Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification.
Authors
Affiliations (5)
Affiliations (5)
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
- Department of Mathematics, Case Western Reserve University, Cleveland, Ohio, USA.
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract
Magnetic Resonance Fingerprinting (MRF) is a technique that can provide rapid quantification of multiple tissue properties. Deep learning may potentially contribute to an accelerated acquisition of MRF. (1) To develop a deep learning method to accelerate the acquisition for kidney MRF; (2) to evaluate its performance in healthy subjects and patients with renal masses. Retrospective and based on internal reference data. Development set was 36 healthy subjects and 20 patients with renal masses. The testing set: 4 healthy subjects and 16 patients. 3T, Steady-State Free Precession (FISP)-based MRF. Quantification accuracy was evaluated in healthy kidneys and renal masses using quantitative metrics including normalized root-mean-square error (NRMSE) calculated based on reference maps generated using the standard template matching approach with all acquired MRF time frames. Paired Student's t-test. p < 0.05 was considered statistically significant. Accurate quantification in both T<sub>1</sub> (NRMSE = 0.025 ± 0.003) and T<sub>2</sub> (NRMSE = 0.053 ± 0.010) maps was obtained for healthy kidney tissues with a three-fold acceleration (576 time frames, 5 s of scan time), outperforming the template matching approach (T<sub>1</sub>, NRMSE = 0.057 ± 0.015; T<sub>2</sub>, NRMSE = 0.143 ± 0.080). For renal masses with T<sub>1</sub> and T<sub>2</sub> values in close range of healthy kidney tissues, similar performance was achieved with a three-fold acceleration. For renal masses presenting distinct T<sub>1</sub> or T<sub>2</sub> values, more MRF time frames were required to provide accurate tissue quantification. No significant difference was noticed in tissue/tumor quantification between neural networks trained using only healthy subjects versus a mixed dataset with healthy subjects and patients (p > 0.05). A deep learning-based method was developed to accelerate acquisition without compromising the accuracy of relaxation time mapping using kidney MRF. These results demonstrate reliable tissue quantification with at least a two-fold acceleration for both healthy kidneys and renal masses with various subtypes and histopathological grades. 4. Stage 1.