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Published on: August 12, 2021
Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification
Zhiqing Yin1, Huay Din2, Jessie E P Sun3
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
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.
