Evaluation of accelerated whole-body diffusion weighted imaging with deep learning reconstruction in patients with metastatic prostate cancer: assessment of image quality and ADC estimates.
Authors
Affiliations (1)
Affiliations (1)
- MRI Unit, The Royal Marsden NHS Foundation Trust, London, SM2 5PT, UK, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, SW3 6JB,
Abstract
ObjectivesTo evaluate the impact of an accelerated whole-body diffusion-weighted MRI (WB-DWI) protocol using deep learning (DL) reconstruction on image quality and apparent diffusion coefficient (ADC) quantification in patients with metastatic prostate cancer. MethodsThis single-centre prospective study involved two patient cohorts undergoing standard-of-care WB-MRI at 1.5T. The DL WB-DWI acquisition used fewer signal averages and higher parallel imaging acceleration, reconstructed with a research application DL package based on a variational network. In Cohort 1 (n = 10), qualitative assessment of image quality was performed across four anatomical stations using a 4-point Likert scale by two radiologists, blinded to the DWI protocol. In Cohort 2 (n = 20), ADC values were evaluated in hypercellular focal bone metastases (as determined by a radiologist) and compared between the standard and accelerated DL WB-DWI protocols. Image quality scores were analysed using Wilcoxon signed-rank tests (Bonferroni-corrected), and ADC agreement was evaluated via Bland-Altman analysis. ResultsAccelerated DL WB-DWI reduced WB-DWI acquisition time by 37% without compromising image quality. Radiologists consistently rated both protocols as good-to-excellent across all image quality metrics. Median ADC estimates in lesions showed no significant difference between protocols; interquartile range of ADC estimates also showed no significant difference. ConclusionsAccelerated DL WB-DWI enables 37% scan time reduction without degrading image quality or affecting ADC estimates of lesions in patients with metastatic prostate cancer. Advances in knowledgeDL-based reconstruction enables faster WB-DWI acquisition without compromising image quality or ADC quantification, supporting clinical implementation in metastatic prostate cancer staging and therapy assessment.