Visual assessment of AI-reconstructed knee MRI: A pilot study

Abstract

INTRODUCTION: Artificial intelligence is increasingly influencing medical imaging workflows by enhancing image quality and reducing acquisition time. The purpose of this study is to evaluate the use of artificial intelligence (AI) reconstruction methods for knee magnetic resonance imaging (MRI) investigations. METHODS: An exploratory comparison was performed between AI-enhanced and standard 3T knee MRI protocols. Sequences included sagittal T1-weighted (T1w) fast spin echo (FSE), proton density-weighted (PDw) FSE fat saturation (FS) and T2w FSE, with additional PDw FSE FS axial and coronal views. Parameters were adjusted to improve image quality (IQ) and shorten scan duration. The optimised AI protocol was tested on ten healthy volunteers. Three MRI experts independently assessed images visually using ViewDEX. Visual grading analysis (VGA), inter-observer agreement (Kappa), and visual grading characteristics (VGC) were utilised for evaluation. RESULTS: 0.76-0.81, p ≤ 0.05). CONCLUSION: This study indicates that incorporating AI into knee MRI protocols can substantially enhance overall image quality while simultaneously reducing acquisition time by 36.9 %. However, further research is needed to reinforce these findings through clinical validation. IMPLICATION FOR PRACTICE: AI can reduce scan time without lowering image quality, improving workflow and patient throughput. Understanding how acquisition parameters affect image quality and artefacts is crucial when integrating AI reconstruction into protocols.

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