Shangxuan Li , Biao Jia , Weiming Huang , Xiaobo Zhang , Wu Zhou , Cheng Wang , Gaojun Teng
{"title":"A diffusion-stimulated CT-US registration model with self-supervised learning and synthetic-to-real domain adaptation","authors":"Shangxuan Li , Biao Jia , Weiming Huang , Xiaobo Zhang , Wu Zhou , Cheng Wang , Gaojun Teng","doi":"10.1016/j.compmedimag.2025.102562","DOIUrl":null,"url":null,"abstract":"<div><div>In abdominal interventional procedures, achieving precise registration of 2D ultrasound (US) frames with 3D computed tomography (CT) scans presents a significant challenge. Traditional tracking methods often rely on high-precision sensors, which can be prohibitively expensive. Furthermore, the clinical need for real-time registration with a broad capture range frequently exceeds the performance of standard image-based optimization techniques. Current automatic registration methods that utilize deep learning are either heavily reliant on manual annotations for training or struggle to effectively bridge the gap between different imaging domains. To address these challenges, we propose a novel diffusion-stimulated CT-US registration model. This model harnesses the physical diffusion properties of US to generate synthetic US images from preoperative CT data. Additionally, we introduce a synthetic-to-real domain adaptation strategy using a diffusion model to mitigate the discrepancies between real and synthetic US images. A dual-stream self-supervised regression neural network, trained on these synthetic images, is then used to estimate the pose within the CT space. The effectiveness of our proposed approach is verified through validation using US and CT scans from a dual-modality human abdominal phantom. The results of our experiments confirm that our method can accurately initialize the US image pose within an acceptable range of error and subsequently refine it to achieve precise alignment. This enables real-time, tracker-independent, and robust rigid registration of CT and US images.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"123 ","pages":"Article 102562"},"PeriodicalIF":5.4000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611125000710","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In abdominal interventional procedures, achieving precise registration of 2D ultrasound (US) frames with 3D computed tomography (CT) scans presents a significant challenge. Traditional tracking methods often rely on high-precision sensors, which can be prohibitively expensive. Furthermore, the clinical need for real-time registration with a broad capture range frequently exceeds the performance of standard image-based optimization techniques. Current automatic registration methods that utilize deep learning are either heavily reliant on manual annotations for training or struggle to effectively bridge the gap between different imaging domains. To address these challenges, we propose a novel diffusion-stimulated CT-US registration model. This model harnesses the physical diffusion properties of US to generate synthetic US images from preoperative CT data. Additionally, we introduce a synthetic-to-real domain adaptation strategy using a diffusion model to mitigate the discrepancies between real and synthetic US images. A dual-stream self-supervised regression neural network, trained on these synthetic images, is then used to estimate the pose within the CT space. The effectiveness of our proposed approach is verified through validation using US and CT scans from a dual-modality human abdominal phantom. The results of our experiments confirm that our method can accurately initialize the US image pose within an acceptable range of error and subsequently refine it to achieve precise alignment. This enables real-time, tracker-independent, and robust rigid registration of CT and US images.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.