Deep Learning MRI Halves Scan Time and Preserves Image Quality Across Routine Neuroradiologic Examinations
Shawn K Lyo, Suyash Mohan, Michael J Hoch, Vivek P Patel, Robert M Kurtz, Alvand HassankhaniAbstract
Background
MRI is a cornerstone of neuroimaging but is limited by lengthy acquisition times, which can lead to motion artifacts, patient discomfort, and delayed care. Deep learning reconstruction is an emerging technology that can offer image acquisition acceleration while maintaining image quality.
Purpose
To compare image quality and acquisition efficiency between deep learning–accelerated vs. conventional MRI across a spectrum of routine neuroradiologic examinations.
Materials and Methods
In this single-center retrospective study, 26 patients underwent imaging with a commercially available, FDA-cleared deep learning-accelerated MRI reconstruction algorithm (Deep Resolve, Siemens Healthineers), and conventional MRI on a Siemens 3 T MAGNETOM Vida scanner between October 24 and November 14, 2023. 113 sequence pairs were acquired across multiple body parts (brain [n = 28], cervical spine [n = 24], thoracic spine [n = 16], lumbar spine [n = 14], internal auditory canals [n = 5], sella [n = 5], neck [n = 5], temporomandibular joints [n = 6], brachial plexus [n = 4], and orbits [n = 6]) and sequences (T2 [n = 38], T1 [n = 30], Short Tau Inversion Recovery [n = 21], T1 Post-contrast [n = 17], T2 Fluid Attenuated Inversion Recovery [n = 5], and Proton Density [n = 2]) and evaluated by four neuroradiologists blinded to the acquisition method for image quality using a 5-point Likert scale. Acquisition parameters were extracted from DICOM metadata and statistically compared. Rater preferences and interrater reliability were assessed using nonparametric tests and intraclass correlation coefficients.
Results
Deep learning reduced mean scan time by 51.6% (95% CI: 45.7-57.7%; from 110.8 seconds to 53.7 seconds; P < .001). Image quality assessments using a Likert scale showed scores slightly above neutral for signal-to-noise ratio (mean 3.51; 95% CI: 3.44-3.58), structural delineation (mean 3.51, 95% CI: 3.44-3.56), and overall image quality (mean 3.56, 95% CI: 3.49-3.63). However, poor interrater reliability (ICC range: 0.06-0.33) showed that the observed differences were not consistent, indicating functional equivalence between conventional and deep learning images.
Conclusion
Deep learning MRI enabled substantial scan time reductions while maintaining image quality.