Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification.

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 T₁ (NRMSE = 0.025 ± 0.003) and T₂ (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 (T₁, NRMSE = 0.057 ± 0.015; T₂, NRMSE = 0.143 ± 0.080). For renal masses with T₁ and T₂ values in close range of healthy kidney tissues, similar performance was achieved with a three-fold acceleration. For renal masses presenting distinct T₁ or T₂ 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.