Improved accuracy of apparent diffusion coefficient quantification using a fully automatic noise bias compensation method: Preliminary evaluation in prostate diffusion weighted imaging.

Abstract

Noise in diffusion magnetic resonance imaging can introduce bias in apparent diffusion coefficient (ADC) quantification. Previous studies proposed methods that are site-specific techniques as research tools with limited availability and typically require manual intervention, not completely ready to use in the clinical environment. The purpose of this study was to develop a fully automatic computational method to correct noise bias in ADC quantification and perform a preliminary evaluation in the clinical prostate diffusion weighted imaging (DWI). Using a pseudo replica approach for the noise map calculation as well as a direct mapping and a stepwise Chebychev polynomial modelling approach for the ADC fitting, a fully automatic noise-bias-compensated ADC calculation method was proposed and implemented both on the scanner and offline. The proposed method was validated in a computer simulation and a standardized diffusion phantom with ground-truth values. Two in vivo studies were performed to evaluate the proposed method in the clinical environment. The first in vivo study performed acquisitions using a clinically routine prostate DWI protocol on 29 subjects to evaluate the consistency between simulated and empirical results. In the second in vivo study, prostate ADC values of 14 subjects were compared between data acquired with external coils only and reconstructed with the proposed method vs. acquired with external combined with endorectal coils and reconstructed with the conventional method. In statistical analyses, p < 0.05 was regarded as significantly different. In the computer simulation, the proposed method showed smaller error percentage than the other methods and was significantly different (p < 2.2 × 10-16). With low signal-to-noise ratio (SNR), the conventional method underestimated ADC values compared to the ground truth values of the diffusion phantom, while the results of the proposed method were more consistent with the ground truth values. Statistical analyses showed no significant differences between measured and simulated results in the first in vivo study (p = 0.5618). Data from the second in vivo study showed that agreement between ADC measured with external coils only and combined coils was improved for the proposed method (mean bias: 0.04 × 10-3 mm2/s, 95% confidence interval (CI) = [-0.01, 0.09] × 10-3 mm2/s, p = 0.187), compared to the conventional method (mean bias: -0.12 × 10-3 mm2/s, 95% CI = [-0.17, -0.06] × 10-3 mm2/s, p < 0.0001). The proposed method compensates noise bias in low-SNR diffusion-weighted acquisitions and results show improved ADC quantification accuracy in the prostate. This method may be suitable for both clinical imaging and research utilizing ADC quantification.