Optimizing MRI Scan Time with AI-Powered Reconstruction and Denoising Techniques

Abstract

MRI is a popular diagnostic procedure because of its high soft-tissue contrast and non-invasiveness. One of such limitations is a long scan time, which causes patient discomfort and can facilitate motion artifacts, while having an impact on the throughput. State-of-the-art artificial intelligence (AI) developments have brought forth robust reconstruction and denoising methods that can speed up MRI acquisition without the loss, or even with the improvement of image quality. This work investigates the use of AI-based algorithms for optimal scan time in MRI through accelerated image reconstruction combined with advanced denoising methods. We summarize current methods and analyse their clinical utility, noise suppression efficiency, artifact reduction effectiveness, and diagnostic reliability. Finally, we discuss potential research needs and practical implications for adopting AI solutions in clinical routines. Through acceleration and denoising with AI based reconstruction, MRI enables and allows faster high-quality imaging that drives better patient care and operational effectiveness for healthcare systems