Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic parameters derived from diffusion-weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion in brain tumor imaging remains unclear. PURPOSE: To evaluate the impact of DLR on quantitative parameters derived from DWI and DSC in patients with brain tumors. STUDY TYPE: Retrospective. SUBJECTS: Sixty-two patients (33 male) with post-radiation brain metastasis. FIELD STRENGTH/SEQUENCE: 3.0 T; T2, FLAIR, T1WI, DWI, DSC perfusion, and contrast-enhanced T1WI. ASSESSMENT: DWI and DSC images were reconstructed at three DLR levels (high, medium, and low). Agreement between original and DLR images for apparent diffusion coefficient (ADC), cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and time to peak (TTP) was assessed using the coefficient of variation, repeatability coefficient (RC), and concordance correlation coefficient. For DSC time-series, signal-to-noise ratio, root mean square error (RMSE), and mean absolute error (MAE) were computed within tumor masks. DWI comparisons used mean signal intensity at b = 0 and b = 1000. STATISTICAL TESTS: Paired t-tests compared ADC, relative CBV, and DWI signals. RMSE and MAE were compared using repeated-measures analysis of variance. Significance was set at p < 0.05. RESULTS: ADC (p = 0.955-0.979) and CBV (p = 0.341-0.708), CBF (p = 0.684-0.983), and MTT (p = 0.403-0.971) values showed no significant differences between original and DLR images, while high-level DLR showed significantly higher TTP than original images. RCs demonstrated high reproducibility across DLR levels for ADC (21.78-22.20), CBV (0.88-0.96), CBF (27.98-34.18), MTT (1.26-1.50), and TTP (3.40-3.99). DSC analysis showed the best noise reduction with high-level DLR (lowest RMSE, 254.62 and MAE, 253.18 of DSC) without compromising CBV quantification. DATA CONCLUSION: DLR effectively reduced noise in DWI and DSC while preserving quantitative accuracy of ADC, CBV, CBF, and MTT. DLR may enable robust physiological MRI when applied in brain tumor imaging. TECHNICAL EFFICACY: Stage 3. Deep learning reconstruction (DLR) is a new technology that improves MRI image quality and may shorten scan time. This study evaluated whether DLR changes important quantitative measurements used to assess brain tumors from diffusion‐weighted imaging and dynamic susceptibility contrast perfusion parameters. In 62 patients, DLR did not change most quantitative measurements, especially apparent diffusion coefficient and blood volume. Only the time‐to‐peak measure showed a small difference at the highest DLR level. Overall, these findings suggest that DLR can be safely used to enhance image quality and potentially reduce scan time while preserving reliable quantitative information for brain tumor MRI.