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An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and
Xin-Xiang Zhou1, Xin-Yu Wang1, En-Hui Liu1
1Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China.
Background:
Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel level is still limited.
Purpose:
To develop an unsupervised deep learning approach for voxel-wise dynamic-exponential IVIM modeling and parameter estimation in the liver.
Study Type:
Prospective.
Population:
Ten healthy subjects (4 males; age 28 ± 6 years).
Field Strength/Sequence:
Single-shot spin-echo echo planar imaging (SE-EPI) sequence with monopolar diffusion-encoding gradients (12 b-values, 0-800 seconds/mm2 ) at 3.0 T.
Assessment:
The proposed deep neural network (DNN) was separately trained on simulated and in vivo hepatic IVIM datasets. The trained networks were compared to the approach combining least squares with Akaike information criterion (LSQ-AIC) in terms of dynamic-exponential modeling accuracy, inter-subject coefficients of variation (CVs), and fitting residuals on the simulated subsets and regions of interest (ROIs) in the left and right liver lobes. The ROIs were delineated by a radiologist (H.-X.Z.) with 7 years of experience in MRI reading.
Statistical Tests:
Comparisons between approaches were performed with a paired t-test (normality) or a Wilcoxon rank-sum test (nonnormality). P < 0.05 was considered statistically significant.
Results:
In simulations, DNN gave significantly higher accuracy (91.6%-95.5%) for identification of bi-exponential decays with respect to LSQ-AIC (79.7%-86.8%). For tri-exponential identification, DNN was also superior to LSQ-AIC despite not reaching a significant level (P = 0.08). Additionally, DNN always yielded comparatively low root-mean-square error for estimated parameters. For the in vivo IVIM measurements, inter-subject CVs (0.011-0.150) of DNN were significantly smaller than those (0.049-0.573) of LSQ-AIC. Concerning fitting residuals, there was no significant difference between the two approaches (P = 0.56 and 0.76) in both the simulated and in vivo studies.
Data Conclusion:
The proposed DNN is recommended for accurate and robust dynamic-exponential modeling and parameter estimation in hepatic IVIM imaging.
Level Of Evidence:
2 TECHNICAL EFFICACY: Stage 1.
