Accelerating whole-body diffusion-weighted mri with deep learning-based denoising image filters

Abstract

Purpose: To use deep learning to improve the image quality of subsampled images (number of acquisitions=1 [NOA1]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. Materials and Methods: Both retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA1 and NOA9 images (acquisition period, 2015-2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA1 (NOA1-DNIF) images were compared with NOA1 images and clinical NOA16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015-2017) to demonstrate feasibility in other body regions. Results:  The model visually improved the quality of NOA1 images in all test patients, with the majority of NOA1-DNIF and NOA16 images being graded as either “average” or “good” across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA1-DNIF images of bone disease deviated from those within NOA9 images by an average of 1.9% (range, 1.1%-2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA12) by 3.7% (range, 0.2%-10.6%). Conclusion: Clinical-standard images were generated from subsampled images by using a DNIF. Supplemental material is available for this article. Abbreviations ADC=apparent diffusion coefficient, DNIF=deep learning-based denoising image filter, DWI=diffusion-weighted MRI, MAE=mean absolute error, MPM=malignant pleural mesothelioma, MSE=mean-squared error, NOA=number of acquisitions, NOA1-DNIF=DNIF-processed NOA1, PSNR=peak SNR, RDM=relative difference of means, SNR=signal-to-noise ratio, SSIM=structural similarity, WBDWI=whole-body DWI Summary A developed model, called quickDWI, enabled accelerated acquisition protocols for whole-body diffusion-weighted MRI of metastatic prostate, breast, and myeloma bone disease by using deep learning, resulting in images that were comparable with clinical-standard images. Key Points n A U-Net-based architecture can successfully reduce the magnitude of noise present in diffusion-weighted MR images; the average mean absolute error of all validation images acquired at b values of 50, 600, and 900 sec/mm2 was reduced from 0.87˟10-3 to 0.53˟10-3. n The algorithm significantly improved the radiologic image quality of fast but noisy whole-body MRI data in 22 patients with bone disease (P