Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image Filters.
prospective_cohort · Level II
Where this comes from
- Record sourced from PubMed, PMID 34617028.
- Also identified by DOI 10.1148/ryai.2021200279 and PMC identifier 8489468.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA<sub>1</sub>]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. 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 NOA<sub>1</sub> and NOA<sub>9</sub> 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 NOA<sub>1</sub> (NOA<sub>1-DNIF</sub>) images were compared with NOA<sub>1</sub> images and clinical NOA<sub>16</sub> 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. The model visually improved the quality of NOA<sub>1</sub> images in all test patients, with the majority of NOA<sub>1-DNIF</sub> and NOA<sub>16</sub> images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA<sub>1-DNIF</sub> images of bone disease deviated from those within NOA<sub>9</sub> 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 NOA<sub>1-DNIF</sub> images of MPM deviated from those measured by using clinical-standard images (NOA<sub>12</sub>) by 3.7% (range, 0.2%-10.6%). Clinical-standard images were generated from subsampled images by using a DNIF.<b>Keywords:</b> Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.