Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in or studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through and studies. For the study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient ( ), pseudodiffusion coefficient ( ), and percentage of water molecules in micro perfusion within 1 voxel ( ) with and without DLR; the limits of agreement of each parameter were determined through a Bland-Altman analysis. The study identified no significant differences between the ADC values for DWI with and without DLR ( > 0.05), and the CV% was significantly different for DWI with and without DLR ( < 0.05) when values ≥250 s/mm were used. The study revealed that and with and without DLR were significantly different ( < 0.001). The limits of agreement of the ADC, , and values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10 , 0.00 ± 0.06 × 10 , and 1.13 ± 4.04 × 10-3 mm /s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as -0.01 ± 0.07. Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher values. It has some effect on and f values in the IVIM index evaluation, but ADC and values are less affected by DLR.