Ekanayake Mevan, Pawar Kamlesh, Chen Zhifeng, Egan Gary, Chen Zhaolin
Monash Biomedical Imaging, Monash University, Clayton, VIC, 3800, Australia.
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, 3800, Australia.
J Imaging Inform Med. 2024 Dec 4. doi: 10.1007/s10278-024-01250-3.
Deep learning (DL) models are effective in leveraging latent representations from MR data, emerging as state-of-the-art solutions for accelerated MRI reconstruction. However, challenges arise due to the inherent uncertainties associated with undersampling in k-space, coupled with the over- or under-parameterized and opaque nature of DL models. Addressing uncertainty has thus become a critical issue in DL MRI reconstruction. Monte Carlo (MC) inference techniques are commonly employed to estimate uncertainty, involving multiple reconstructions of the same scan to compute variance as a measure of uncertainty. Nevertheless, these methods entail significant computational expenses, requiring multiple inferences through the DL model. In this context, we propose a novel approach to uncertainty estimation during MRI reconstruction using a pixel classification framework. Our method, PixCUE (Pixel Classification Uncertainty Estimation), generates both the reconstructed image and an uncertainty map in a single forward pass through the DL model. We validate the efficacy of this approach by demonstrating that PixCUE-generated uncertainty maps exhibit a strong correlation with reconstruction errors across various MR imaging sequences and under diverse adversarial conditions. We present an empirical relationship between uncertainty estimations using PixCUE and established reconstruction metrics such as NMSE, PSNR, and SSIM. Furthermore, we establish a correlation between the estimated uncertainties from PixCUE and the conventional MC method. Our findings affirm that PixCUE reliably estimates uncertainty in MRI reconstruction with minimal additional computational cost.
深度学习（DL）模型在利用磁共振（MR）数据的潜在表征方面很有效，已成为加速磁共振成像（MRI）重建的最先进解决方案。然而，由于k空间欠采样所固有的不确定性，再加上DL模型的参数过多或过少以及不透明的性质，挑战随之而来。因此，解决不确定性已成为DL MRI重建中的一个关键问题。蒙特卡罗（MC）推理技术通常用于估计不确定性，包括对同一扫描进行多次重建以计算方差作为不确定性的度量。然而，这些方法需要大量的计算开销，需要通过DL模型进行多次推理。在此背景下，我们提出了一种在MRI重建过程中使用像素分类框架进行不确定性估计的新方法。我们的方法PixCUE（像素分类不确定性估计）在单次正向通过DL模型时生成重建图像和不确定性图。我们通过证明PixCUE生成的不确定性图在各种MR成像序列和不同对抗条件下与重建误差具有很强的相关性，来验证这种方法的有效性。我们给出了使用PixCUE进行不确定性估计与诸如归一化均方误差（NMSE）、峰值信噪比（PSNR）和结构相似性指数（SSIM）等既定重建指标之间的经验关系。此外，我们建立了PixCUE估计的不确定性与传统MC方法之间的相关性。我们的研究结果证实，PixCUE能够以最小的额外计算成本可靠地估计MRI重建中的不确定性。
