Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

• Published in: NMR in biomedicine 2022-04, Vol.35 (4), p.e4224-n/a
• Main Authors: Gong, Kuang, Han, Paul, El Fakhri, Georges, Ma, Chao, Li, Quanzheng
• Format: Article
• Language: English
• Subjects: applications; Biological products; Brain; Deep Learning; human study; Humans; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Image resolution; Imaging techniques; In vivo methods and tests; Labeling; Labels; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; methods and engineering; Neural networks; neurological; Noise reduction; Perfusion; perfusion and permeability methods; perfusion spin labeling methods; post‐acquisition processing; Qualitative analysis; reconstruction; Signal-To-Noise Ratio; Spatial discrimination; Spatial resolution; Spin labeling; Spin Labels; Training
• ISSN: 0952-3480; 1099-1492
• DOI: 10.1002/nbm.4224

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. 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. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed framework over the reference methods.