Enhancing Cerebral Blood Flow Quantification: A Comprehensive Review of Denoising, Artifact Correction, and Simulation in Arterial Spin Labeling MRI.
Authors
Affiliations (4)
Affiliations (4)
- Department of Computer Science and Engineering, Thangal Kunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kollam 691005, India.
- Department School of Computer and Information Science, Vidya Academy of Science and Technology Technical Campus, APJ Abdul Kalam Technological University, Trivandrum 695602, India.
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum 695011, India.
- Centre for Engineering Research in Intelligent Sensors and Systems, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK.
Abstract
Arterial Spin Labeling (ASL) Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique used to quantify cerebral blood flow (CBF) by using magnetically labeled arterial blood water as an endogenous tracer. Although ASL eliminates the need for exogenous contrast agents, its widespread clinical use is limited by several challenges, including low Signal-to-Noise Ratio (SNR), susceptibility to motion, and various imaging artifacts. To address these limitations, both traditional denoising techniques and Machine Learning (ML)/Deep Learning (DL)-based approaches have been developed to improve the reliability of ASL by reducing noise, correcting artifacts, and enhancing image quality. In addition, the generation of simulated ASL datasets has become an important strategy for training and validating novel methods when sufficient clinical data are unavailable. This review examines conventional image-processing techniques together with modern machine learning and deep learning approaches developed to improve ASL image quality through denoising and enhancement. It also discusses the major artifacts that affect ASL acquisition and summarizes the simulation methodologies used for the development and evaluation of new algorithms.