绘制草图,绘制肉体:从任何x射线视图的全身计算机断层扫描

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computer Vision Pub Date : 2024-11-27 DOI:10.1007/s11263-024-02286-2
Yongsheng Pan, Yiwen Ye, Yanning Zhang, Yong Xia, Dinggang Shen
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引用次数: 0

摘要

立体观察是医学图像分析的共同基础,一般通过CT、MRI等固定扫描仪的三维医学成像来实现,在一些灵活的场景下,这些扫描仪不如x光机方便。然而,x射线图像只能提供二维透视观察,缺乏三维视角。如果能从x射线图像中推断出三维信息,将会拓宽x射线机的应用范围。针对上述目标,本文致力于从非平行二维透视x射线(PXR)视图生成伪三维CT扫描,并提出了Draw Sketch and Draw Flesh (DSDF)框架,首先从PXR视图粗略预测组织分布(Sketch),然后从组织分布和PXR视图渲染组织细节(Flesh)。不同于以往的研究只关注部分部位,如胸部或颈部,本研究从理论上探讨了头腿重建的可行性,即普遍适用于身体的任何部位。来自4个队列的559个全身样本的实验表明,我们的DSDF可以重建比现有方法更合理的伪CT图像,并且在可视化和各种下游任务中都取得了令人满意的结果。源代码和训练有素的模型可从https://github.com/YongshengPan/WholeBodyXraytoCT获得。
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Draw Sketch, Draw Flesh: Whole-Body Computed Tomography from Any X-Ray Views

Stereoscopic observation is a common foundation of medical image analysis and is generally achieved by 3D medical imaging based on settled scanners, such as CT and MRI, that are not as convenient as X-ray machines in some flexible scenarios. However, X-ray images can only provide perspective 2D observation and lack view in the third dimension. If 3D information can be deduced from X-ray images, it would broaden the application of X-ray machines. Focus on the above objective, this paper dedicates to the generation of pseudo 3D CT scans from non-parallel 2D perspective X-ray (PXR) views and proposes the Draw Sketch and Draw Flesh (DSDF) framework to first roughly predict the tissue distribution (Sketch) from PXR views and then render the tissue details (Flesh) from the tissue distribution and PXR views. Different from previous studies that focus only on partial locations, e.g., chest or neck, this study theoretically investigates the feasibility of head-to-leg reconstruction, i.e., generally applicable to any body parts. Experiments on 559 whole-body samples from 4 cohorts suggest that our DSDF can reconstruct more reasonable pseudo CT images than state-of-the-art methods and achieve promising results in both visualization and various downstream tasks. The source code and well-trained models are available a https://github.com/YongshengPan/WholeBodyXraytoCT.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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