Abstract
Purpose To synthesize existing evidence on artificial intelligence (AI) applications for reducing magnetic resonance (MR) imaging acquisition time, evaluate their impact on image quality, and identify challenges in clinical implementation.
Method A systematic literature review was conducted using PubMed, CINAHL, and Scopus including peer reviewed, English-language articles published between 2020 and 2026. Both qualitative and quantitative primary research were eligible and had to be on AI use in MR processes. Findings were analyzed through thematic synthesis, and the quality of each study was appraised using relevant critical appraisal tools.
Results Three major themes emerged: AI methods in MR procedures, perceived effectiveness of AI in improving efficiency in imaging, and implementation issues.
Discussion The findings revealed a growing application of AI in MR; however, a clear link between optimized workflow and actual reductions in acquisition time has not been established. Although qualitative evidence is abundant, most studies focus on improved workflow efficiency and reporting speed rather than directly measuring acquisition time. AI is primarily used through machine learning and computer-aided detection systems, which enhance workflow performance, but their impact on acquisition time remains unclear. Key barriers include variability in performance, lack of standardization, and limited clinician confidence in AI tools.
Conclusion AI-driven MR acceleration is a promising advancement, but further research is needed to address implementation barriers and support widespread clinical adoption.
DOI
https://doi.org/10.52519/00258
Graduation Date
Summer 8-7-2026
Document Type
Thesis
Degree Name
Master of Science in Radiologic Science
Program
The School of Health Professions
Faculty Advisor
Kevin R. Clark
Committee Member
Jessyca B. Wagner
Director, Graduate Program
William A. Undie
Dean
Kimberly Hoggatt Krumwiede
Recommended Citation
Arief, F. M. Reducing MR Acquisition Times Using Artificial Intelligence: A Systematic Review. [thesis]. University of Texas MD Anderson Cancer Center; 2026. https://doi.org/10.52519/00258
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