Year: 2023
Type: article
Abstract: Background: Magnetic Resonance Imaging (MRI) is a widely applied medical imaging technology for various clinical applications. However, one major drawback is the long scan time, which results in significant artifacts due to patient’s voluntary and non-voluntary movement. This work aimed to expedite the scan time by reconstructing diagnostic MRI images from an undersampled k-space. Methods: To solve this ill-posedness, wavelet sparse transform was introduced to provide a successful model for optimizing and regularizing the inverse problem. It offers a complete representation of the image and accurate analysis at different spatial orientations. To that end, the implicit sparsity in MRI images is employed to undersampled k-space. In this study, we exploited MLTL, which is an iterative multi-level wavelet algorithm, to reduce MRI acquisition time and improve reconstruction. MRI brain experiment was carried out using multi-channels coil model to evaluate the parallel imaging acquisition. To evaluate the MLTL algorithm with this multi-channel coil model, different undersampling implementations were used. The acceleration factors were set to 5 and 10. The performance of the proposed algorithm is demonstrated and compared with that of the traditional reconstruction method (IFFT) and another competitive algorithm (FISTA). Results: The final numerical simulation showed an outperformance of the proposed MLTL algorithm in terms of the accuracy of the reconstructed image and acceleration of the reconstruction time. MLTL reconstructed high-resolution images from the undersampled measurements. It maintained the details and recovered the structures of the image more accurately. Meanwhile, IFFT showed very noisy images and FISTA provided blurry images, both of which cannot be used clinically. The MSE and PSNR were calculated for the three methods, and the MLTL-reconstructed images showed less error and the best image resolution. Conclusions: MLTL can be successfully applied to undersampled k-space reconstruction, which has the potential to accelerate clinical acquisition
Authors: Aladwani, Fatemah, Alessandro Perelli
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: University of Dundee, Stryker, Wellcome Trust, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, University of Glasgow, University of Aberdeen, King Abdulaziz University, Universiti Sains Malaysia, Siemens Healthineers, Medical Research Council, Engineering and Physical Sciences Research Council
Awards: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents (SOCIAL), G201556457, G2133464415, G2569584418, G2694014206, UK Health Informatics Research Network, MICA: InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch: PICTURES, DTP 2016-2017 University of Dundee, Myocardial fibrosis and left ventricular remodelling in cardiovascular disease, G8965059024
OpenAlex ID: W7112138570