具有自监督学习和合成域自适应的扩散刺激CT-US配准模型

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Shangxuan Li , Biao Jia , Weiming Huang , Xiaobo Zhang , Wu Zhou , Cheng Wang , Gaojun Teng
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引用次数: 0

摘要

在腹部介入手术中,实现2D超声(US)帧与3D计算机断层扫描(CT)扫描的精确注册是一个重大挑战。传统的跟踪方法通常依赖于高精度的传感器,而这种传感器可能非常昂贵。此外,临床需要实时注册与广泛的捕获范围经常超过标准的基于图像的优化技术的性能。目前利用深度学习的自动配准方法要么严重依赖人工标注进行训练,要么难以有效地弥合不同图像域之间的差距。为了解决这些挑战,我们提出了一种新的扩散刺激CT-US配准模型。该模型利用超声的物理扩散特性,从术前CT数据生成合成超声图像。此外,我们引入了一种使用扩散模型的合成到真实域的自适应策略,以减轻真实图像和合成图像之间的差异。在这些合成图像上训练的双流自监督回归神经网络,然后用于估计CT空间内的姿态。我们提出的方法的有效性通过使用双模态人类腹部幻影的US和CT扫描验证。我们的实验结果证实,我们的方法可以在可接受的误差范围内准确地初始化美国图像姿态,并随后对其进行改进以实现精确对准。这使得CT和US图像的实时、独立于跟踪器和健壮的刚性配准成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A diffusion-stimulated CT-US registration model with self-supervised learning and synthetic-to-real domain adaptation
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.
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
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.
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