IEEE Transactions on Medical Imaging最新文献

{"title":"MultiASNet: Multimodal Label Noise Robust Framework for the Classification of Aortic Stenosis in Echocardiography.","authors":"Victoria Wu,Andrea Fung,Bahar Khodabakhshian,Baraa Abdelsamad,Hooman Vaseli,Neda Ahmadi,Jamie A D Goco,Michael Y Tsang,Christina Luong,Purang Abolmaesumi,Teresa S M Tsang","doi":"10.1109/tmi.2025.3609319","DOIUrl":"https://doi.org/10.1109/tmi.2025.3609319","url":null,"abstract":"Aortic stenosis (AS), a prevalent and serious heart valve disorder, requires early detection but remains difficult to diagnose in routine practice. Although echocardiography with Doppler imaging is the clinical standard, these assessments are typically limited to trained specialists. Point-of-care ultrasound (POCUS) offers an accessible alternative for AS screening but is restricted to basic 2D B-mode imaging, often lacking the analysis Doppler provides. Our project introduces MultiASNet, a multimodal machine learning framework designed to enhance AS screening with POCUS by combining 2D B-mode videos with structured data from echocardiography reports, including Doppler parameters. Using contrastive learning, MultiASNet aligns video features with report features in tabular form from the same patient to improve interpretive quality. To address misalignment where a single report corresponds to multiple video views, some irrelevant to AS diagnosis, we use cross-attention in a transformer-based video and tabular network to assign less importance to irrelevant report data. The model integrates structured data only during training, enabling independent use with B-mode videos during inference for broader accessibility. MultiASNet also incorporates sample selection to counteract label noise from observer variability, yielding improved accuracy on two datasets. We achieved balanced accuracy scores of 93.0% on a private dataset and 83.9% on the public TMED-2 dataset for AS detection. For severity classification, balanced accuracy scores were 80.4% and 59.4% on the private and public datasets, respectively. This model facilitates reliable AS screening in non-specialist settings, bridging the gap left by Doppler data while reducing noise-related errors. Our code is publicly available at github.com/DeepRCL/MultiASNet.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"15 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images","authors":"Shahira Abousamra, Danielle Fassler, Rajarsi Gupta, Tahsin Kurc, Luisa F. Escobar-Hoyos, Dimitris Samaras, Kenneth R. Shroyer, Joel Saltz, Chao Chen","doi":"10.1109/tmi.2025.3609245","DOIUrl":"https://doi.org/10.1109/tmi.2025.3609245","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"46 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying tumour microvasculature with optical coherence angiography and intravoxel incoherent motion diffusion MRI.","authors":"W Jeffrey Zabel,Hector Contreras-Sanchez,Warren Foltz,Costel Flueraru,Edward Taylor,Alex Vitkin","doi":"10.1109/tmi.2025.3607752","DOIUrl":"https://doi.org/10.1109/tmi.2025.3607752","url":null,"abstract":"Intravoxel Incoherent Motion (IVIM) MRI is a contrast-agent-free microvascular imaging method finding increasing use in biomedicine. However, there is uncertainty in the ability of IVIM-MRI to quantify tissue microvasculature given MRI's limited spatial resolution (mm scale). Nine NRG mice were subcutaneously inoculated with human pancreatic cancer BxPC-3 cells transfected with DsRed, and MR-compatible plastic window chambers were surgically installed in the dorsal skinfold. Mice were imaged with speckle variance optical coherence tomography (OCT) and colour Doppler OCT, providing high resolution 3D measurements of the vascular volume density (VVD) and average Doppler phase shift (Δϕ) respectively. IVIM imaging was performed on a 7T preclinical MRI scanner, to generate maps of the perfusion fraction f, the extravascular diffusion coefficient Dslow, and the intravascular diffusion coefficient Dfast. The IVIM parameter maps were coregistered with the optical datasets to enable direct spatial correlation. A significant positive correlation was noted between OCT's VVD and MR's f (Pearson correlation coefficient r = 0.34,p < 0.0001). Surprisingly, no significant correlation was found between Δϕ and Dfast. This may be due to larger errors in the determined Dfast values compared to f, as confirmed by Monte Carlo simulations. Several other inter- and intra-modality correlations were also quantified. Direct same-animal correlation of clinically applicable IVIM imaging with preclinical OCT microvascular imaging support the biomedical relevance of IVIM-MRI metrics, for example through f's relationship to the VVD.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"59 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CASHNet: Context-Aware Semantics-driven Hierarchical Network for Hybrid Diffeomorphic CT-CBCT Image Registration.","authors":"Xiaoru Gao,Housheng Xie,Donghua Hang,Guoyan Zheng","doi":"10.1109/tmi.2025.3607700","DOIUrl":"https://doi.org/10.1109/tmi.2025.3607700","url":null,"abstract":"Computed Tomography (CT) to Cone-Beam Computed Tomography (CBCT) image registration is crucial for image-guided radiotherapy and surgical procedures. However, achieving accurate CT-CBCT registration remains challenging due to various factors such as inconsistent intensities, low contrast resolution and imaging artifacts. In this study, we propose a Context-Aware Semantics-driven Hierarchical Network (referred to as CASHNet), which hierarchically integrates context-aware semantics-encoded features into a coarse-to-fine registration scheme, to explicitly enhance semantic structural perception during progressive alignment. Moreover, it leverages diffeomorphisms to integrate rigid and non-rigid registration within a single end-to-end trainable network, enabling anatomically plausible deformations and preserving topological consistency. CASHNet comprises a Siamese Mamba-based multi-scale feature encoder and a coarse-to-fine registration decoder, which integrates a Rigid Registration (RR) module with multiple Semantics-guided Velocity Estimation and Feature Alignment (SVEFA) modules operating at different resolutions. Each SVEFA module comprises three carefully designed components: i) a cross-resolution feature aggregation (CFA) component that synthesizes enhanced global contextual representations, ii) a semantics perception and encoding (SPE) component that captures and encodes local semantic information, and iii) an incremental velocity estimation and feature alignment (IVEFA) component that leverages contextual and semantic features to update velocity fields and to align features. These modules work synergistically to boost the overall registration performance. Extensive experiments on three typical yet challenging CT-CBCT datasets of both soft and hard tissues demonstrate the superiority of our proposed method over other state-of-the-art methods. The code will be publicly available at https://github.com/xiaorugao999/CASHNet.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"14 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lesion Asymmetry Screening Assisted Global Awareness Multi-view Network for Mammogram Classification.","authors":"Xinchuan Liu,Luhao Sun,Chao Li,Bowen Han,Wenzong Jiang,Tianhao Yuan,Weifeng Liu,Zhaoyun Liu,Zhiyong Yu,Baodi Liu","doi":"10.1109/tmi.2025.3607877","DOIUrl":"https://doi.org/10.1109/tmi.2025.3607877","url":null,"abstract":"Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability. To address this issue, this paper proposes a novel end-to-end framework for breast cancer diagnosis: lesion asymmetry screening assisted global awareness multi-view network (LAS-GAM). More than just the most common image-level diagnostic model, LAS-GAM operates at the patient level, simulating the workflow of radiologists analyzing mammographic images. The framework processes the four views of a patient and revolves around two key modules: a global module and a lesion screening module. The global module simulates the comprehensive assessment by radiologists, integrating complementary information from the craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts to generate global features that represent the patient's overall condition. The lesion screening module mimics the process of locating lesions by comparing symmetric regions in contralateral views, identifying potential lesion areas and extracting lesion-specific features using a lightweight model. By combining the global features and lesion-specific features, LAS-GAM simulates the diagnostic process, making patient-level predictions. Moreover, it is trained using only patient-level labels, significantly reducing data annotation costs. Experiments on the Digital Database for Screening Mammography (DDSM) and In-house datasets validate LAS-GAM, achieving AUCs of 0.817 and 0.894, respectively.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"27 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Activation Pattern Analysis based on Hidden Semi-Markov Model for Brain Spatiotemporal Dynamics","authors":"Zihao Yuan, Jiaqing Chen, Han Qiu, Houxiang Wang, Yangxin Huang, Fuchun Lin","doi":"10.1109/tmi.2025.3607113","DOIUrl":"https://doi.org/10.1109/tmi.2025.3607113","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"46 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"J-Score: Joint Distribution Learning with Score-based Diffusion for Accelerating T1rho Mapping","authors":"Congcong Liu, Yuanyuan Liu, Chentao Cao, Jing Cheng, Qingyong Zhu, Tian Zhou, Chen Luo, Yanjie Zhu, Haifeng Wang, Zhuo-Xu Cui, Dong Liang","doi":"10.1109/tmi.2025.3606660","DOIUrl":"https://doi.org/10.1109/tmi.2025.3606660","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"64 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-ASIC Architecture-Driven Deep Learning Photon-Counting Detector Model Under Limited Data","authors":"Xiaopeng Yu, Qianyu Wu, Wenhui Qin, Tao Zhong, Mengqing Su, Jinglu Ma, Yikun Zhang, Xu Ji, Wenying Wang, Guotao Quan, Yanfeng Du, Yang Chen, Xiaochun Lai","doi":"10.1109/tmi.2025.3605886","DOIUrl":"https://doi.org/10.1109/tmi.2025.3605886","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"62 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stroke-Aware CycleGAN: Improving Low-Field MRI Image Quality for Accurate Stroke Assessment.","authors":"Yu Zhou,Ziyang Liu,Xuewei Xie,Hao Li,Wanlin Zhu,Zhe Zhang,Yue Suo,Xia Meng,Jian Cheng,Hong Xu,Ning Wang,Yihuai Wang,Chunguang Zhang,Bingshan Xue,Jing Jing,Yongjun Wang,Tao Liu","doi":"10.1109/tmi.2025.3605566","DOIUrl":"https://doi.org/10.1109/tmi.2025.3605566","url":null,"abstract":"Low-field portable magnetic resonance imaging (pMRI) devices address a crucial requirement in the realm of healthcare by offering the capability for on-demand and timely access to MRI, especially in the context of routine stroke emergency. Nevertheless, images acquired by these devices often exhibit poor clarity and low resolution, resulting in their reduced potential to support precise diagnostic evaluations and lesion quantification. In this paper, we propose a 3D deep learning based model, named Stroke-Aware CycleGAN (SA-CycleGAN), to enhance the quality of low-field images for further improving diagnosis of routine stroke. Firstly, based on traditional CycleGAN, SA-CycleGAN incorporates a prior of stroke lesions by applying a novel spatial feature transform mechanism. Secondly, gradient difference losses are combined to deal with the problem that the synthesized images tend to be overly smooth. We present a dataset comprising 101 paired high-field and low-field diffusion-weighted imaging (DWI), which were acquired through dual scans of the same patient in close temporal proximity. Our experiments demonstrate that SA-CycleGAN is capable of generating images with higher quality and greater clarity compared to the original low-field DWI. Additionally, in terms of quantifying stroke lesions, SA-CycleGAN outperforms existing methods. The lesion volume exhibits a strong correlation between the generated images and the high-field images, with R=0.852. In contrast, the lesion volume correlation between the low-field images and the high-field images is notably lower, with R=0.462. Furthermore, the mean absolute difference in lesion volumes between the generated images and high-field images (1.73±2.03 mL) was significantly smaller than the difference between the low-field images and high-field images (2.53±4.24 mL). It shows that the synthesized images not only exhibit superior visual clarity compared to the low-field acquired images, but also possess a high degree of consistency with high-field images. In routine clinical practice, the proposed SA-CycleGAN offers an accessible and cost-effective means of rapidly obtaining higher-quality images, holding the potential to enhance the efficiency and accuracy of stroke diagnosis in routine clinical settings. The code and trained models will be released on GitHub: SA-CycleGAN.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"15 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic surgery video summarization with balancing informativeness and diversity.","authors":"Hao Wang,Yiyang Su,Min Xue,Xuefei Song,Cheng Yang,Lei Shi,Xianqun Fan,Shuai Ding","doi":"10.1109/tmi.2025.3603170","DOIUrl":"https://doi.org/10.1109/tmi.2025.3603170","url":null,"abstract":"Surgery video summarization can help medical professionals quickly gain the insight into the surgical process for the surgical education and skill evaluation. However, existing methods are unable to efficiently summarize information to satisfy medical professionals. Since it is challenging to summarize the video while balancing the information richness and diversity. In this paper, we propose a dynamic surgery video summarization framework (DSVS). We first used a multitask learning network to perceive and comprehend surgical action triplet components and phases. An information contribution module then measures the frame-level importance using the predicted triplets. A two-stage strategy which involves phase recognition and change-point detection further applied to divide each phase of the surgical videos into shots. Finally, A multi-objective zero-one programming model is formulated to select the optimal subset of shots by simultaneously maximizing intra-shot information contribution and minimizing inter-shot information similarity. Experimental results on two surgical video datasets show the framework can generate summaries that encompass crucial and diverse content. Clinical validations indicate the framework is capable of summarizing the information expected by surgeons. The source code can be found at https://github.com/syypretend/DSVS.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"36 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}