Image quality assessment and longitudinal quality monitoring of clinically-applied AI-based reconstructions in MRI of rectal cancer

Motivation: With increasing AI adoption in MR-reconstructions, robust quality assessment becomes paramount. This study aims to ensure that AI-techniques meet clinical requirements at implementation and longitudinally. Goal(s): 1) Compare image quality of AI-imaging with standard techniques in anorectal cancer. 2) Develop longitudinal quality control (QC) assessments capable of detecting changes in AI-reconstructions without resource-intensive evaluations. Approach: A prospective study involving 40 patients utilised radiologist scoring and quantitative image-quality-metrics (IQMs). Retrospective reconstructions gauged sensitivity of IQMs to reconstruction pipeline changes. Results: AI-reconstructions demonstrated >50% time savings with improved image quality. Feasibility of quantitative-IQMs for assessing AI-reconstructions is established, providing a practical solution for ongoing QC. Impact: There is a need to develop QC assessments offering performance monitoring for AI-based reconstructions in diverse clinical settings. The study presents feasible ways to support integration of AI-imaging into clinical practice, including resource-efficient quantitative image quality assessments.