IEEE transactions on medical imaging最新文献

Simulation and Design Study of Edge-on GaAs:Cr Detector for Clinical Photon Counting CT. 临床光子计数CT边缘上GaAs:Cr探测器的模拟与设计研究。

IEEE transactions on medical imaging Pub Date : 2026-09-04 DOI: 10.1109/TMI.2026.3730875

Tao Zhong, He Li, Wenhui Qin, Xiaopeng Yu, Xiaochun Lai

{"title":"Simulation and Design Study of Edge-on GaAs:Cr Detector for Clinical Photon Counting CT.","authors":"Tao Zhong, He Li, Wenhui Qin, Xiaopeng Yu, Xiaochun Lai","doi":"10.1109/TMI.2026.3730875","DOIUrl":"https://doi.org/10.1109/TMI.2026.3730875","url":null,"abstract":"Chromium-compensated Gallium Arsenide (GaAs:Cr) has emerged as a promising sensor material for photon counting detectors (PCDs) due to its excellent resistivity and charge carrier mobility. However, due to the limitations of the Cr compensation process, the GaAs:Cr wafer thickness is less than 1 mm, which distorts its X-ray absorption efficiency and limits its application in clinical computed tomography (CT). This study proposes a GaAs detector with an edge-on structure for clinical photon counting CT (PCCT) applications. To evaluate the performance of the detector, we modeled the spectral response and analyzed material decomposition (MD) noise using a full-chain detector model. In imaging domain study, digital phantoms, such as low contrast phantom, Gammex phantom, and XCAT digital phantoms, were virtually scanned to assess the image uniformity, CT number accuracy, material decomposition, and virtual mono-energetic imaging (VMI) performance, with the detector system modeled to reflect actual PCCT prototypes. The results indicate that the GaAs:Cr detector exhibits excellent mono-energetic spectrum response and low material decomposition (MD) noise. Additionally, the GaAs:Cr detector provides lower noise, higher contrast-to-noise ratio (CNR) in VMI results, and more precise concentration measurements in quantitative analysis, compared to existing PCDs. These findings highlight the feasibility of developing GaAs:Cr detectors for clinical CT applications, offering potential advantages over existing PCDs.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893022","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}

引用次数: 0

Multi-Granularity Graph-Mamba Multi-Instance Learning for Unlabeled Autofluorescence Whole-Slide Image Classification. 基于多粒度图-曼巴多实例学习的无标记自动荧光整张图像分类。

IEEE transactions on medical imaging Pub Date : 2026-09-02 DOI: 10.1109/TMI.2026.3729829

Jiuyang Dong, Yu Lei, Junjun Jiang, Jiahan Li, Haiyu Zhou, Yongbing Zhang

{"title":"Multi-Granularity Graph-Mamba Multi-Instance Learning for Unlabeled Autofluorescence Whole-Slide Image Classification.","authors":"Jiuyang Dong, Yu Lei, Junjun Jiang, Jiahan Li, Haiyu Zhou, Yongbing Zhang","doi":"10.1109/TMI.2026.3729829","DOIUrl":"https://doi.org/10.1109/TMI.2026.3729829","url":null,"abstract":"Multi-instance learning (MIL) has significantly advanced AI-assisted cancer diagnosis using histochemically stained whole-slide images (WSIs). However, acquiring such WSIs is time-consuming, labor-intensive, and environmentally unfriendly. To address this, we propose using unlabeled autofluorescence (UAF) WSIs as a cost-effective alternative for MIL-based diagnosis. We introduce a dedicated UAF WSI dataset, LCUHI-UAF, along with a matched H&E-stained WSI dataset, LCUHI-H&E, derived from the same tissue sections for direct comparison. To tackle the low signal-to-noise ratio and blurred morphological details in UAF images for classification tasks, we propose a novel Multi-Granularity Graph-Mamba (MGGM) MIL framework. In this framework, each WSI is represented as a graph constructed from the spatial coordinates of tissue instances, allowing graph convolution to capture local spatial dependencies while the Mamba architecture models long-range relationships. A multi-granularity mechanism is further proposed to enable comprehensive representation of hierarchical relationships among cell clusters and microenvironments within the tissue. Experiments on the paired lung cancer datasets LCUHI-H& E and LCUHI-UAF show that MGGM-MIL achieves top-performing results across two pre-trained feature settings. These results simultaneously confirm the viability of UAF-stained WSIs as a cost-effective substitute for H&E in cancer diagnosis. Additional evaluation on the Camelyon16 breast cancer dataset further validates the generalizability of MGGM-MIL across tissue types. Code is available at https://github.com/JiuyangDong/MGGMMIL.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883150","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}

引用次数: 0

UniTransAD: Unified Translation Framework for Anomaly Detection in Brain MRI UniTransAD：脑MRI异常检测的统一翻译框架

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-07-10 DOI: 10.1109/TMI.2026.3711975

Qi Zhang;Xia Li;Yibo Hu;Jianqi Sun

{"title":"UniTransAD: Unified Translation Framework for Anomaly Detection in Brain MRI","authors":"Qi Zhang;Xia Li;Yibo Hu;Jianqi Sun","doi":"10.1109/TMI.2026.3711975","DOIUrl":"10.1109/TMI.2026.3711975","url":null,"abstract":"Unsupervised anomaly detection (UAD) in brain MRI is crucial for early diagnosis, yet generalizing existing methods across diverse diseases, sequences, and missing data scenarios remains a significant challenge. Current reconstruction-based methods often fail to detect subtle anomalies, while conventional translation methods lack flexibility regarding input sequences. To address these limitations, we propose UniTransAD, a unified translation-based anomaly detection framework. UniTransAD introduces three key innovations: 1) a unified cyclic-translation inference paradigm built upon content-style disentanglement, capable of processing diverse brain MRI inputs; 2) a Dynamic Style Prototype Memory (DSPM) that enables a flexible and robust cyclic-inference mechanism; and 3) a dual-level detection mechanism that combines pixel-level translation errors with feature-level dissimilarities to enhance detection specificity. Furthermore, to rigorously evaluate generalization beyond disease-specific datasets, we establish the Brain-OmniA evaluation dataset, aggregating seven public datasets covering distinct brain pathologies and sequences. Extensive experiments demonstrate that UniTransAD significantly outperforms state-of-the-art methods on Brain-OmniA with superior flexibility. In summary, UniTransAD offers a robust, flexible and generalizable solution for clinical anomaly detection in heterogeneous clinical environments. Our code, pre-trained models, and full dataset are available at: <uri>https://github.com/zhibaishouheilab/UniTransAD</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4876-4891"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148416094","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}

引用次数: 0

Annotation-efficient Semi-supervised and Active Learning for Breast Cancer Segmentation in DCE-MRI. 基于注释的半监督和主动学习的DCE-MRI乳腺癌分割。

IEEE transactions on medical imaging Pub Date : 2026-09-01 DOI: 10.1109/TMI.2026.3729235

Zetian Feng, Quanling Zou, Yu Xie, Wenrong Cai, Sheng Zhong, Zeyan Xu, Yi Wang

{"title":"Annotation-efficient Semi-supervised and Active Learning for Breast Cancer Segmentation in DCE-MRI.","authors":"Zetian Feng, Quanling Zou, Yu Xie, Wenrong Cai, Sheng Zhong, Zeyan Xu, Yi Wang","doi":"10.1109/TMI.2026.3729235","DOIUrl":"https://doi.org/10.1109/TMI.2026.3729235","url":null,"abstract":"Accurate breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital for diagnosis and treatment planning. Despite advances in deep learning, its performance remains constrained by the need for extensive voxel-wise annotations. To mitigate this burden, we propose an annotation-efficient framework that jointly optimizes data selection, unlabeled data utilization, and data augmentation under limited annotation budgets. A diversity-aware uncertainty query (DUQ) strategy guides the annotation by jointly modeling data representativeness and informativeness through a representative candidate selector (RCS) and an uncertainty-based decision maker (UDM), ensuring efficient and targeted labeling. To leverage unlabeled data, a cross-decoder consistency regularization (CDCR) mechanism enforces prediction consistency between two decoders with distinct attention mechanisms, enhancing robustness and confidence. Furthermore, a lesion transplant augmentation (LTA) technique synthesizes anatomically valid pseudo samples by transplanting lesion regions from labeled to unlabeled images, effectively expanding training diversity. Experiments were conducted on two DCE-MRI datasets with biopsy-proven breast cancers, one as internal dataset containing 676 subjects and the other as external dataset with 344 subjects. Comparative and ablation results demonstrate that our framework consistently outperforms state-of-the-art semi-supervised and active learning methods, providing a simple yet effective annotation-efficient solution for breast cancer segmentation in DCE-MRI. The code is publicly available at https: //github.com/zouquanling/DUQ_and_CDCR.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876829","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}

引用次数: 0

UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-Directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT 基于改进层次提示策略和双向渐进式一致性学习的通用OCT视网膜层分割。

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-07-06 DOI: 10.1109/TMI.2026.3710244

Jian Zhong;Li Lin;Kenneth K. Y. Wong;Xiaoying Tang

{"title":"UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-Directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT","authors":"Jian Zhong;Li Lin;Kenneth K. Y. Wong;Xiaoying Tang","doi":"10.1109/TMI.2026.3710244","DOIUrl":"10.1109/TMI.2026.3710244","url":null,"abstract":"Universal medical image segmentation aims to unify heterogeneous datasets or annotation protocols within a single adaptable framework. However, existing prompt-based universal models often overlook background context, neglect hierarchical task dependencies, and struggle to generalize to unseen annotation granularities. These challenges are particularly pronounced in OCT-based retinal layer segmentation, where annotation schemes differ significantly across studies. To this end, we here propose UniOCTSeg++, a universal OCT segmentation framework that 1) introduces a Refined Hierarchical Prompting Strategy (RHPS) to reconstruct task-aware prompts into foreground-background paired embeddings, explicitly encoding fine-to-coarse anatomical relationships; and 2) adopts a Bi-directional Progressive Consistency Learning (BPCL) scheme that enforces mutual constraints between fine- and coarse-grained predictions under a training schedule with gradually increasing task difficulty, improving stability and mitigating pseudo-label noise. Moreover, we construct the Hierarchical Retinal OCT Segmentation Benchmark (HROCT-Bench), comprising 4.86 million OCT B-scans collected from eleven public datasets across eight annotation granularities, providing a unified evaluation protocol for universal OCT segmentation. Extensive experiments demonstrate that UniOCTSeg++ achieves state-of-the-art adaptability, reaching 90.06% DSC/ 1.38 HD95 on internal datasets and 86.83% DSC / 2.00 HD95 on external datasets. We further demonstrate UniOCTSeg++’s strong label efficiency: when trained with only 30% labeled data and supplemented with large-scale unlabeled data, UniOCTSeg++ approaches the performance of its fully supervised counterpart, highlighting its practical value for real-world deployment. The benchmark and code will be released at <uri>https://github.com/Halcyon1010/UniOCTSeg++</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4809-4821"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148393513","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}

引用次数: 0

Prototype-guided Multimodal Retrieval for Knowledge-assisted Interventional Radiology. 知识辅助介入放射学的原型引导多模态检索。

IEEE transactions on medical imaging Pub Date : 2026-09-01 DOI: 10.1109/TMI.2026.3728817

Jingxiong Li, Chenglu Zhu, Yuxuan Sun, Yixuan Si, Lin Yang, Guojun Li, Yizhe Zhang, Liang Xiao, Tao Zhou

{"title":"Prototype-guided Multimodal Retrieval for Knowledge-assisted Interventional Radiology.","authors":"Jingxiong Li, Chenglu Zhu, Yuxuan Sun, Yixuan Si, Lin Yang, Guojun Li, Yizhe Zhang, Liang Xiao, Tao Zhou","doi":"10.1109/TMI.2026.3728817","DOIUrl":"https://doi.org/10.1109/TMI.2026.3728817","url":null,"abstract":"Interventional radiology (IR) requires joint reasoning over procedural images and domain-specific clinical knowledge. Existing medical retrieval-augmented generation (RAG) methods are mainly text-oriented or designed for general medical vision-language tasks, and therefore remain limited in retrieving fine-grained visual-textual evidence for IR scenarios. To address this limitation, we present Prototype-guided Retrieval for Interventional Medical Assistance (PRIMA), a multimodal RAG framework that jointly leverages multimodal imaging and clinical text to support IR decision-making. PRIMA constructs a multimodal IR knowledge index through anatomy-aware visual-textual alignment and modality-preserving representation learning. It then introduces domain-informed prototype learning to organize IR concepts, enabling prototype-guided retrieval that re-ranks evidence using both query similarity and prototype affinity. We conduct comprehensive evaluations on literature-curated and clinically collected IR datasets. Experimental results show that PRIMA consistently improves generation quality, question-answering accuracy and expert-rated clinical interpretability compared with existing RAG baselines. These findings demonstrate the effectiveness of clinically grounded prototype-guided retrieval for multimodal knowledge assistance in interventional radiology. Related resources are available at https://github.com/StonHamA/PRIMA.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876806","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}

引用次数: 0

Physiology-Guided Self-Supervised Learning for Simultaneous Dual-Tracer PET Separation 同时双示踪PET分离的生理引导自监督学习。

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-06-30 DOI: 10.1109/TMI.2026.3708472

Yufei Jin;Hengjia Ran;Gaoning Ning;Xinhui Su;Min Guo;Wentao Zhu;Huafeng Liu

{"title":"Physiology-Guided Self-Supervised Learning for Simultaneous Dual-Tracer PET Separation","authors":"Yufei Jin;Hengjia Ran;Gaoning Ning;Xinhui Su;Min Guo;Wentao Zhu;Huafeng Liu","doi":"10.1109/TMI.2026.3708472","DOIUrl":"10.1109/TMI.2026.3708472","url":null,"abstract":"Simultaneous dual-tracer PET provides more comprehensive information for clinical diagnosis than standard PET imaging, but separating the hybrid dual-tracer signal remains challenging. Deep learning (DL) offers a promising solution. However, most DL methods rely on large datasets with spatiotemporal alignment between dual-tracer and two single-tracer scans. Precise alignment across different scans is difficult, making data collection costly and time-consuming, thereby limiting scalability. To avoid these difficulties and improve clinical robustness, we propose a simultaneous dual-tracer PET self-supervised separation method (DTPSS) guided by tracer kinetic prior information. By incorporating kinetic priors and integrating the ODEs residual constraints derived from the parallel two-compartment model into the loss function, DTPSS enables accurate and robust dual-tracer separation without access to single-tracer labels. Compared with data-driven supervised methods and self-supervised kinetics-aware method, DTPSS achieved improved quantitative and qualitative performance across two datasets (2,400 brain image samples with and without deformation). It also shows robustness across tracer combinations and sampling protocols. In animal study (13 rats with [18F]FDG / [18F]FET and 17 mice with [18F]FDG / [18F]FAPI), DTPSS achieved improved separation results under real-data settings with unavailable paired single-tracer labels and substantial physiological variability.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4728-4741"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364507","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}

引用次数: 0

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction EndoLRMGS：结合大重建建模和高斯飞溅完整的内镜场景重建。

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-06-25 DOI: 10.1109/TMI.2026.3707404

Xu Wang;Shuai Zhang;Baoru Huang;Jialang Xu;Danail Stoyanov;Evangelos B. Mazomenos

{"title":"EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction","authors":"Xu Wang;Shuai Zhang;Baoru Huang;Jialang Xu;Danail Stoyanov;Evangelos B. Mazomenos","doi":"10.1109/TMI.2026.3707404","DOIUrl":"10.1109/TMI.2026.3707404","url":null,"abstract":"Reconstructing dynamic surgical scenes from endoscopic videos remains a fundamental challenge in robot-assisted surgery. Existing methods primarily focus on deformable tissues, overlooking the presence of articulated instruments. To bridge this gap, we present EndoLRMGS, the first unified framework capable of reconstructing both deformable tissue and articulated instruments in a modular approach from monocular video and depth priors. We introduce Frequency-Modulated Gaussian Splatting (FMGS), for deformable tissue reconstruction, which modulates the spatial frequency of Gaussian primitives according to the Nyquist–Shannon sampling theorem, improving the visual fidelity while maintaining robust geometric accuracy. For instrument reconstruction, we leverage the Large Reconstruction Model (LRM) to generate high-quality, watertight 3D models from single images, and introduce a novel Orthographic and Perspective joint Projection Optimization (OPjPO) module to recover metric scale and spatial alignment. Extensive experiments on public datasets demonstrate the effectiveness of EndoLRMGS, achieving PSNR values from 28.4981 to 38.4179, with Chamfer distance ranging from 1.43 to 4.71 mm in tissue reconstruction. For instrument reconstruction, EndoLRMGS achieves PSNR values ranging from 19.7341 to 22.4393 on left views and 17.7442 to 20.8436 on right views. In terms of spatial alignment accuracy, it attains IoU values between 71.56% and 85.82%, with Chamfer distance ranging from 12.59 to 17.38 mm. These results highlight EndoLRMGS as a powerful and versatile solution for accurate, complete, and photorealistic 3D reconstruction of surgical scenes. Code is available at: <uri>https://github.com/MichaelWangGo/EndoLRMGS</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4678-4689"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148322677","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}

引用次数: 0

Volumetric Functional Ultrasound Imaging in Macaques 猕猴的体积功能超声成像。

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-07-06 DOI: 10.1109/TMI.2026.3710133

Nora E. Fitzgerald;Gabriel Montaldo;Mathilda Froesel;Alan Urban;Wim Vanduffel

{"title":"Volumetric Functional Ultrasound Imaging in Macaques","authors":"Nora E. Fitzgerald;Gabriel Montaldo;Mathilda Froesel;Alan Urban;Wim Vanduffel","doi":"10.1109/TMI.2026.3710133","DOIUrl":"10.1109/TMI.2026.3710133","url":null,"abstract":"Linking circuit level activity to large scale functional organization requires imaging methods combining high spatial resolution, broad coverage, and single trial sensitivity. We present volumetric functional ultrasound imaging (3D-fUS) in behaving macaques, enabling imaging of ~1<inline-formula> <tex-math>$tilde{mathbf{C}}$ </tex-math></inline-formula>m3 cortical volumes at high spatiotemporal resolution (<inline-formula> <tex-math>$100times 150times 150~mu $ </tex-math></inline-formula>m3 voxels, 1.67 Hz). Visually evoked responses were reliably detected at the level of single trials and single voxels, substantially reducing experimental time. To enable model-based analyses analogous to functional magnetic resonance imaging (fMRI), we estimated a fUS hemodynamic response function (fUS-HRF) that was consistent across subjects, cortical areas, and visual stimuli and was well approximated by a gamma function. Compared with fMRI HRFs, the fUS-HRF exhibited faster dynamics, enabling shorter and more closely spaced stimulus presentations. Together, these results establish 3D-fUS as a fast, volumetric, and circuit relevant imaging modality for efficient investigation of distributed cortical dynamics in primates.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4822-4833"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148393514","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}

引用次数: 0

Generalized Medical Phrase Grounding 广义医学短语基础。

IEEE transactions on medical imaging Pub Date : 2026-09-01 Epub Date: 2026-06-26 DOI: 10.1109/TMI.2026.3707568

Wenjun Zhang;Shekhar S. Chandra;Aaron Nicolson

{"title":"Generalized Medical Phrase Grounding","authors":"Wenjun Zhang;Shekhar S. Chandra;Aaron Nicolson","doi":"10.1109/TMI.2026.3707568","DOIUrl":"10.1109/TMI.2026.3707568","url":null,"abstract":"Medical phrase grounding (MPG) maps textual descriptions of radiological findings to corresponding image regions. These grounded reports are easier to interpret, especially for non-experts. Existing MPG systems mostly follow the referring expression comprehension (REC) paradigm and return exactly one bounding box per phrase. Real reports often violate this assumption. They contain multi-region findings, non-diagnostic text, and non-groundable phrases, such as negations or descriptions of normal anatomy. Motivated by this, we reformulate the task as generalised medical phrase grounding (GMPG), where each sentence is mapped to zero, one, or multiple scored regions. To realise this formulation, we introduce the first GMPG model: MedGrounder. We adopted a two-stage training regime: pre-training on report sentence–anatomy box alignment datasets and fine-tuning on report sentence–human annotated box datasets. Experiments on PadChest-GR and MS-CXR show that MedGrounder achieves strong zero-shot transfer, state-of-the-art overall performance on PadChest-GR, competitive results on MS-CXR, and the largest gains on multi-region phrases, while using far fewer human box annotations. Finally, we show that MedGrounder can be composed with existing report generators to produce grounded reports without retraining the generator.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4690-4702"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148341664","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}

引用次数: 0