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一种用于腹部放射治疗的从 MRI 生成合成 CT 的技术。

A technique to generate synthetic CT from MRI for abdominal radiotherapy.

机构信息

Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.

Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA.

出版信息

J Appl Clin Med Phys. 2020 Feb;21(2):136-143. doi: 10.1002/acm2.12816. Epub 2020 Feb 11.

Abstract

PURPOSE

To investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy.

METHODS

An institutional review board approved volunteer study was performed on a 3T MRI scanner. In-phase, fat and water images were acquired for five volunteers with breath-hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high-density tissue, and spine/air/lungs) were generated using a fuzzy-c means clustering algorithm. The third cluster was further segmented into three sub-clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two-arc volumetric modulated arc therapy (VMAT) technique.

RESULTS

The volume ratio of auto-segmentation to manual segmentation was 0.88-2.1 for the air segment and 0.72-1.13 for the remaining segments. The range of the Dice coefficient was 0.24-0.83, 0.84-0.93, 0.94-0.98, 0.93-0.96, and 0.76-0.79 for air, fat, lungs, high-density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3-29.1 mm, 0.5-1.3 mm, 0.4-1 mm, 0.7-1.6 mm, and 1.2-1.4 mm for air, fat, lungs, high-density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations.

CONCLUSION

A method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.

摘要

目的

研究一种使用 MRI 为腹部放射治疗计划生成合成 CT 的组织类型分类方法。

方法

在 3T MRI 扫描仪上进行了机构审查委员会批准的志愿者研究。使用 mDixon 脉冲序列对 5 名志愿者进行屏气采集同相位、脂肪和水图像。开发了一种用于腹部合成 CT 生成的组织分类方法。使用模糊 C 均值聚类算法生成三个组织聚类(脂肪、高密度组织和脊柱/空气/肺)。第三聚类进一步分为代表脊柱、空气和肺的三个子聚类。因此,自动生成了五个段。为了评估该方法的分割准确性,将这五个段手动勾画在 MRI 图像上作为金标准,计算体积比、Dice 系数和 Hausdorff 距离度量。使用双弧容积调强弧形治疗(VMAT)技术评估模拟靠近空气、肺和脊柱的目标的分割准确性的剂量学效应。

结果

自动分割与手动分割的体积比为空气段的 0.88-2.1,其余段的 0.72-1.13。Dice 系数的范围为空气段 0.24-0.83、0.84-0.93、0.94-0.98、0.93-0.96 和 0.76-0.79,脂肪段 0.84-0.93、0.94-0.98、0.93-0.96 和 0.76-0.79,肺段 0.94-0.98、0.93-0.96 和 0.76-0.79,高密度组织段 0.93-0.96 和 0.76-0.79,脊柱段 0.76-0.79。尽管空气和脊柱的分割准确性较差,但所有目标位置的剂量学效应差异最大为 0.8%,仅为 0.2%±0.2%。

结论

开发了一种用于腹部生成合成 CT 的方法,并评估了分割准确性及其剂量学效应。我们的结果表明,单独使用 MRI 进行腹部治疗计划具有潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53d8/7020981/eb48d3e0911b/ACM2-21-136-g001.jpg

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