Accelerated diffusion-weighted MRI of rectal cancer using a residual convolutional network Journal Article
| Authors: | Mohammadi, M.; Kaye, E. A.; Alus, O.; Kee, Y.; Golia Pernicka, J. S.; El Homsi, M.; Petkovska, I.; Otazo, R. |
| Article Title: | Accelerated diffusion-weighted MRI of rectal cancer using a residual convolutional network |
| Abstract: | This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1–L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer. © 2023 by the authors. |
| Keywords: | rectal cancer; diffusion-weighted mri; denoising; deep learning |
| Journal Title: | Bioengineering |
| Volume: | 10 |
| Issue: | 3 |
| ISSN: | 2306-5354 |
| Publisher: | MDPI |
| Date Published: | 2023-03-01 |
| Start Page: | 359 |
| Language: | English |
| DOI: | 10.3390/bioengineering10030359 |
| PROVIDER: | scopus |
| PMCID: | PMC10045764 |
| PUBMED: | 36978750 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding author is MSK author Ricardo Otazo -- Source: Scopus |
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