The scientific evidence of commercial AI products for MRI acceleration: a systematic review

Abstract

Objectives: This study explores the methods employed by commercially available AI products to accelerate MRI protocols and investigates the strength of their diagnostic image quality assessment.

Materials and methods: All commercial AI products for MRI acceleration were identified from the exhibitors presented at the RSNA 2023 and ECR 2024 annual meetings. Peer-reviewed scientific articles describing validation of clinical performance were searched for each product. Information was extracted regarding the MRI acceleration technique, achieved acceleration, diagnostic performance metrics, test cohort, and hallucinatory artifacts. The strength of the diagnostic image quality was assessed using scientific evidence levels ranging from "product's technical feasibility for clinical purposes" to "product's economic impact on society".

Results: Out of 1046 companies, 14 products of 14 companies were included. No scientific articles were found for four products (29%). For the remaining ten products (71%), 21 articles were retrieved. Four acceleration methods were identified: noise reduction, raw data reconstruction, personalized scanning protocols, and synthetic image generation. Only a limited number of articles prospectively demonstrated impact on patient outcomes (n = 4, 19%), and no articles discussed an evaluation in a prospective cohort of > 100 patients or performed an economic analysis. None of the articles performed an analysis of hallucinatory artifacts.

Conclusion: Currently, commercially available AI products for MRI acceleration can be categorized into four main methods. The acceleration methods lack prospective scientific evidence on clinical performance in large cohorts and economic analysis, which would help to get a better insight into their diagnostic performance and enable safe and effective clinical implementation.

Key points: Question There is a growing interest in AI products that reduce MRI scan time, but an overview of these methods and their scientific evidence is missing. Findings Only a limited number of articles (n = 4, 19%) prospectively demonstrated the impact of the software for accelerating MRI on diagnostic performance metrics. Clinical relevance Although various commercially available products shorten MRI acquisition time, more studies in large cohorts are needed to get a better insight into the diagnostic performance of AI-constructed MRI.

Keywords: Acceleration; Artificial intelligence; Evidence-based practice; Magnetic resonance imaging; Radiology.

Fig. 1

STARD diagram of article selection

Fig. 2

Schematic overview of an example of the noise reduction method for MRI acceleration. The raw MRI data acquisition is reduced and reconstructed into a noisy image using regular reconstruction. An AI-denoising model is employed to produce an image with good diagnostic quality

Fig. 3

Schematic overview of raw data reconstruction method for MRI acceleration. An image is acquired with reduced raw MRI data and completed using an AI raw data generation model. After the raw MRI data is predicted, the image is reconstructed using regular reconstruction

Fig. 4

Schematic overview of the synthetic image generation method for MRI acceleration. First, a couple of MRI images are acquired separately. These are inputs for AI to synthesize other images with different MRI contrasts

Fig. 5

Schematic overview of personalized scanning method for MRI acceleration. The image acquisition continues until the AI decision model concludes that a certain diagnosis can be made. After each data acquisition step, the AI decision model predicts whether to continue or stop the MRI acquisition