用于纵向单片腹部计算机断层扫描协调的深度条件生成模型。

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-03-01 Epub Date: 2024-04-02 DOI:10.1117/1.JMI.11.2.024008
Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A Landman
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引用次数: 0

摘要

目的:二维单片腹部计算机断层扫描(CT)提供了高分辨率的详细组织图,可定量分析健康状况与衰老之间的关系。然而,由于不同年份采集的切片之间存在位置差异,导致采集到的器官/组织不同,因此很难利用这些扫描结果对身体成分变化进行纵向分析:为了解决这个问题,我们提出了C-SliceGen,它以腹部区域的任意轴向切片为条件,通过估计潜空间的结构变化生成预定义的椎体水平切片:我们对来自两个内部数据集的 2608 个容积 CT 数据和来自 2015 年多图集腹部标注挑战赛颅外穹隆 (BTCV) 数据集的 50 个受试者进行了实验,结果表明我们的模型可以生成逼真且相似的高质量图像。我们还在巴尔的摩老龄化纵向研究数据集中的 1033 名受试者身上进一步评估了我们的方法协调纵向位置差异的能力,该数据集包含纵向单个腹部切片,结果证实我们的方法可以协调内脏脂肪面积方面的切片位置差异:结论:这种方法为将不同椎体水平的切片映射到目标切片并减少单片纵向分析的位置差异提供了一个很有前景的方向。源代码见:https://github.com/MASILab/C-SliceGen。
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Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization.

Purpose: Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured.

Approach: To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space.

Results: Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge Beyond the Cranial Vault (BTCV) dataset demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore longitudinal study of aging dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.

Conclusion: This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.

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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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