Title: Brain image compression and reconstruction system using deep learning

Authors: S. Seenuvasamurthi; S. Ashok; B. Shankarlal; A. Mohamed Abbas; Ashok Vajravelu

Addresses: RAAK College of Engineering and Technology, Puducherry, India ' Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan and Dr. Sakunthala Engineering College, Avadi, Chennai, India ' Department of Electronics and Communication Engineering, Perunthalaivar Kamarajar Institute of Engineering and Technology, Karaikal, India ' Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan and Dr. Sakunthala Engineering College, Avadi, Chennai, India ' Department of Electronics, Faculty of Electrical Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

Abstract: New perspectives on brain structure and function can only be gained through the rapid advancement of brain imaging technology. Throughout history, this has been the case. It is common practise in medicine to employ image processing in the early stages of diagnosis and treatment. In classification and segmentation tasks, deep neural networks (DNNs) have so far proven to be exceptional. Functional ultrasound (fUS) is a novel imaging technique that enables the observation of neuronal activity across the brain in awake, ambulatory rats. To achieve adequate blood flow sensitivity in the brain microvasculature, fUS relies on lengthy ultrasonic data collecting at high frame rates, placing a load on the sampling and processing hardware. Parallel MRI is introduced in broad terms, with an emphasis on the classical understanding of image space and k-space-based techniques.

Keywords: accelerated MRI; parallel imaging; iterative image reconstruction; numerical optimisation; machine learning; deep learning.

DOI: 10.1504/IJMEI.2024.140799

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.401 - 413

Received: 02 Jan 2022
Accepted: 03 Apr 2022
Published online: 03 Sep 2024 *

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