Yanning Zhang;Yong Xia","doi":"10.1109/TMI.2026.3712008","DOIUrl":"10.1109/TMI.2026.3712008","url":null,"abstract":"Semi-supervised learning (SSL) offers a promising solution to reduce annotation costs in medical image segmentation. Recent text-enhanced SSL methods incorporate domain-specific textual cues to improve representation learning. However, they often overemphasize language priors and neglect the importance of visual features for precise segmentation. In this work, we propose Retrieval-Augmented Representation (RAR), a novel framework that enriches the representation of unlabeled data by retrieving semantically relevant labeled samples from both visual and textual perspectives. Rather than fusing multimodal features, RAR treats the retrieved samples as positive anchors in a contrastive learning framework, encouraging the model to learn robust and spatially coherent features guided by both semantic context and anatomical structure. Extensive experiments across five diverse modalities (CT, X-ray, MRI, ultrasound, and pathology) show that RAR consistently outperforms state-of-the-art vision-only and text-enhanced SSL approaches, achieving an average Dice improvement of 6.2% over the strongest baseline across the evaluated tasks, while maintaining competitive computational efficiency. Our results suggest the robustness and generalizability of RAR. Code is available at: <uri>https://github.com/jgfiuuuu/RAR</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4892-4904"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OA-Recon: An Online Adaptive Reconstruction Framework for Sparse-View Multi-Energy CT.","authors":"Xi Wang, Tong Lin, Jiashun Wang, Xu Ji, Chun Yang, Tianyu Tang, Yikun Zhang, Jean-Louis Coatrieux, Jiasong Wu, Shenghong Ju, Yang Chen","doi":"10.1109/TMI.2026.3728200","DOIUrl":"https://doi.org/10.1109/TMI.2026.3728200","url":null,"abstract":"<p><p>Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radiation exposure, we propose a complementary sparse-view scanning protocol tailored for MECT, which reduces radiation dose while maximizing angular coverage. To reconstruct high-quality images from these sparse-view data and ensure algorithmic reliability in practical applications, we introduce an Online Adaptive Reconstruction (OA-Recon) framework that adapts robustly to varying acquisition settings through two designs. First, it adopts a Bayesian adaptation strategy for instance-specific optimization while preserving the learned prior. Second, it incorporates a Frequency-adaptive and Physics-informed Network (FaPiNet) for adaptive feature extraction and acquisition-conditioned feature modulation. In addition, it incorporates a spectral attention mechanism to fully exploit complementary information across energy channels. Experiments on simulated MECT and real mouse PCCT data show that FaPiNet-OA-Recon achieves better performance in suppressing streak artifacts, restoring image details, and maintaining CT-value accuracy. More importantly, OA-Recon demonstrates adaptability to changes in view, spectrum, and anatomy, providing a preliminarily feasible solution for clinical applications of MECT.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement.","authors":"Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang","doi":"10.1109/TMI.2026.3727981","DOIUrl":"10.1109/TMI.2026.3727981","url":null,"abstract":"<p><p>Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases. Scaling up the dataset size by combining the cross-center samples may bring a gain in the pursuit of performance, while the inherent data heterogeneity across centers or populations induces the conflict. Conventional multi-task learning paradigms offer a promising framework; however, they fail to consider critical meta information (e.g., site-specific acquisition and modality availability) to combat the heterogeneity. To address this challenge, we propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis, which injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training, enabling explicit modeling of heterogeneity. Besides, a trustregion constrained optimization scheme is designed to regularize the model from spurious correlations during training through a reference model. Across seven independent cohorts, our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline, while maintaining superior out-of-domain generalization under both cross-center and cross-sequence evaluations. Extensive validation also confirms the alignment between model predictions and established biomarkers (amyloid, tau) and clinical severity, further supporting COME's robustness and interpretability for multi-center dementia etiology analysis.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Unified Graph Domain Adaptation Framework for Cross-Site Brain Network Analysis.","authors":"Lin Zhao, Cheng Ding, Shengrong Li, Chunwei Tian, Chao Huang, Donghai Guan, Daoqiang Zhang, Qi Zhu","doi":"10.1109/TMI.2026.3726773","DOIUrl":"https://doi.org/10.1109/TMI.2026.3726773","url":null,"abstract":"<p><p>Compared to single-site brain network analysis, multi-site methods leverage larger sample sizes to achieve more robust prediction and generalizability, which has garnered increasing attention in brain disease diagnosis. However, these approaches also face heightened heterogeneity across sites (e.g., scanner protocols) and subjects (e.g., clinical variability), along with potential outliers with distributional shifts in connectivity patterns that may misguide model training. To address these challenges, we propose a unified graph domain adaptation framework for cross-site brain network analysis, which mitigates the heterogeneity at the sample and site levels while preserving their topological structure. Specifically, we first develop graph convolution to extract the topological representations of brain networks in the source and target sites. Next, adversarial learning is employed to learn domain invariant representations, while domain shift across sites is mitigated. Additionally, we further align intra-class similarity and inter-class dissimilarity across sites by contrastive learning, enhancing feature discriminability and alleviating sample heterogeneity. Finally, we incorporate a self-paced learning strategy that gradually assesses the difficulty and uncertainty of samples, which guides the model to prioritize simpler samples during the early training, thereby mitigating the influence of difficult samples and further reducing sample heterogeneity. Extensive experiments on multi-site brain disorder datasets demonstrate that our proposed method outperforms state-of-the-art methods in cross-site brain disease analysis tasks.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Context Perception Attention Generative Adversarial Network with Large Foundation Models for Alzheimer's Disease Risk Prediction.","authors":"Zhaoxu Xing, Da-Fang Zhang, Kun Xie, Wenli Chen, Xia-An Bi, Tianming Liu","doi":"10.1109/TMI.2026.3727077","DOIUrl":"https://doi.org/10.1109/TMI.2026.3727077","url":null,"abstract":"<p><p>Alzheimer's disease (AD) risk prediction relies on accurately characterizing pathological mechanisms underlying AD progression. However, existing methods struggle with heterogeneous multi-omics data and often fail to capture the spatiotemporal dynamics of the disease, limiting their predictive performance. In this paper, an integrated framework fusing spatial and temporal information is proposed to improve prediction capability. First, brain region-gene directed networks are constructed based on large foundation model-enhanced features. Second, a context perception attention model is designed to characterize topological changes of directed networks during AD progression. Based on this model, we develop a Context Perception Attention Generative Adversarial Network (CPA-GAN) that leverages adversarial training to mine AD evolutionary patterns, thereby supporting risk prediction and pathogeny extraction. Finally, the superiority, effectiveness, and robustness of CPA-GAN are validated by extensive experiments. Overall, this work provides a robust and effective modeling framework tailored for early-stage AD risk prediction.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RankByGene: GeneGene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency.","authors":"Wentao Huang, Meilong Xu, Xiaoling Hu, Shahira Abousamra, Aniruddha Ganguly, Saarthak Kapse, Alisa Yurovsky, Prateek Prasanna, Tahsin Kurc, Joel Saltz, Michael L Miller, Chao Chen","doi":"10.1109/TMI.2026.3725898","DOIUrl":"10.1109/TMI.2026.3725898","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal relationships. To address these limitations, we propose a novel framework that aligns gene and image features using a ranking-based alignment loss, preserving relative similarity across modalities and enabling robust multi-scale alignment. To further enhance the alignment's stability, we employ self-supervised knowledge distillation with a teacher-student network architecture, which serves as an intra-modal stability regularizer that prevents image-representation drift during cross-modal alignment. Extensive experiments on seven public datasets that encompass gene expression prediction, slide-level classification, and survival analysis demonstrate the efficacy of our method, showing improved alignment and predictive performance over existing methods. Code is available at https://github.com/winston52/RankByGene.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SurgFusion-Net: Diversified Adaptive Multimodal Fusion Network for Surgical Skill Assessment.","authors":"Runlong He, Freweini M Tesfai, Matthew W E Boal, Nazir Sirajudeen, Dimitrios Anastasiou, Jialang Xu, Mobarak I Hoque, Philip J Edwards, John D Kelly, Ashwin Sridhar, Abdolrahim Kadkhodamohammadi, Dhivya Chandrasekaran, Matthew J Clarkson, Danail Stoyanov, Nader Francis, Evangelos B Mazomenos","doi":"10.1109/TMI.2026.3722251","DOIUrl":"10.1109/TMI.2026.3722251","url":null,"abstract":"<p><p>Robotic-assisted surgery (RAS) is established in clinical practice, and automated surgical skill assessment utilizing multimodal data offers transformative potential for surgical analytics and education. However, developing effective multimodal methods remains challenging due to the task complexity, limited annotated datasets and insufficient techniques for cross-modal information fusion. Existing state-of-the-art methods rely exclusively on RGB video and are evaluated only in dry-lab settings, failing to bridge the substantial domain gap to real clinical procedures. In clinical RAS cases such as Robot-assisted Hysterectomy (RAH) and Robot-Assisted Radical Prostatectomy (RARP), background tissue peristalsis and strong specular reflections from instruments, needles, and catheters introduce significant visual challenges. This work introduces RAH-skill and RARP-skill, two clinical datasets for RAS skill assessment, comprising 279,691 and 70,661 RGB frames from 37 and 33 suturing videos, both paired with expert M-GEARS annotations and automatically generated optical flow and segmentation masks. We propose SurgFusion-Net, a novel multimodal fusion network that integrates RGB frames, optical flow, and tool segmentation masks to address background motion noise and instrument specular reflections in RAS skill assessment. The core component, Divergence Regulated Attention (DRA), is a scene-aware fusion mechanism that adaptively regulates cross-modal feature alignment to address the visual challenges of clinical cases, ensuring accurate and robust RAS skill assessment across heterogeneous surgical settings. Validated on the JIGSAWS benchmark, RAH-skill, and RARP-skill datasets, our approach outperforms recent baselines with SCC improvements of 0.02 in LOSO, 0.04 in LOUO across JIGSAWS tasks, and 0.0538 and 0.0493 gains on RAH-skill and RARP-skill, respectively. Our source code and dataset is available at https://github.com/HRL-Mike/SurgFusion-Net.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAT-WSI: Context-Aware Trajectory Learning for Whole-Slide Breast Pathology Segmentation.","authors":"Jiajun Qiu, Chaoran Zhang, Guangjing Yang, Chang Zheng, Xiaorong Zhong, Zhang Zhang, Ting Luo, Shaoting Zhang, Qicheng Lao, Jin Yin, Jing Jing","doi":"10.1109/TMI.2026.3721494","DOIUrl":"10.1109/TMI.2026.3721494","url":null,"abstract":"<p><p>Computational analysis of breast histopathological images is critical for reliable computer-aided diagnosis and treatment planning. Owing to the ultra-high resolution of whole-slide images (WSIs), most existing WSI segmentation methods rely on patch-wise processing. However, independently processing isolated patches breaks spatial continuity and weakens global tissue context, ultimately limiting segmentation performance. To overcome these limitations, we propose CAT-WSI, a context-aware trajectory learning framework for breast pathology WSI segmentation. Rather than treating patches as unordered samples, CAT-WSI organizes them into structured transverse trajectories across each slide, thereby preserving long-range spatial dependencies while reducing the directional bias and boundary fragmentation inherent in conventional patch-based pipelines. To further enhance global positional awareness, CAT-WSI augments these trajectory representations with a paired downsampled whole-slide thumbnail, enabling explicit global-local contextual modeling over the entire slide. We evaluate CAT-WSI on the CAMELYON16 and Breast-HER2+ datasets across multiple magnification levels. Extensive experiments demonstrate that CAT-WSI achieves consistently strong performance across multiple magnification levels, attaining the best overall results on the evaluated benchmarks in our experimental setting.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topo-Morphological Edge Logits Field for 3D Dental Proposal Generation and Instance Segmentation.","authors":"Jiafu Zhuang, Lanxiang Chen","doi":"10.1109/TMI.2026.3721080","DOIUrl":"10.1109/TMI.2026.3721080","url":null,"abstract":"<p><p>Automatic tooth segmentation is essential for computer-aided diagnosis and treatment planning in dentistry. Proposal generation plays a pivotal role in accurately delineating instance boundaries and is critical for improving segmentation accuracy. However, existing methods suffer from dispersive offset bias, spatial information loss, and spillover-induced centroid shift, all of which are difficult to alleviate in Euclidean space. This limitation commonly leads to missed detections or excessive merging of instances, particularly in cases of missing or crowded teeth. To overcome these issues, we introduce a novel proposal generation framework based on Topological Morphological Clustering (TMC), which integrates edge logits fields with topological morphology. The key insight is that morphological erosion applied to tooth and edge masks enables the separation of individual tooth instances. To further enhance the continuity of tooth-tooth edges, we design an edge enhancement module that fuses the alpha-shape algorithm with our proposed KNN-based iterative contour approximation (KNN-ICA) method, thereby constructing a supplementary geometric edge logits field. Extensive experiments show that our method achieves superior detection performance over existing proposal generation techniques, and outperforms prior instance segmentation methods on two public benchmarks. Our code is available at https://github.com/jfzhuang314/TMELFNet.</p>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}