Computerized Medical Imaging and Graphics最新文献

{"title":"Dual-state adaptive unrolled network for structure-preserving low-dose CT image restoration.","authors":"Xinyuan Zhang, Yimin Zhu, Qingzhe Cui, Jie Song, Shuo Duan, Xing Wei, Xing Wu, Quan Qian, Jianbiao Dai","doi":"10.1016/j.compmedimag.2026.102826","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2026.102826","url":null,"abstract":"<p><p>Low-dose computed tomography (LDCT) reduces radiation exposure but introduces spatially heterogeneous noise and artifacts that can obscure subtle anatomical structures. We address post-reconstruction image-domain LDCT restoration, where degradation suppression and structure preservation impose competing demands on a single restoration trajectory. We propose DSAUNet, a Dual-State Adaptive Unrolled Network that maintains persistent anatomy- and degradation-oriented latent states across unrolled stages. Rather than acting as transient feature branches, the evolving states participate in parameterizing subsequent restoration behavior through a degradation-oriented global policy coordinate for stage-level modulation and state-derived confidence variables for spatially selective regulation. Direction-aware prior modeling and multi-scale state guidance further realize this state-parameterized restoration process. Under the reported protocols, DSAUNet achieves 40.3721 dB PSNR and 0.9642 SSIM on 2DeteCT, exceeding the strongest baseline by 1.6320 dB and 0.0108, and achieves 47.4277 dB PSNR and 0.9846 SSIM under the patient-level five-fold Mayo evaluation, with a 1.0920 dB PSNR gain over the strongest PSNR baseline. On LoDoPaB-CT, it further obtains 38.5501 dB PSNR and 0.9141 SSIM, with the best PSNR, SSIM, and VIF among the compared methods. Controlled raw-state, persistent state-swapping, policy-coordinate, and confidence-map analyses support functionally differentiated state roles and active adaptive regulation. Bidirectional 2DeteCT-Mayo zero-shot experiments reveal direction-dependent transfer behavior, while unified profiling characterizes an accuracy-complexity trade-off. Together, these results support persistent dual-state controlled unrolling as a structured approach to image-domain LDCT restoration within the evaluated settings.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"136 ","pages":"102826"},"PeriodicalIF":5.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segment Anything Models for medical image segmentation: From native capabilities to clinical requirements.","authors":"Qingling Xia, Xue Sun, Bin Jiang, Yun Zhao, Diyou Chen, Shengjie Zhai","doi":"10.1016/j.compmedimag.2026.102835","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2026.102835","url":null,"abstract":"<p><p>Medical image segmentation is essential for computer-aided diagnosis and precision medicine, yet conventional supervised models depend heavily on high-quality annotations and often generalize poorly across domains. The Segment Anything Model (SAM) family offers a transferable, interactive, and extensible foundation for segmentation, but direct medical transfer remains limited by domain gaps, insufficient modeling of medical semantics and anatomy, and mismatches in high-dimensional volumetric and sequential imaging scenarios. This review critically examines the evolution and native capability boundaries of SAM, SAM 2, and SAM 3. Across generations, native capabilities expand from spatial prompting to sequential propagation and concept-level target specification, without automatically conferring medical expertise. We organize medical adaptation from three connected perspectives: model adaptation and strategy selection, task knowledge organization and utilization, and application-oriented adaptation. Cross-method analysis shows that no strategy is universally optimal; effectiveness depends on the gap between foundation-model capabilities and target-task requirements, together with data scale, domain discrepancy, imaging dimensionality, computational resources, and clinical constraints. We further identify four priorities for clinical translation: standardized evaluation for cross-study comparability, controlled continual adaptation under clinical domain drift, dynamic resource scheduling for high-dimensional and real-time workflows, and uncertainty stratification and clinical reliability control. Overall, further progress depends on accurate capability assessment, condition-matched adaptation, and explicit control of computational and prediction risks.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"136 ","pages":"102835"},"PeriodicalIF":5.5,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of Kolmogorov-Arnold networks for medical image analysis.","authors":"Jieyun Bai, Zhongguo Cai, Rongshun Yang, Karim Lekadir, Shuo Li","doi":"10.1016/j.compmedimag.2026.102827","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2026.102827","url":null,"abstract":"<p><p>Kolmogorov-Arnold Networks (KANs) replace selected scalar weights or fixed activations with learnable univariate edge functions. We conducted a controlled retrospective evidence reconstruction and update of Crossref, Europe PMC, arXiv, and OpenAlex, supplemented by local full texts and citation tracking, for studies published from January 2024 to February 2026. After report-level screening and version consolidation, 138 independent medical-imaging studies met explicitly defined medical-imaging eligibility criteria (127 direct imaging inputs; 11 image-derived representations). Studies were classified by primary KAN placement - backbone, bottleneck/token, physics-informed or implicit representation, or task-specific head - and evaluated for task, modality, comparator, reported result, and evidence of a KAN-specific contribution. None met the strictest contribution-evidence standard of a complete resource-controlled factorial comparison; 48 provided moderate, 46 weak/compound, and 44 insufficient evidence for the independent KAN contribution. Whole-model results were favorable in 68 studies, mixed or trade-off in 30, unfavorable or without incremental gain in 4, and indeterminate or not comparable in 36; these labels do not isolate the effect of KAN. External validation, calibration, repeated-run stability, and deployment metrics were inconsistently reported. The evidence supports KAN chiefly as a task-dependent module for controlled testing within established convolutional, Transformer, state-space, or physics-informed pipelines, rather than as a generally superior replacement. Priorities include matched ablations, function-stability analysis, external-center evaluation, and standardized resource reporting.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"136 ","pages":"102827"},"PeriodicalIF":5.5,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Vision Foundation Models: A comprehensive review from technical evolution to clinical translation.","authors":"Nengjian Lin, Yi Ding, Zhen Qin, Mingsheng Cao, Jiyuan Zhang, Yuchao Xia, Yuhao Zhong, Xiaoyong Zhang","doi":"10.1016/j.compmedimag.2026.102832","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2026.102832","url":null,"abstract":"<p><p>Medical Vision Foundation Models (MVFMs) mark a shift from task-specific artificial intelligence (AI) toward general-purpose visual intelligence in medical imaging. However, clinical deployment remains constrained by tensions among data availability, technological development, and integration into clinical workflows. To address these issues, this review provides a systematic analysis of the field's evolution from 2019 to 2025. We introduce the Data-Technology-Translation Triad as a unifying conceptual framework to structure the progress, key factors, and challenges of MVFMs. Our analysis traces the field's development and identifies a convergence toward Transformer-based and hybrid architectures, alongside hybrid pre-training paradigms. We present a unified technical framework that covers pre-training strategies, including generative, contrastive, and hybrid learning, as well as adaptation mechanisms such as full fine-tuning, parameter-efficient fine-tuning, and prompt learning. Furthermore, we examine how data scarcity, heterogeneity, and privacy constraints in medical imaging drive the adoption of self-supervised learning and highlight domain generalization as a critical research direction. Although MVFMs demonstrate strong performance in data-efficient and multi-task settings, their clinical translation is hindered by limited generalization, insufficient interpretability, misalignment with clinical workflows, and regulatory uncertainty. In response, we propose practical directions for developing reliable general-purpose medical vision systems, with emphasis on causal reasoning, dynamic data learning, interactive human-AI collaboration, and coordinated efforts among researchers, clinicians, and regulators. This review provides a structured reference to guide MVFMs from technical validation to clinically effective, reliable, and ethically sound deployment. An updated collection of MVFM papers and related resources is available at our website https://github.com/ice-TuoShui/MVFM-Review.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"136 ","pages":"102832"},"PeriodicalIF":5.5,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting transformer-based cardiac MRI segmentation with text-to-image controllable diffusion pipelines","authors":"H. Fouadi,&nbsp;M. Kas,&nbsp;Y. Ruichek,&nbsp;Y. El Merabet","doi":"10.1016/j.compmedimag.2026.102803","DOIUrl":"10.1016/j.compmedimag.2026.102803","url":null,"abstract":"<div><div>Cardiac magnetic resonance imaging (MRI) plays a critical role in diagnosing cardiovascular diseases; however, acquiring large, annotated datasets remains a significant challenge due to ethical, economic, and logistical constraints. In this paper, we propose a generative framework based on diffusion models for the controllable synthesis of anatomically consistent cardiac MRI scans with corresponding segmentation labels. Our approach combines Low-Rank Adaptation (LoRA) to generate pathology-aware label maps from textual prompts, and ControlNet to guide image synthesis using both semantic and spatial conditioning. This enables the creation of a fully annotated synthetic dataset aligned with cardiac pathologies and phases, supporting supervised training of segmentation models without additional manual labeling. We evaluate the proposed framework in terms of image realism using Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Fréchet Radiomic Distance (FRD), as well as downstream segmentation performance using a SegFormer model trained on real, synthetic, and combined datasets. Results show that the proposed method improves segmentation accuracy, particularly in data-limited settings. Beyond in-domain evaluation on the ACDC dataset, cross-dataset experiments on the multi-center M&amp;Ms cohort demonstrate improved generalization and robustness to domain shifts. These findings highlight the potential of text-guided diffusion pipelines to generate high-quality, semantically consistent medical imaging data for robust and generalizable AI training.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102803"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for multimodal brain tumor segmentation: Architectures, fusion, robust learning, and deployment perspectives","authors":"Yi Zhou,&nbsp;Jeevan Kanesan,&nbsp;Chee-Onn Chow,&nbsp;Chengbao Xu","doi":"10.1016/j.compmedimag.2026.102807","DOIUrl":"10.1016/j.compmedimag.2026.102807","url":null,"abstract":"<div><div>Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, radiotherapy targeting, and longitudinal assessment. Deep learning has advanced this task through convolutional neural networks, Transformers, state-space models, diffusion methods, and foundation models. However, strong benchmark performance does not guarantee clinical reliability because systems remain vulnerable to missing or degraded modalities, cross-center shift, and subregion-specific failures, particularly in enhancing tumor (ET) and tumor core (TC). This survey critically reviews deep learning for multimodal brain tumor segmentation from a deployment-oriented, failure-focused perspective. We introduce a unifying framework linking input reliability, fusion–architecture co-design, subregion-specific failure mechanisms, and uncertainty-aware clinical triage. Using this framework, we compare major architectural paradigms in contextual modeling, boundary preservation, computational feasibility, and robustness across ET, TC, and whole tumor (WT). We reinterpret multimodal fusion as a reliability-allocation problem and examine early, intermediate, token-level, sequence-aware, and adaptive strategies under incomplete or degraded inputs. We also synthesize robust learning approaches for sparse supervision, MRI quality degradation, cross-center variation, and test-time adaptation, and assess interpretability, uncertainty estimation, and human-in-the-loop review as mechanisms for clinical risk control. Finally, our evidence-oriented benchmarking analysis identifies WT performance saturation, persistent ET/TC instability, inconsistent boundary-metric reporting, and insufficient stress testing, center-stratified evaluation, calibration assessment, and computational transparency. We conclude that progress should be judged not only by benchmark accuracy but also by subregion-level reliability under realistic deployment conditions.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102807"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RulerNet: Learning perspective-invariant ruler representations for robust image scale estimation","authors":"Yimu Pan,&nbsp;Manas Mehta,&nbsp;Gwen Sincerbeaux,&nbsp;Jeffery A. Goldstein,&nbsp;Alison D. Gernand,&nbsp;James Z. Wang","doi":"10.1016/j.compmedimag.2026.102805","DOIUrl":"10.1016/j.compmedimag.2026.102805","url":null,"abstract":"<div><div>Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision, limiting progress in applications such as biomedicine, forensics, nutritional analysis, and e-commerce. We introduce <strong>RulerNet</strong>, a deep learning framework that robustly infers scale in the wild by reformulating ruler reading as a unified keypoint detection problem and representing rulers with geometric progression parameters that compactly approximate the non-uniform spacing induced by perspective transformations. Unlike traditional methods that rely on handcrafted thresholds or rigid, ruler-specific pipelines, RulerNet directly localizes centimeter markings using a mark-visibility-based annotation and training strategy that remains valid under mark-preserving transformations, enabling strong generalization across diverse ruler types and imaging conditions while mitigating data scarcity. Additionally, we introduce a scalable synthetic data generation pipeline that combines graphics-based ruler creation with ControlNet-enhanced realism, significantly expanding training diversity and improving model performance. Extensive experiments on both our datasets and public benchmarks demonstrate that RulerNet achieves accurate, consistent, and efficient scale estimation under challenging real-world conditions. Integration into a medical analysis pipeline further demonstrates its practical utility for scale-aware measurement. These results suggest that RulerNet can serve as a generalizable measurement component and be readily integrated with other vision modules, such as segmentation, monocular depth estimation, and diagnostic workflows, for automated analysis in medical and other domains. An online demo is provided. The code and data are publicly released with the paper.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102805"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148724483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric decoupled dual-domain learning for CT metal artifact reduction: Integrating Fast Fourier Convolution and vision transformers","authors":"Minghe Yang,&nbsp;Jia Li","doi":"10.1016/j.compmedimag.2026.102801","DOIUrl":"10.1016/j.compmedimag.2026.102801","url":null,"abstract":"<div><div>Computed Tomography (CT) imaging is frequently compromised by metallic implants, which introduce severe streaking and shading artifacts that obscure anatomical details. While dual-domain networks have advanced Metal Artifact Reduction (MAR), they are limited by their reliance on architecturally homogeneous frameworks. This design ignores the cross-domain asymmetry in artifact manifestation: metal implants appear as broad geometric discontinuities in the sinogram domain but propagate as non-local streaks in the image domain. To bridge this topological discrepancy, we propose an Asymmetric Decoupled Dual-Domain Framework. First, we introduce a Spectral–Geometric Inpainting Module (SGIM) utilizing Fast Fourier Convolution to exploit global spectral consistency for reconstructing missing projections within large gaps. Second, an Anatomical Texture Refinement Module (ATRM) based on Vision Transformers is designed to capture long-range pixel dependencies and mitigate residual streaks. A Residual-Prior Guided Fusion strategy integrates geometrically corrected structural priors from the projection space into the image space. Extensive experiments on the Synthesized DeepLesion dataset demonstrate that our approach outperforms state-of-the-art baselines, achieving a Peak Signal-to-Noise Ratio (PSNR) of 46.49 dB and Structural Similarity (SSIM) of 0.9972. Additionally, the proposed framework exhibits superior generalization on clinical datasets, strong robustness against mask segmentation errors, and high computational efficiency suitable for clinical deployment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102801"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-domain latent alignment for MRI-to-CT synthesis via target-aware manifold learning","authors":"Wenzhe Zheng,&nbsp;Yuping Sun,&nbsp;Si Li,&nbsp;Yi Liu,&nbsp;Yiheng Tan,&nbsp;Shun Yao","doi":"10.1016/j.compmedimag.2026.102804","DOIUrl":"10.1016/j.compmedimag.2026.102804","url":null,"abstract":"<div><div>Computed tomography (CT) is essential for clinical diagnosis and radiotherapy planning but involves ionizing radiation, motivating the synthesis of CT images from magnetic resonance imaging (MRI). Most existing MRI-to-CT synthesis methods rely on fully paired supervision, which is difficult to scale in practice and restricts the availability of diverse training data. While unpaired CT samples can provide additional anatomical priors, current approaches typically exploit them as generic style references, lacking explicit mechanisms to preserve anatomical geometry. In this work, we propose DELA-Net, a Dual-Encoder Latent Alignment Network that reformulates MRI-to-CT synthesis as a target-aware latent manifold alignment problem. DELA-Net introduces a geometric reference selection strategy to identify informative unpaired CT samples as latent anchors, enabling structured alignment between paired and unpaired domains. The optimization is decomposed into two coupled alignment tasks — MRI-to-unpaired CT and paired-to-unpaired CT — facilitated by a latent space modulator that adapts MRI representations to the CT manifold while maintaining structural fidelity. Experiments on three benchmark datasets provide promising evidence that DELA-Net achieves a favorable balance across synthesis quality, cross-modality task translation, and bone-related structural preservation in downstream task-driven evaluation. The source code is publicly available at: <span><span>https://github.com/kennysyp/DELA-Net</span><svg><path></path></svg></span>. The GitHub repository is now publicly accessible.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102804"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A diffusion-based generative prior approach to sparse-view computed tomography","authors":"Davide Evangelista,&nbsp;Pasquale Cascarano,&nbsp;Elena Loli Piccolomini","doi":"10.1016/j.compmedimag.2026.102802","DOIUrl":"10.1016/j.compmedimag.2026.102802","url":null,"abstract":"<div><div>The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of diffusion-based generative models is combined with an iterative optimization algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"134 ","pages":"Article 102802"},"PeriodicalIF":5.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}