Title: A mixed loss joint approach-based deep learning strategy to enhance SNR and resolution of arterial spin labelling MRI

Authors: A. Shyna; Amma C. Ushadevi; John Ansamma; C. Kesavadas; Thomas Bejoy; T.J. Anagha

Addresses: Department of Computer Science and Engineering, Thangal Kunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kerala, India ' Department of Electronics and Communications Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kerala, India ' Department of Computer Science and Engineering, Thangal Kunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kerala, India ' Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India ' Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India ' Department of Computer Science and Engineering, Thangal Kunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kerala, India

Abstract: The arterial spin labelling (ASL) MRI is a non-invasive technique to quantify cerebral blood flow (CBF) to diagnose various neurological disorders. However, low spatial resolution, which produces partial volume effects and low SNR of ASL images, hampered CBF quantification, which can be corrected with appropriate post-processing approaches. The deep learning-oriented super-resolution technique is a promising way to enhance the overall image quality of ASL-MRI. The proposed 2D CNN architecture uses a residual dense block (RDB) as the basic building unit and a mixed loss joint strategy to increase SNR and resolution while retaining edge information. The proposed model is validated on simulated ASL-MRI data generated from structural images of ADNI in terms of different metrics such as RMSE, PSNR, and SSIM and using clinical data by two independent radiologists in terms of the visual quality control score (VQCS). Experimental results show that the proposed model outperforms state-of-the-art methods.

Keywords: arterial spin labelling; ASL; cerebral blood flow; CBF; partial volume effect; super resolution; deep learning.

DOI: 10.1504/IJBET.2024.138062

International Journal of Biomedical Engineering and Technology, 2024 Vol.44 No.4, pp.324 - 347

Received: 20 May 2023
Accepted: 26 Jun 2023
Published online: 18 Apr 2024 *

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