Abstract
BACKGROUND: 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. PURPOSE: (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. STUDY TYPE: Retrospective and based on internal reference data. SUBJECTS: Development set was 36 healthy subjects and 20 patients with renal masses. The testing set: 4 healthy subjects and 16 patients. FIELD STRENGTH/SEQUENCE: 3T, Steady-State Free Precession (FISP)-based MRF. ASSESSMENT: 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. STATISTICAL TESTS: Paired Student's t-test. p < 0.05 was considered statistically significant. RESULTS: Accurate quantification in both T(1) (NRMSE = 0.025 ± 0.003) and T(2) (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(1), NRMSE = 0.057 ± 0.015; T(2), NRMSE = 0.143 ± 0.080). For renal masses with T(1) and T(2) values in close range of healthy kidney tissues, similar performance was achieved with a three-fold acceleration. For renal masses presenting distinct T(1) or T(2) 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). CONCLUSION: 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. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.