Abstract Presentations

Abstract Presentations

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2018 AuntMinnie.com Roadie Award:
Hottest Artificial Intelligence Abstract

aunt minnie RSNA 2018 200In this presentation, Dr. Lawrence N. Tanenbaum, M.D, FACR demonstrates the capabilities of iQMR, a machine learning-based image reconstruction algorithm allowing to sharply lower scanning times for brain and lumbar MRI studies. The algorithm enables MR scans to be performed with a lower cumulative signal, thus reduce MRI scanning time by approximately 30% while producing equivalent overall image quality.

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APPLICATION OF MACHINE LEARNING-BASED ITERATIVE IMAGE RECONSTRUCTION ALGORITHMS FOR MRI SCAN TIME REDUCTION IN BRAIN AND SPINE
LAWRENCE N. TANENBAUM, MD, FACR
ASNR ANNUAL MEETING 2019, BOSTON

asnr 2019The study aims to evaluate the scan time shortening potential of iQMR, a 3D image enhancement algorithm, in brain, lumbar spine and cervical spine MRI. iQMR provides image enhancement and increased Signal-to-Noise Ratio (SNR), thus permitting substantial scan time reduction, without changing the imaging contrast, resolution or scanner hardware or software.

The study reports findings of 73 patients (37 brain, 19 lumbar spine, 17 cervical spine) who underwent MRI scans on 4 different scanners at 3 sites. Image quality, diagnostic quality, artifacts’ appearance and brain grey-white (GW) matter differentiation of iQMR-processed images acquired by short MRI protocols were compared to images acquired by the site’s standard MRI protocols.

 

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