Convolutional network denoising for acceleration of multi-shot diffusion MRI Journal Article
| Authors: | Alus, O.; El Homsi, M.; Golia Pernicka, J. S.; Rodriguez, L.; Mazaheri, Y.; Kee, Y.; Petkovska, I.; Otazo, R. |
| Article Title: | Convolutional network denoising for acceleration of multi-shot diffusion MRI |
| Abstract: | Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase. © 2023 Elsevier Inc. |
| Keywords: | rectal cancer; denoising; deep learning; multi-shot diffusion mri |
| Journal Title: | Magnetic Resonance Imaging |
| Volume: | 105 |
| ISSN: | 0730-725X |
| Publisher: | Elsevier Science, Inc. |
| Date Published: | 2024-01-01 |
| Start Page: | 108 |
| End Page: | 113 |
| Language: | English |
| DOI: | 10.1016/j.mri.2023.10.002 |
| PUBMED: | 37820978 |
| PROVIDER: | scopus |
| PMCID: | PMC11138874 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Ricardo Otazo -- Source: Scopus |
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