Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

Overview

abstract

PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD: A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) C_a-25min, using DCE-MRI data of 25 min with individually sampled AIF (C_a); (ii) C_a-10min, using truncated 10min data with AIF (C_a); and (iii) C_p-10min, using truncated 10 min data with CIF (C_p). The PS estimates from the C_a-25min method were used as reference standard values to assess the accuracy of the C_a-10min and C_p-10min methods in estimating the PS values. RESULTS: When compared to the reference method(C_a-25min), the C_a-10min and C_p-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10^-4 min^-1) with the C_a-10min, while it was reduced to 1.63 ± 2.25 (x10^-4 min^-1) with the C_p-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the C_p-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS: We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.