Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care : A Prospective Multicenter Multireader Study

Abstract

Objective: This prospective multicenter multireader study evaluated the performance of 40% scan-time reduced spinal magnetic resonance imaging (MRI) reconstructed with deep learning (DL).

Methods: A total of 61 patients underwent standard of care (SOC) and accelerated (FAST) spine MRI. DL was used to enhance the accelerated set (FAST-DL). Three neuroradiologists were presented with paired side-by-side datasets (666 series). Datasets were blinded and randomized in sequence and left-right display order. Image features were preference rated. Structural similarity index (SSIM) and per pixel L1 was assessed for the image sets pre and post DL-enhancement as a quantitative assessment of image integrity impact.

Results: FAST-DL was qualitatively better than SOC for perceived signal-to-noise ratio (SNR) and artifacts and equivalent for other features. Quantitative SSIM was high, supporting the absence of image corruption by DL processing.

Conclusion: DL enables 40% spine MRI scan time reduction while maintaining diagnostic integrity and image quality with perceived benefits in SNR and artifact reduction, suggesting potential for clinical practice utility.

Keywords: Artificial intelligence; Deep learning; Imaging; MRI; Spine.

Fig. 1

Consistency across datasets. Sagittal T2 (left to right): SOC, FAST, FAST-DL with acquisition times. Blinded readers found no variations in image integrity (morphology/pathology) across the datasets. A tiny incidental intrathecal schwannoma (white arrow) at upper L3 level maintains excellent visual conspicuity across all three datasets

Fig. 2

Multisequence imaging. SOC (a) and FAST-DL (b) Representative patients and acquisition times