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Deep Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors
Zeyu Liu1,2, Vishal Patel1, Xiangzhi Zhou1
1From the Department of Radiology (Z.L., V.P., X.Z., S.T., T.Y., E.M.W., V.G., E.H.M.), Mayo Clinic, Jacksonville, Florida.
Background And Purpose:
Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ultra-high-field MRI. The objective of this study was to evaluate the performance of a prototype DL-based image reconstruction technique in 7T MRI of the brain utilizing magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) and sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) acquisitions, in comparison with reconstructions in conventional compressed sensing and controlled aliasing in parallel imaging techniques.
Materials And Methods:
This retrospective study involved 60 patients who underwent 7T brain MRI between June 2024 and October 2024, comprising 30 patients with MP2RAGE data and 30 patients with SPACE FLAIR data. Each set of raw data was reconstructed with DL-based reconstruction and conventional reconstruction. Image quality was independently assessed by 2 neuroradiologists by using a 5-point Likert scale, which included overall image quality, artifacts, sharpness, structural conspicuity, and noise level. Interobserver agreement was determined by using top-box analysis. Contrast-to-noise ratio (CNR) and noise levels were quantitatively evaluated and compared by using the Wilcoxon signed-rank test.
Results:
DL-based reconstruction resulted in a significant increase in overall image quality and a reduction in subjective noise level for both MP2RAGE and SPACE FLAIR data (all P < .001), with no significant differences in image artifacts (all P > .05). When compared with standard reconstruction, the implementation of DL-based reconstruction yielded an increase in CNR of 49.5% (95% CI, 33.0%-59.0%) for MP2RAGE data and 90.6% (95% CI, 73.2%-117.7%) for SPACE FLAIR data, along with a decrease in noise of 33.5% (95% CI, 23.0%-38.0%) for MP2RAGE data and 47.5% (95% CI, 41.9%-52.6%) for SPACE FLAIR data.
Conclusions:
DL-based reconstruction of 7T MRI significantly enhanced image quality compared with conventional reconstruction without introducing image artifacts. The achievable high acceleration factors have the potential to substantially improve image quality and resolution in 7T MRI.
