A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases
A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases
复制标题
DOI：
10.1016/j.radonc.2020.10.018
复制
发表时间：
2020-12-01
影响因子：
5.7
通讯作者：
Valentini, Vincenzo
中科院分区：
文献类型：
--
作者：
Cusumano, Davide;Lenkowicz, Jacopo;Valentini, Vincenzo
目的：人工智能（AI）可以在磁共振引导放射治疗（MRgRT）中发挥重要作用，特别是加快在线自适应工作流程。本研究的目的是建立一种能够从骨盆和腹部的低场MR图像生成合成计算机断层扫描（sCT）图像的深度学习（DL）方法。方法：使用条件生成对抗网络（cGAN）生成sCT：总共120名在盆腔和腹部接受治疗的患者被纳入，并被分为训练组（80名）和测试组（40名）。调强放疗（在sCT和原始CT上计算IMRT）治疗计划，然后根据伽玛分析和剂量体积直方图（DVH）的差异进行比较，使用配对样本的双单侧检验（TOST-P）来评价CT和sCT图像上计算的靶器官和危及器官（OAR）的不同DVH参数之间的等效性。使用CPU架构，神经网络生成合成CT所需的平均时间，盆腔病例为175 +/- 43秒，腹部病例为110 +/- 40秒。（1%/1 mm、2%/2 mm和3%/3 mm）分别为90.8 ± 4.5%、98.7 ± 1.1%和99.8 ± 0.2%;盆腔指标分别为89.3 ± 4.8%、99.0 ± 0.7%和99.9 ± 0.2%，而DVH指标之间的等效性在1%以内。本研究证明，使用DL方法生成sCT对于骨盆和腹部的低场MR图像是可行的，从而可以可靠地计算MRgRT中的IMRT计划。(C)2020爱思唯尔B. V.保留所有权利。
Purpose: Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generate synthetic computed tomography (sCT) images from low field MR images in pelvis and abdomen.Methods: A conditional Generative Adversarial Network (cGAN) was used for sCT generation: a total of 120 patients treated on pelvic and abdominal sites were enrolled and divided in training (80) and test sets (40).Intensity modulated radiotherapy (IMRT) treatment plans were calculated on sCT and original CT and then compared in terms of gamma analysis and differences in Dose Volume Histogram (DVH).The two one-sided test for paired samples (TOST-P) was used to evaluate the equivalence among different DVH parameters calculated for target and organs at risks (OAR) on CT and sCT images.Results: Using a CPU architecture, the mean time required by the neural network to generate a synthetic CT was 175 +/- 43 seconds (s) for pelvic cases and 110 +/- 40 s for abdominal ones.Mean gamma passing rates for the three tolerance criteria analysed (1%/1 mm, 2%/2 mm and 3%/3 mm) were respectively 90.8 +/- 4.5%, 98.7 +/- 1.1% and 99.8 +/- 0.2% for abdominal cases; 89.3 +/- 4.8%, 99.0 +/- 0.7% and 99.9 +/- 0.2% for pelvic ones, while equivalence within 1% was observed among the DVH indicators.Conclusion: This study demonstrated that sCT generation using a DL approach is feasible for low field MR images in pelvis and abdomen, allowing a reliable calculation of IMRT plans in MRgRT. (C) 2020 Elsevier B.V. All rights reserved.