Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning,NMR in Biomedicine

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Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
NMR in Biomedicine ( IF 2.7 ) Pub Date : 2019-12-22 , DOI: 10.1002/nbm.4224
Kuang Gong ₁ , Paul Han ₁ , Georges El Fakhri ₁ , Chao Ma ₁ , Quanzheng Li ₁

Affiliation

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.

中文翻译：

使用无监督深度学习的动脉自旋标记 MR 图像去噪和重建

动脉自旋标记 (ASL) 成像是一种强大的磁共振成像技术，可以无创地定量测量血液灌注，这在评估各种临床环境中的组织活力方面具有巨大潜力。然而，目前 ASL 的临床应用受到其低信噪比 (SNR)、空间分辨率有限和成像时间长的限制。在这项工作中，我们提出了一种基于无监督深度学习的图像去噪和重建框架，以提高 SNR 并加快高分辨率 ASL 成像的成像速度。所提出的框架的独特之处在于它不需要任何先验训练对，而只需要受试者自己的解剖学先验（例如 T1 加权图像）作为网络输入。神经网络在去噪或重建过程中从头开始训练，使用噪声图像或少量采样的 k 空间数据作为训练标签。使用从 3 名健康受试者在 3T MR 扫描仪上获得的体内实验数据评估所提出方法的性能，使用以 44 分钟采集时间采集的 ASL 图像作为基本事实。定性和定量分析都证明了所提出的 txtc 框架优于参考方法的性能。总之，我们提出的基于无监督深度学习的去噪和重建框架可以提高图像质量并加快 ASL 成像的成像速度。使用从 3 名健康受试者在 3T MR 扫描仪上获得的体内实验数据评估所提出方法的性能，使用以 44 分钟采集时间采集的 ASL 图像作为基本事实。定性和定量分析都证明了所提出的 txtc 框架优于参考方法的性能。总之，我们提出的基于无监督深度学习的去噪和重建框架可以提高图像质量并加快 ASL 成像的成像速度。使用从 3 名健康受试者在 3T MR 扫描仪上获得的体内实验数据评估所提出方法的性能，使用以 44 分钟采集时间采集的 ASL 图像作为基本事实。定性和定量分析都证明了所提出的 txtc 框架优于参考方法的性能。总之，我们提出的基于无监督深度学习的去噪和重建框架可以提高图像质量并加快 ASL 成像的成像速度。

更新日期：2019-12-22

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