IEEE transactions on medical imaging最新文献

Ultrasound Autofocusing: Common Midpoint Phase Error Optimization via Differentiable Beamforming. 超声自动聚焦：基于可微波束形成的常见中点相位误差优化。

IEEE transactions on medical imaging Pub Date : 2025-09-09 DOI: 10.1109/TMI.2025.3607875

Walter Simson, Louise Zhuang, Benjamin N Frey, Sergio J Sanabria, Jeremy J Dahl, Dongwoon Hyun

{"title":"Ultrasound Autofocusing: Common Midpoint Phase Error Optimization via Differentiable Beamforming.","authors":"Walter Simson, Louise Zhuang, Benjamin N Frey, Sergio J Sanabria, Jeremy J Dahl, Dongwoon Hyun","doi":"10.1109/TMI.2025.3607875","DOIUrl":"10.1109/TMI.2025.3607875","url":null,"abstract":"In ultrasound imaging, propagation of an acoustic wavefront through heterogeneous media causes phase aberrations that degrade the coherence of the reflected wavefront, leading to reduced image resolution and contrast. Adaptive imaging techniques attempt to correct this phase aberration and restore coherence, leading to improved focusing of the image. We propose an autofocusing paradigm for aberration correction in ultrasound imaging by fitting an acoustic velocity field to pressure measurements, via optimization of the common midpoint phase error (CMPE), using a straight-ray wave propagation model for beamforming in diffusely scattering media. We show that CMPE induced by heterogeneous acoustic velocity is a robust measure of phase aberration that can be used for acoustic autofocusing. CMPE is optimized iteratively using a differentiable beamforming approach to simultaneously improve the image focus while estimating the acoustic velocity field of the interrogated medium. The approach relies solely on wavefield measurements using a straight-ray integral solution of the two-way time-of-flight without explicit numerical time-stepping models of wave propagation. We demonstrate method performance through in silico simulations, in vitro phantom measurements, and in vivo mammalian models, showing practical applications in distributed aberration quantification, correction, and velocity estimation for medical ultrasound autofocusing.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031617","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

LGFFM: A Localized and Globalized Frequency Fusion Model for Ultrasound Image Segmentation. LGFFM：一种局部和全球化的超声图像分割频率融合模型。

IEEE transactions on medical imaging Pub Date : 2025-08-19 DOI: 10.1109/TMI.2025.3600327

Xiling Luo, Yi Wang, Le Ou-Yang

{"title":"LGFFM: A Localized and Globalized Frequency Fusion Model for Ultrasound Image Segmentation.","authors":"Xiling Luo, Yi Wang, Le Ou-Yang","doi":"10.1109/TMI.2025.3600327","DOIUrl":"10.1109/TMI.2025.3600327","url":null,"abstract":"Accurate segmentation of ultrasound images plays a critical role in disease screening and diagnosis. Recently, neural network-based methods have garnered significant attention for their potential in improving ultrasound image segmentation. However, these methods still face significant challenges, primarily due to inherent issues in ultrasound images, such as low resolution, speckle noise, and artifacts. Additionally, ultrasound image segmentation encompasses a wide range of scenarios, including organ segmentation (e.g., cardiac and fetal head) and lesion segmentation (e.g., breast cancer and thyroid nodules), making the task highly diverse and complex. Existing methods are often designed for specific segmentation scenarios, which limits their flexibility and ability to meet the diverse needs across various scenarios. To address these challenges, we propose a novel Localized and Globalized Frequency Fusion Model (LGFFM) for ultrasound image segmentation. Specifically, we first design a Parallel Bi-Encoder (PBE) architecture that integrates Local Feature Blocks (LFB) and Global Feature Blocks (GLB) to enhance feature extraction. Additionally, we introduce a Frequency Domain Mapping Module (FDMM) to capture texture information, particularly high-frequency details such as edges. Finally, a Multi-Domain Fusion (MDF) method is developed to effectively integrate features across different domains. We conduct extensive experiments on eight representative public ultrasound datasets across four different types. The results demonstrate that LGFFM outperforms current state-of-the-art methods in both segmentation accuracy and generalization performance.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884639","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

EndoRD-GS: Robust Deformable Endoscopic Scene Reconstruction via Gaussian Splatting. 基于高斯飞溅的鲁棒可变形内窥镜场景重建。

IEEE transactions on medical imaging Pub Date : 2025-08-19 DOI: 10.1109/TMI.2025.3600253

Bingchen Gao, Jun Zhou, Jing Zou, Jing Qin

{"title":"EndoRD-GS: Robust Deformable Endoscopic Scene Reconstruction via Gaussian Splatting.","authors":"Bingchen Gao, Jun Zhou, Jing Zou, Jing Qin","doi":"10.1109/TMI.2025.3600253","DOIUrl":"10.1109/TMI.2025.3600253","url":null,"abstract":"Real-time and realistic reconstruction of 3D dynamic surgical scenes from surgical videos is a novel and unique tool for surgical planning and intraoperative guidance. The 3D Gaussian splatting (GS), with its high rendering speed and reconstruction fidelity, has recently emerged as a promising technique for surgical scene reconstruction. However, existing GS-based methods still have two obvious shortcomings for realistic reconstruction. First, they largely struggle to capture localized yet intricate soft tissue deformations caused by complex instrument-tissue interactions. Second, they fail to model spatiotemporal coupling among Gaussian primitives for global adjustments during rapid perspective transformations, resulting in unstable reconstruction outputs. In this paper, we propose EndoRD-GS, an innovative approach that overcomes these two limitations through two core techniques: (1) periodic modulated Gaussian functions and (2) a new Biplane module. Specifically, our periodic modulated Gaussian functions incorporate meticulously designed modulations, significantly enhancing the representation of complex local tissue deformations. On the other hand, our Biplane module constructs spatiotemporal interactions among Gaussian primitives, enabling global adjustments and ensuring reliable scene reconstruction during rapid perspective transformations. Extensive experiments on three datasets demonstrate that our EndoRD-GS achieves superior performance in endoscopic scene reconstruction compared to state-of-the-art methods. The code is available at EndoRD-GS.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884638","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

Adaptive Sequential Bayesian Iterative Learning for Myocardial Motion Estimation on Cardiac Image Sequences. 基于自适应序列贝叶斯迭代学习的心脏图像序列心肌运动估计。

IEEE transactions on medical imaging Pub Date : 2025-08-18 DOI: 10.1109/TMI.2025.3599487

Shuxin Zhuang, Heye Zhang, Dong Liang, Hui Liu, Zhifan Gao

{"title":"Adaptive Sequential Bayesian Iterative Learning for Myocardial Motion Estimation on Cardiac Image Sequences.","authors":"Shuxin Zhuang, Heye Zhang, Dong Liang, Hui Liu, Zhifan Gao","doi":"10.1109/TMI.2025.3599487","DOIUrl":"10.1109/TMI.2025.3599487","url":null,"abstract":"Motion estimation of left ventricle myocardium on the cardiac image sequence is crucial for assessing cardiac function. However, the intensity variation of cardiac image sequences brings the challenge of uncertain interference to myocardial motion estimation. Such imaging-related uncertain interference appears in different cardiac imaging modalities. We propose adaptive sequential Bayesian iterative learning to overcome the challenge. Specifically, our method applies the adaptive structural inference to state transition and observation to cope with a complex myocardial motion under uncertain setting. In state transition, adaptive structural inference establishes a hierarchical structure recurrence to obtain the complex latent representation of cardiac image sequences. In state observation, the adaptive structural inference forms a chain structure mapping to correlate the latent representation of the cardiac image sequence with that of the motion. Extensive experiments on US, CMR, and TMR datasets concerning 1270 patients (650 patients for CMR, 500 patients for US and 120 patients for TMR) have shown the effectiveness of our method, as well as the superiority to eight state-of-the-art motion estimation methods.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877629","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

Hierarchical Contrastive Learning for Precise Whole-body Anatomical Localization in PET/CT Imaging. 分层对比学习在PET/CT成像中的精确全身解剖定位。

IEEE transactions on medical imaging Pub Date : 2025-08-18 DOI: 10.1109/TMI.2025.3599197

Yaozong Gao, Yiran Shu, Mingyang Yu, Yanbo Chen, Jingyu Liu, Shaonan Zhong, Weifang Zhang, Yiqiang Zhan, Xiang Sean Zhou, Xinlu Wang, Meixin Zhao, Dinggang Shen

{"title":"Hierarchical Contrastive Learning for Precise Whole-body Anatomical Localization in PET/CT Imaging.","authors":"Yaozong Gao, Yiran Shu, Mingyang Yu, Yanbo Chen, Jingyu Liu, Shaonan Zhong, Weifang Zhang, Yiqiang Zhan, Xiang Sean Zhou, Xinlu Wang, Meixin Zhao, Dinggang Shen","doi":"10.1109/TMI.2025.3599197","DOIUrl":"10.1109/TMI.2025.3599197","url":null,"abstract":"Automatic anatomical localization is critical for radiology report generation. While many studies focus on lesion detection and segmentation, anatomical localization-accurately describing lesion positions in radiology reports-has received less attention. Conventional segmentation-based methods are limited to organ-level localization and often fail in severe disease cases due to low segmentation accuracy. To address these limitations, we reformulate anatomical localization as an image-to-text retrieval task. Specifically, we propose a CLIP-based framework that aligns lesion image patches with anatomically descriptive text embeddings in a shared multimodal space. By projecting lesion features into the semantic space and retrieving the most relevant anatomical descriptions in a coarse-to-fine manner, our method achieves fine-grained lesion localization with high accuracy across the entire body. Our main contributions are as follows: (1) hierarchical anatomical retrieval, which organizes 387 locations into a two-level hierarchy, by retrieving from the first level of 124 coarse categories to narrow down the search space and reduce localization complexity; (2) augmented location descriptions, which integrate domain-specific anatomical knowledge for enhancing semantic representation and improving visual-text alignment; and (3) semi-hard negative sample mining, which improves training stability and discriminative learning by avoiding selecting the overly similar negative samples that may introduce label noise or semantic ambiguity. We validate our method on two whole-body PET/CT datasets, achieving an 84.13% localization accuracy on the internal test set and 80.42% on the external test set, with a per-lesion inference time of 34 ms. The proposed framework also demonstrated superior robustness in complex clinical cases compared to segmentation-based approaches.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877630","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

PAINT: Prior-aided Alternate Iterative NeTwork for Ultra-low-dose CT Imaging Using Diffusion Model-restored Sinogram. PAINT：基于扩散模型恢复Sinogram超低剂量CT成像的先验辅助交替迭代网络。

IEEE transactions on medical imaging Pub Date : 2025-08-18 DOI: 10.1109/TMI.2025.3599508

Kaile Chen, Weikang Zhang, Ziheng Deng, Yufu Zhou, Jun Zhao

{"title":"PAINT: Prior-aided Alternate Iterative NeTwork for Ultra-low-dose CT Imaging Using Diffusion Model-restored Sinogram.","authors":"Kaile Chen, Weikang Zhang, Ziheng Deng, Yufu Zhou, Jun Zhao","doi":"10.1109/TMI.2025.3599508","DOIUrl":"10.1109/TMI.2025.3599508","url":null,"abstract":"Obtaining multiple CT scans from the same patient is required in many clinical scenarios, such as lung nodule screening and image-guided radiation therapy. Repeated scans would expose patients to higher radiation dose and increase the risk of cancer. In this study, we aim to achieve ultra-low-dose imaging for subsequent scans by collecting extremely undersampled sinogram via regional few-view scanning, and preserve image quality utilizing the preceding fullsampled scan as prior. To fully exploit prior information, we propose a two-stage framework consisting of diffusion model-based sinogram restoration and deep learning-based unrolled iterative reconstruction. Specifically, the undersampled sinogram is first restored by a conditional diffusion model with sinogram-domain prior guidance. Then, we formulate the undersampled data reconstruction problem as an optimization problem combining fidelity terms for both undersampled and restored data, along with a regularization term based on image-domain prior. Next, we propose Prior-aided Alternate Iterative NeTwork (PAINT) to solve the optimization problem. PAINT alternately updates the undersampled or restored data fidelity term, and unrolls the iterations to integrate neural network-based prior regularization. In the case of 112 mm field of view in simulated data experiments, our proposed framework achieved superior performance in terms of CT value accuracy and image details preservation. Clinical data experiments also demonstrated that our proposed framework outperformed the comparison methods in artifact reduction and structure recovery.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877631","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

SynthAorta: A 3D Mesh Dataset of Parametrized Physiological Healthy Aortas. SynthAorta：一个参数化生理健康主动脉的三维网格数据集。

IEEE transactions on medical imaging Pub Date : 2025-08-18 DOI: 10.1109/TMI.2025.3599937

Domagoj Bosnjak, Gian Marco Melito, Richard Schussnig, Katrin Ellermann, Thomas-Peter Fries

{"title":"SynthAorta: A 3D Mesh Dataset of Parametrized Physiological Healthy Aortas.","authors":"Domagoj Bosnjak, Gian Marco Melito, Richard Schussnig, Katrin Ellermann, Thomas-Peter Fries","doi":"10.1109/TMI.2025.3599937","DOIUrl":"10.1109/TMI.2025.3599937","url":null,"abstract":"The effects of the aortic geometry on its mechanics and blood flow, and subsequently on aortic pathologies, remain largely unexplored. The main obstacle lies in obtaining patient-specific aorta models, an extremely difficult procedure in terms of ethics and availability, segmentation, mesh generation, and all of the accompanying processes. Contrastingly, idealized models are easy to build but do not faithfully represent patient-specific variability. Additionally, a unified aortic parametrization in clinic and engineering has not yet been achieved. To bridge this gap, we introduce a new set of statistical parameters to generate synthetic models of the aorta. The parameters possess geometric significance and fall within physiological ranges, effectively bridging the disciplines of clinical medicine and engineering. Smoothly blended realistic representations are recovered with convolution surfaces. These enable high-quality visualization and biological appearance, whereas the structured mesh generation paves the way for numerical simulations. The only requirement of the approach is one patient-specific aorta model and the statistical data for parameter values obtained from the literature. The output of this work is SynthAorta, a dataset of ready-to-use synthetic, physiological aorta models, each containing a centerline, surface representation, and a structured hexahedral finite element mesh. The meshes are structured and fully consistent between different cases, making them imminently suitable for reduced order modeling and machine learning approaches.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877632","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

Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia. 互惠多网络噪声标签学习（MMNNLL）方法及其在双相情感障碍和精神分裂症跨诊断分类中的应用。

IEEE transactions on medical imaging Pub Date : 2025-07-04 DOI: 10.1109/TMI.2025.3585880

Yuhui Du, Zheng Wang, Ju Niu, Yulong Wang, Godfrey D Pearlson, Vince D Calhoun

{"title":"Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia.","authors":"Yuhui Du, Zheng Wang, Ju Niu, Yulong Wang, Godfrey D Pearlson, Vince D Calhoun","doi":"10.1109/TMI.2025.3585880","DOIUrl":"https://doi.org/10.1109/TMI.2025.3585880","url":null,"abstract":"The subjective nature of diagnosing mental disorders complicates achieving accurate diagnoses. The complex relationship among disorders further exacerbates this issue, particularly in clinical practice where conditions like bipolar disorder (BP) and schizophrenia (SZ) can present similar clinical symptoms and cognitive impairments. To address these challenges, this paper proposes a mutualistic multi-network noisy label learning (MMNNLL) method, which aims to enhance diagnostic accuracy by leveraging neuroimaging data under the presence of potential clinical diagnosis bias or errors. MMNNLL effectively utilizes multiple deep neural networks (DNNs) for learning from data with noisy labels by maximizing the consistency among DNNs in identifying and utilizing samples with clean and noisy labels. Experimental results on public CIFAR-10 and PathMNIST datasets demonstrate the effectiveness of our method in classifying independent test data across various types and levels of label noise. Additionally, our MMNNLL method significantly outperforms state-of-the-art noisy label learning methods. When applied to brain functional connectivity data from BP and SZ patients, our method identifies two biotypes that show more pronounced group differences, and improved classification accuracy compared to the original clinical categories, using both traditional machine learning and advanced deep learning techniques. In summary, our method effectively addresses the possible inaccuracy in nosology of mental disorders and achieves transdiagnostic classification through robust noisy label learning via multi-network collaboration and competition.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565572","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

A Chain of Diagnosis Framework for Accurate and Explainable Radiology Report Generation. 生成准确和可解释的放射学报告的诊断链框架。

IEEE transactions on medical imaging Pub Date : 2025-07-03 DOI: 10.1109/TMI.2025.3585765

Haibo Jin, Haoxuan Che, Sunan He, Hao Chen

{"title":"A Chain of Diagnosis Framework for Accurate and Explainable Radiology Report Generation.","authors":"Haibo Jin, Haoxuan Che, Sunan He, Hao Chen","doi":"10.1109/TMI.2025.3585765","DOIUrl":"https://doi.org/10.1109/TMI.2025.3585765","url":null,"abstract":"Despite the progress of radiology report generation (RRG), existing works face two challenges: 1) The performances in clinical efficacy are unsatisfactory, especially for lesion attributes description; 2) the generated text lacks explainability, making it difficult for radiologists to trust the results. To address the challenges, we focus on a trustworthy RRG model, which not only generates accurate descriptions of abnormalities, but also provides basis of its predictions. To this end, we propose a framework named chain of diagnosis (CoD), which maintains a chain of diagnostic process for clinically accurate and explainable RRG. It first generates question-answer (QA) pairs via diagnostic conversation to extract key findings, then prompts a large language model with QA diagnoses for accurate generation. To enhance explainability, a diagnosis grounding module is designed to match QA diagnoses and generated sentences, where the diagnoses act as a reference. Moreover, a lesion grounding module is designed to locate abnormalities in the image, further improving the working efficiency of radiologists. To facilitate label-efficient training, we propose an omni-supervised learning strategy with clinical consistency to leverage various types of annotations from different datasets. Our efforts lead to 1) an omni-labeled RRG dataset with QA pairs and lesion boxes; 2) a evaluation tool for assessing the accuracy of reports in describing lesion location and severity; 3) extensive experiments to demonstrate the effectiveness of CoD, where it outperforms both specialist and generalist models consistently on two RRG benchmarks and shows promising explainability by accurately grounding generated sentences to QA diagnoses and images.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562423","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

Joint Shape Reconstruction and Registration via a Shared Hybrid Diffeomorphic Flow. 基于共享混合差胚流的关节形状重建与配准。

IEEE transactions on medical imaging Pub Date : 2025-07-03 DOI: 10.1109/TMI.2025.3585560

Hengxiang Shi, Ping Wang, Shouhui Zhang, Xiuyang Zhao, Bo Yang, Caiming Zhang

{"title":"Joint Shape Reconstruction and Registration via a Shared Hybrid Diffeomorphic Flow.","authors":"Hengxiang Shi, Ping Wang, Shouhui Zhang, Xiuyang Zhao, Bo Yang, Caiming Zhang","doi":"10.1109/TMI.2025.3585560","DOIUrl":"https://doi.org/10.1109/TMI.2025.3585560","url":null,"abstract":"Deep implicit functions (DIFs) effectively represent shapes by using a neural network to map 3D spatial coordinates to scalar values that encode the shape's geometry, but it is difficult to establish correspondences between shapes directly, limiting their use in medical image registration. The recently presented deformation field-based methods achieve implicit templates learning via template field learning with DIFs and deformation field learning, establishing shape correspondence through deformation fields. Although these approaches enable joint learning of shape representation and shape correspondence, the decoupled optimization for template field and deformation field, caused by the absence of deformation annotations lead to a relatively accurate template field but an underoptimized deformation field. In this paper, we propose a novel implicit template learning framework via a shared hybrid diffeomorphic flow (SHDF), which enables shared optimization for deformation and template, contributing to better deformations and shape representation. Specifically, we formulate the signed distance function (SDF, a type of DIFs) as a one-dimensional (1D) integral, unifying dimensions to match the form used in solving ordinary differential equation (ODE) for deformation field learning. Then, SDF in 1D integral form is integrated seamlessly into the deformation field learning. Using a recurrent learning strategy, we frame shape representations and deformations as solving different initial value problems of the same ODE. We also introduce a global smoothness regularization to handle local optima due to limited outside-of-shape data. Experiments on medical datasets show that SHDF outperforms state-of-the-art methods in shape representation and registration.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562424","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