MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model
MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model
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DOI：
10.1002/mp.13717
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发表时间：
2019-08-07
期刊：
影响因子：
3.8
通讯作者：
Park, Justin C.
中科院分区：
文献类型：
--
作者：
Chun, Jaehee;Zhang, Hao;Park, Justin C.
基于深度学习（DL）的磁共振成像（MRI）的超分辨率（SR）重建由于其空间分辨率的显著提高而备受关注。然而，阻碍这些方法广泛实施的挑战仍然存在。在临床中捕获的低分辨率（LR） mri显示出被噪声混淆的复杂组织结构，这对于简单的DL框架来说很难处理。此外，为SR任务训练一个强大的网络需要大量的、完美匹配的LR和高分辨率（HR）图像，这些图像通常是不可用的或难以收集的。本研究的目的是基于级联深度学习的概念开发一种新的MRI SR技术，该技术允许在训练数据不足、翻译模型未知和噪声存在的情况下重建高质量的SR图像。该框架基于级联深度学习的概念，由三个部分组成：(a)使用临床LR噪声MRI扫描训练的去噪自编码器（DAE），该扫描经过非局部均值滤波器处理，生成去噪LR数据；(b)下采样网络（DSN），该网络使用来自志愿者的少量配对LR/HR数据进行训练，可以生成完美配对的LR/HR数据，用于生成模型的训练；(c)建议的SR生成模型（p-SRG）使用DSN生成的数据进行训练，该数据从LR输入映射到HR输出。训练后，LR临床图像可以通过DAE和p-SRG进行输入，以产生LR输入的SR重建。在两种情况下探讨了该框架的应用：从LR轴向扫描中获得的3D屏气MRI轴向SR重建(
Purpose Deep learning (DL)-based super-resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challenges hindering the widespread implementation of these approaches remain, however. Low-resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise that are difficult for a simple DL framework to handle. Moreover, training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high-resolution (HR) images that are often unavailable or difficult to collect. The purpose of this study is to develop a novel SR technique for MRI based on the concept of cascaded DL that allows for the reconstruction of high-quality SR images in the presence of insufficient training data, an unknown translation model, and noise. Methods The proposed framework, based on the concept named cascaded deep learning, consists of three components: (a) a denoising autoencoder (DAE) trained using clinical LR noisy MRI scans that have been processed with a nonlocal means filter that generates denoised LR data; (b) a down-sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data for the training of a generative model; and (c) the proposed SR generative model (p-SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p-SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D breath-hold MRI axial SR reconstruction from LR axial scans (