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探索基于深度学习的大脑MRI-to-CT合成中的对比概括

Lotte Nijskens¹, Cornelis A T van den Berg¹, Joost J C Verhoeff²

¹Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Science, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands; Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

|July 20, 2023

概括

域随机化 (DR) 增强了从MRI生成合成CT的深度学习模型,提高了稳定性并减少了再培训需求. 这种方法提高了放射治疗应用中未见的MRI序列的概括性.

科学领域:

医疗成像医学成像
放射治疗物理物理
人工智能在医学中的应用

背景情况:

合成计算机断层扫描 (sCT) 对基于MRI的放射治疗至关重要,深度学习 (DL) 模型对其产生有希望.
挑战来自不同中心的MRI协议的变化,导致DL模型的概括性较差和质量较低的sCT.
这就需要方法来提高用于sCT生成的DL模型的稳定性和通用性.

研究的目的:

调查域随机化 (DR) 在增强DL模型对大脑sCT生成的概括能力方面的有效性.
评估DR对从未见过的MRI序列生成的sCT的准确性和稳定性的影响.

主要方法:

一个生成对抗网络 (GAN) 使用CT和各种MRI序列 (T1,T2,FLAIR) 来训练95名接受放射治疗的患者.
一个基线模型在没有DR的情况下被训练,其在未见的FLAIR序列上的性能与DR增强模型进行了比较.
图像相似度指标和基于sCT的放射治疗剂量计划的准确性与基底真相CT进行了评估.

主要成果:

基线模型在未见的FLAIR序列 (106 ± 20.7 HU) 上显示了最高的平均绝对误差 (MAE).
与基线相比,DR模型在FLAIR上表现有所改善 (MAE = 99.0 ± 14.9 HU),尽管仍然低于用FLAIR数据训练的模型 (MAE = 72.6 ± 10.1 HU).
与基线相比,DR还导致了改善的马传递率,这表明剂量计划的准确性更好.

结论:

域随机化有效地提高图像相似性和剂量准确性,用于在看不见的MRI序列上生成sCT.
DR提高了DL模型的稳定性,在遇到新的或变化的MRI数据时减轻了重新培训的需要.
这种方法支持基于MRI的放射疗法的更广泛的临床采用,通过提高不同成像协议中合成CT生成的可靠性.

关键词:

人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.域名转移域名转移域名转移概括概括是一个概括.机器学习机器学习磁共振成像技术磁共振成像技术医学成像医学成像辐射疗法辐射疗法回归是一种回归.

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