IEEE transactions on medical imaging最新文献

{"title":"SynReEM: Synapse Reconstruction via Instance Structure Encoding in Anisotropic Electron Microscopic Volumes","authors":"Jinyue Guo;Yanchao Zhang;Hao Zhai;Yi Jiang;Qi Zhang;Yunfeng Hua;Jing Liu;Hua Han","doi":"10.1109/TMI.2026.3706567","DOIUrl":"10.1109/TMI.2026.3706567","url":null,"abstract":"Volume electron microscopy (vEM) has revolutionized the nanoscale reconstruction of synapses in neural circuits. However, large-scale vEM techniques relying on serial sectioning suffer from severe anisotropy, where axial resolution is far worse than lateral resolution. This anisotropic imaging induces discontinuities in biological architectures across 3D space, compromising reconstruction accuracy and instance segmentation of synapses. Although synapse reconstruction can be realized via aggregation of segmented voxels or detected superpixels, conventional semantic and instance-level models fail to learn voxel instance attributes robustly from strong anisotropic datasets. Here, we present SynReEM, a dedicated framework for synapse reconstruction. Specifically, we first conduct structural encoding on synapse annotations to optimize structural components, making instance segmentation feasible within a semantic context. Then, we incorporate biological priors to impose continuity and inclusion constraints on model outputs, leveraging online pseudo-labels to enhance model convergence. Furthermore, we design a dual-headed branch for simultaneous semantic and instance decoding from shared feature maps, fuse the multi-task outputs, and adopt the watershed algorithm to achieve accurate instance reconstruction. Comprehensive evaluations on three vEM datasets containing synapses (Synapse178, AC3/AC4, and SynWTAD) consistently confirm the superior performance of our proposed SynReEM method.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4662-4677"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304878","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":"From Slice to Sequence: Autoregressive Tracking Transformer for Consistent 3-D Lymph Node Detection in CT Scans","authors":"Qinji Yu;Yirui Wang;Ke Yan;Dandan Zheng;Dashan Ai;Dazhou Guo;Zhanghexuan Ji;Yanzhou Su;Yun Bian;Xiaowei Ding;Yi Guo;Kuaile Zhao;Le Lu;Na Shen;Xianghua Ye;Dakai Jin","doi":"10.1109/TMI.2026.3701886","DOIUrl":"10.1109/TMI.2026.3701886","url":null,"abstract":"Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging and treatment planning. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate the effectiveness of 2.5D approaches (i.e., using 2D backbone with multi-slice inputs), leveraging pretrained 2D model weights and showing improved accuracy as compared to separate 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances, which can involve tuning a set of parameters for each dataset. In this work, we formulate 3D LN detection as a slice-by-slice tracking task along the z-axis and propose LN-Tracker, a novel LN tracking transformer, for joint end-to-end detection and 3D instance association. Built upon a DETR-based detector, LN-Tracker decouples transformer queries into distinct track and detection groups with independent matching, enabling comprehensive LN detection while maintaining trajectory consistency. A masked attention mechanism further separates learning between these query groups, and a similarity loss promotes robust inter-slice LN association, particularly in low-contrast scenarios. Extensive evaluation on four LN datasets shows LN-Tracker’s superior performance, with at least <inline-formula> <tex-math>${2}.{49}%$ </tex-math></inline-formula> gain in average sensitivity when compared to top 3D/2.5D/tracking detectors. Further validation on public lung nodule and prostate tumor detection tasks confirms the generalizability of LN-Tracker as it achieves top performance on both tasks. Code is available at <uri>https://github.com/alibaba-damo-academy/LN-Tracker</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4649-4661"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213898","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":"Polar Subarea-Aware Fusion Net for Posterior Eyeball Shape Reconstruction","authors":"Jiaqi Zhang;Xiuzhe Wu;Jiahui Liu;Chunyu Zou;Yan Hu;Fengze Nie;Zicheng Sun;Xiaojuan Qi;Jiang Liu","doi":"10.1109/TMI.2025.3642381","DOIUrl":"10.1109/TMI.2025.3642381","url":null,"abstract":"High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules—Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)—to enhance positional awareness, integrate local–global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at <uri>https://github.com/HKUZJ77/PSAFNet</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4637-4648"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728474","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":"PIPA: Prior-Driven Prompting With Diagnosis-Oriented Retrieval-Augmentation for 3-D Radiology Report Generation","authors":"Qiushi Yang;Wuyang Li;Xiaoqing Guo;Maymay Cerys Harwood;Peter Y. M. Woo;Jingyang Zhang;Yang Chen;Ke Zhang;Yixuan Yuan","doi":"10.1109/TMI.2026.3710717","DOIUrl":"10.1109/TMI.2026.3710717","url":null,"abstract":"Automatic radiology report generation has gained increasing attention for its potential to assist in clinical reporting and reduce the workload of radiologists. Existing 3D radiology report generation methods employ multi-modal foundation model to encode volume-text inputs and produce diagnosis reports, while they ignore the characteristics of 3D volumes including much background regions and suffer from generating hallucinations, especially in medical domain that contains many uncommon professional terms. In this paper, we aim to efficiently adapt the pre-trained foundation model to specific 3D radiology report generation, and present a Prior-drIven Prompting with diagnosis-oriented retrieval-Augmentation (PIPA) framework. In PIPA, we design a Prior-drIven Prompting (PIP) strategy to exploit diagnostic knowledge from input volumes and a Diagnosis-oriented volume-report retrieval-augmentation Generation (DIG) module to explore beneficial knowledge from external database. Specifically, in PIP, to take full advantage of the patient’s clinical information, e.g., age and symptoms, and the possible disease information, e.g., brain tumor, edema, we formulate them as the patient and disease priors to mine clinical relevant knowledge. Furthermore, we propose utilizing visual and textual embeddings as queries to retrieve similar external data by devising a diagnosis-oriented retrieval-augmentation scheme for leveraging more report resources as references for LLM to produce accuracy outcomes. With PIP and DIG, PIPA integrates clinical priors and external data to learn effective diagnostic representations for high-quality report generation. We evaluate the framework on both public and in-house 3D medical datasets with corresponding reports, demonstrating its strong performance in generating accurate diagnosis reports. Source codes have been published at <uri>https://github.com/CUHK-AIM-Group/PIPA/tree/main</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4834-4845"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400555","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":"BrainCL: Transformer-Based Brain Network Contrastive Learning With Multi-Order Topology and Salience Masking","authors":"Yongliang Zhang;Haochen Qian;Jinbo Yang;Fangfang Chen;Xi-Jian Dai;Kaiyu Fan;Li Xiao;Yu-Ping Wang","doi":"10.1109/TMI.2026.3709646","DOIUrl":"10.1109/TMI.2026.3709646","url":null,"abstract":"Brain network analysis based on functional magnetic resonance imaging (fMRI) is crucial for the diagnosis of neurological disorders. Recently, Transformers have been adopted for brain network analysis to mitigate the over-smoothing issue in GNNs. However, they often fail to account for complex topological properties of brain networks and tend to rely on a limited set of regions of interest (ROIs) for neurological disorder prediction. This makes these models highly sensitive to site differences and inter-subject variability, leading to suboptimal performance. In this paper, we propose a novel brain network contrastive learning framework (BrainCL) to enhance Transformer-based brain network analysis. Specifically, we first design a multi-order topology-aware Transformer (MoTFormer) that leverages a preferential random walk scheme (PRWS) and a hop-wise gated attention (HWGA) module to adaptively introduce multi-order topological inductive biases, thereby capturing informative multi-hop functional interactions. In addition, to overcome the limitation of relying on a few ROIs, we propose a salience-informed dynamic masking strategy that deliberately occludes salient ROIs to encourage MoTFormer to continuously mine subtle yet critical functional abnormalities from relatively unactivated ROIs for complementary learning, thereby generating more comprehensive representations. Finally, we develop synergistic dual-level contrastive learning to promote semantically meaningful representation invariance and construct a class-discriminative feature space, further improving model robustness. Experimental results demonstrate that BrainCL significantly outperforms existing methods and achieves excellent cross-site generalization. Furthermore, visualization results show that BrainCL can leverage information from a broader set of ROIs, which may offer a novel perspective for exploring more diverse biomarkers in future research.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4784-4796"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371982","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":"MUST: Multi-Style Virtual Staining With Incomplete Pairs","authors":"Jiaxin Zhuang;Yao Du;Xiaoyu Zheng;Linshan Wu;Chao He;Lin Luo;Hao Chen","doi":"10.1109/TMI.2026.3709810","DOIUrl":"10.1109/TMI.2026.3709810","url":null,"abstract":"Multi-style virtual staining transforms histological images into multiple staining modalities, offering significant clinical value at reduced cost and time. However, a critical challenge impeding clinical adoption is incompletely paired training data—an inevitable consequence of tissue degradation and processing artifacts during sequential staining. Current methods assume perfectly paired datasets, severely limiting their clinical utility. We address this problem by introducing MUST (MUlti-style virtual STaining), which reformulates virtual staining as progressive cross-modality refinement under incomplete supervision. Our approach comprises two synergistic components: 1) Collaborative Denoising (CoDe) that uses cross-modality cross attention to condition a latent diffusion model, enabling effective information exchange across modalities with incomplete supervision, and 2) Semantic Preservation (SP) that further maintains cross-modal consistency through contrastive learning while generating reliable pseudo-supervision from confident model predictions in samples without ground truth. Extensive experiments across three histopathology datasets demonstrate that MUST significantly outperforms state-of-the-art methods, effectively mining cross-modality correlations while generating high-confidence pseudo-supervision from incomplete data. Code and trained models will be publicly released upon publication at <uri>https://github.com/JiaxinZhuang/MUST</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4797-4808"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148379857","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":"NGSE-Corr: A Technique for Objective Clinical Evaluation of Quantitative-Imaging Methods Without a Gold Standard","authors":"Yan Liu;Ziping Liu;Zekun Li;Jingqin Luo;Daniel L. J. Thorek;Barry A. Siegel;Abhinav K. Jha","doi":"10.1109/TMI.2026.3707743","DOIUrl":"10.1109/TMI.2026.3707743","url":null,"abstract":"Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates QI methods without true values. The technique assumes a linear stochastic relationship between true and measured values, characterized by a slope, bias, and multivariate Gaussian-distributed noise term that models correlated noise across QI methods. We derive a maximum-likelihood approach to estimate these parameters using only measured values. From the estimates, we compute noise-to-slope ratio (NSR) to rank QI methods based on precision. Numerical experiments showed that NGSE-Corr reliably estimated the NSR, accurately ranked methods, and maintained performance even when assumptions made by the technique were partially violated. We also validated NGSE-Corr in an <italic>in silico</i> imaging trial to rank three quantitative SPECT methods for measuring regional activity uptake in patients with bone metastatic castrate-resistant prostate cancer treated with radium-223. NGSE-Corr correctly identified the most precise QI method and ranked the methods for 95% (95% CI, 89%–98%) and 91% (95% CI, 84%–95%) of trials, respectively, with data from 50 patients. Performance further improved with larger cohorts. With 200 patients, NGSE-Corr yielded same rankings as those obtained with true values across all trial instances. These findings demonstrate the ability of NGSE-Corr to accurately rank QI methods without gold standards and motivate clinical validation and broader applications.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4703-4713"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432024","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":"Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT","authors":"Guoxi Zhu;Li Zhang;Zhiqiang Chen;Hewei Gao","doi":"10.1109/TMI.2026.3708964","DOIUrl":"10.1109/TMI.2026.3708964","url":null,"abstract":"X-ray scatter has been a serious concern in computed tomography (CT), leading to image artifacts and distortion of CT values. The linear Boltzmann transport equation (LBTE) is recognized as a fast and accurate approach for scatter estimation. However, for multi-spectral CT, it is cumbersome to compute multiple scattering components for different spectra separately when applying LBTE-based scatter correction. In this work, we propose a Matrixed-Spectrum Decomposition accelerated LBTE solver (MSD-LBTE) that can be used to compute X-ray scatter distributions from CT acquisitions at two or more different spectra simultaneously, in a unified framework with no sacrifice in accuracy and significant reduction in computation compared with conventional solvers in theory. First, a matrixed-spectrum solver of the LBTE is obtained by introducing an additional label dimension to expand the phase space. Then, we propose a “spectrum basis” for the LBTE and a principle of selection of basis using the QR decomposition, along with the above solver to construct the MSD-LBTE. Based on MSD-LBTE, a unified scatter correction method can be established for multi-spectral CT. We validate the effectiveness and accuracy of our method by comparing it with the Monte Carlo method. We also evaluate the scatter correction performance using two different phantoms for kV-switching dual-energy spectral CT, and using an elliptical phantom in a numerical simulation for kV-modulation enabled CT scans, validating that our proposed method can significantly reduce the computational cost at multiple spectra and effectively reduce scatter artifact in reconstructed CT images.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4742-4756"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371534","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":"DiffGeo-AOR: Diffusion-Optimized Medical Grading via Geometric Priors Enhanced Autoregressive Ordinal Regression","authors":"Qinkai Yu;He Zhao;Yanyu Xu;Meng Wang;Yitian Zhao;Huazhu Fu;Xujiong Ye;Aline Villavicencio;Gregory Y. H. Lip;Yalin Zheng;Yanda Meng","doi":"10.1109/TMI.2026.3710844","DOIUrl":"10.1109/TMI.2026.3710844","url":null,"abstract":"Ordinal regression is well-known for leveraging the underlying inherent order between successive categories to obtain additional regularization beyond traditional probabilistic classification mechanism. However, there are challenges in real-world medical grading tasks: 1) The uneven distribution of disease severity levels, characterized by a long-tailed format, complicates the ordinal regression process. 2)The ambiguity in establishing disease severity thresholds introduces substantial challenges, rendering the ordinal regression framework susceptible to inter-class inconsistencies. To address the challenge, this work proposes <italic>DiffGeo-AOR</i>, by introducing an autoregressive process to ordinal regression that operates directly on continuous global features, without any need for vector quantization. <italic>DiffGeo-AOR</i> decomposes a <inline-formula> <tex-math>$K$ </tex-math></inline-formula>-class ordinal problem into <inline-formula> <tex-math>${K}{-}{1}$ </tex-math></inline-formula> conditional binary decision steps, enabling the model to explicitly infer whether the severity has crossed the next grade threshold at each step. We also introduces parameterized diffusion optimization to model conditional probability distributions, allowing continuous global features to be extracted and directly leveraged in the autoregressive process. In addition, we design a <italic>FiLM</i>-gated Step-Aware Diffusion Conditioning Fusion that guides each step’s decision based on both the current image representation and the previous soft prediction probabilities. Furthermore, we regularize the feature space with rank-anchored ordinal priors during training to facilitate stable convergence of the autoregressive module. <italic>DiffGeo-AOR</i> consistently outperforms current state-of-the-art ordinal regression methods across both 2D and 3D medical grading tasks on three large-scale datasets. The implementation code is publicly available at <uri>https://github.com/Qinkaiyu/DiffGeo-AOR</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4846-4860"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400557","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":"Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning","authors":"Anbang Wang;Ming Lei;Heye Zhang;Zhifan Gao;Qi Zhang;Zhihui Zhang;Ping Zhu;Dan Deng;Lingyun Zu;Guang Yang;Xiujian Liu","doi":"10.1109/TMI.2026.3707748","DOIUrl":"10.1109/TMI.2026.3707748","url":null,"abstract":"Virtual coronary intervention planning (VCIP) aims to optimize the hemodynamic outcomes of percutaneous coronary intervention (PCI) in patients with coronary stenosis. However, its clinical adoption remains constrained by the computational burden associated with evaluating numerous combinatorial intervention strategies, leading to time-consuming workflows and potentially suboptimal decisions in the catheterization laboratory. While conventional deep reinforcement learning (DRL) offers a path to automated VCIP, it often explores state–action–reward space inefficiently. In this study, we propose an Informed-Exploration Reinforcement Learning framework that concentrates the search on clinically meaningful interventions by integrating historical intervention experience with patient-specific anatomical and physiological information to guide the generation of functionally informed stent strategies. Extensive experiments on 172 vessels from 146 patients show that IERL achieves high agreement (r = 0.815) with real interventions and excellent computational efficiency with an average run time of 2.1 seconds. By aligning exploration with both prior experience and patient context, IERL provides objective, reproducible, and near-real-time VCIP decision support, enabling timely and interpretable recommendations compatible with catheterization workflows. The code and models are available at: <uri>https://github.com/HIC-SYSU/IERL/tree/main</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 9","pages":"4714-4727"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148335904","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}