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基于通量概念从胸部自由呼吸MRI切片采集构建4D图像。

4D image construction from free-breathing MRI slice acquisitions of the thorax based on a concept of flux.

作者信息

Hao You, Udupa Jayaram K, Tong Yubing, Wu Caiyun, Li Hua, McDonough Joseph M, Torigian Drew A, Cahill Patrick J

机构信息

Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.

Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States.

出版信息

Proc SPIE Int Soc Opt Eng. 2020 Feb;11312. doi: 10.1117/12.2550040. Epub 2020 Mar 16.

Abstract

Retrospective 4D image construction from continuously acquired 2D slices is a necessary step to achieve high-quality 4D images. Self-gating methods, which extract breathing signals only from image information without any external gating technology, have much potential, such as in pediatric patients with thoracic insufficiency syndrome (TIS) who suffer from extreme malformations of the chest wall, diaphragm, and spine, leading to breathing that is very complex with lots of abnormal respiration cycles, including very deep or shallow cycles. Existing methods do not work well in this clinical scenario and most are not fully automatic, requiring some manual interactive operations. In this paper, we propose a fully automatic 4D dMRI construction method based on the concept of flux to address the 4D image construction from 2D slices of subjects with complex respiration. Firstly, we extract the breathing signal for each location based on the flux of the optical flow vector field of the body region from the image series. Then, we give a full analysis for all cycles and extract several normal ones and map them to one cosine respiration model for each location. After that, we re-sample one normal cycle from the respiration model for each location independently. All of these resampled normal cycles form the final constructed 4D image. Qualitative and quantitative evaluations on 25 subjects show that the proposed method can handle datasets from subjects with more complex respiration and achieves good self-consistency results while maintaining time and space continuity.

摘要

从连续采集的二维切片构建回顾性四维图像是获得高质量四维图像的必要步骤。自门控方法仅从图像信息中提取呼吸信号,无需任何外部门控技术,具有很大潜力,例如在患有胸廓发育不全综合征(TIS)的儿科患者中,这些患者的胸壁、膈肌和脊柱存在严重畸形,导致呼吸非常复杂,有许多异常呼吸周期,包括非常深或浅的周期。现有方法在这种临床场景中效果不佳,并且大多数不是完全自动的,需要一些手动交互操作。在本文中,我们提出了一种基于通量概念的全自动四维扩散磁共振成像构建方法,以解决复杂呼吸受试者二维切片的四维图像构建问题。首先,我们根据图像序列中身体区域光流矢量场的通量为每个位置提取呼吸信号。然后,我们对所有周期进行全面分析,提取几个正常周期,并将它们映射到每个位置的一个余弦呼吸模型。之后,我们为每个位置独立地从呼吸模型中重新采样一个正常周期。所有这些重新采样的正常周期构成最终构建的四维图像。对25名受试者的定性和定量评估表明,所提出的方法可以处理来自呼吸更复杂受试者的数据集,并在保持时间和空间连续性的同时取得良好的自一致性结果。

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