Poitrasson-Rivière Alexis, Vanderver Michael D, Hagio Tomoe, Arida-Moody Liliana, Moody Jonathan B, Renaud Jennifer M, Ficaro Edward P, Murthy Venkatesh L
INVIA, LLC, Ann Arbor, MI, USA.
INVIA, LLC, Ann Arbor, MI, USA.
J Nucl Cardiol. 2024 Dec;42:102052. doi: 10.1016/j.nuclcard.2024.102052. Epub 2024 Oct 3.
Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets and myocardial segmentation enables consistent image scaling and quantification. However, such manual tasks are cumbersome. We developed a 3D U-Net deep-learning (DL) algorithm for automated myocardial segmentation in cardiac sarcoidosis FDG PET.
The DL model was trained on FDG PET scans from 316 patients with left ventricular contours derived from paired perfusion datasets. Qualitative analysis of clinical readability was performed to compare DL segmentation with the current automated method on a 50-patient test subset. Additionally, left ventricle displacement and angulation, as well as SUVmax sampling were compared with inter-user reproducibility results. A hybrid workflow was also investigated to accelerate study processing time.
DL segmentation enhanced readability scores in over 90% of cases compared with the standard segmentation currently used in the software. DL segmentation performed similar to a trained technologist, surpassing standard segmentation for left ventricle displacement and angulation, as well as correlation of SUVmax. Using the DL segmentation as initial placement for manual segmentation significantly decreased the processing time.
A novel DL-based automated segmentation tool markedly improves processing of cardiac sarcoidosis FDG PET. This tool yields optimized splash display of sarcoidosis FDG PET datasets with no user input and offers significant processing time improvement for manual segmentation of such datasets.
氟脱氧葡萄糖正电子发射断层扫描（FDG PET）通过抑制心肌葡萄糖利用在心脏结节病的诊断中起着关键作用。将图像重新定向以匹配灌注数据集并进行心肌分割可实现一致的图像缩放和量化。然而，这些手动任务很繁琐。我们开发了一种3D U-Net深度学习（DL）算法，用于在心脏结节病FDG PET中自动进行心肌分割。
DL模型在来自316例患者的FDG PET扫描上进行训练，这些扫描具有从配对灌注数据集中得出的左心室轮廓。对50例患者的测试子集进行了临床可读性的定性分析，以比较DL分割与当前自动方法。此外，还将左心室位移和角度以及SUVmax采样与用户间的可重复性结果进行了比较。还研究了一种混合工作流程以加快研究处理时间。
与软件中当前使用的标准分割相比，DL分割在超过90%的病例中提高了可读性分数。DL分割的表现与训练有素的技术人员相似，在左心室位移和角度以及SUVmax的相关性方面超过了标准分割。将DL分割用作手动分割的初始放置可显著减少处理时间。
一种基于DL的新型自动分割工具显著改善了心脏结节病FDG PET的处理。该工具无需用户输入即可优化结节病FDG PET数据集的显示，并为手动分割此类数据集提供了显著的处理时间改进。
