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Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network
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 a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045764/ https://www.ncbi.nlm.nih.gov/pubmed/36978750 http://dx.doi.org/10.3390/bioengineering10030359 |
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author | Mohammadi, Mohaddese Kaye, Elena A. Alus, Or Kee, Youngwook Golia Pernicka, Jennifer S. El Homsi, Maria Petkovska, Iva Otazo, Ricardo |
author_facet | Mohammadi, Mohaddese Kaye, Elena A. Alus, Or Kee, Youngwook Golia Pernicka, Jennifer S. El Homsi, Maria Petkovska, Iva Otazo, Ricardo |
author_sort | Mohammadi, Mohaddese |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10045764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100457642023-03-29 Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network Mohammadi, Mohaddese Kaye, Elena A. Alus, Or Kee, Youngwook Golia Pernicka, Jennifer S. El Homsi, Maria Petkovska, Iva Otazo, Ricardo Bioengineering (Basel) Article 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. MDPI 2023-03-14 /pmc/articles/PMC10045764/ /pubmed/36978750 http://dx.doi.org/10.3390/bioengineering10030359 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mohammadi, Mohaddese Kaye, Elena A. Alus, Or Kee, Youngwook Golia Pernicka, Jennifer S. El Homsi, Maria Petkovska, Iva Otazo, Ricardo Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title | Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title_full | Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title_fullStr | Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title_full_unstemmed | Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title_short | Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network |
title_sort | accelerated diffusion-weighted mri of rectal cancer using a residual convolutional network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045764/ https://www.ncbi.nlm.nih.gov/pubmed/36978750 http://dx.doi.org/10.3390/bioengineering10030359 |
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