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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...

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Autores principales: Mohammadi, Mohaddese, Kaye, Elena A., Alus, Or, Kee, Youngwook, Golia Pernicka, Jennifer S., El Homsi, Maria, Petkovska, Iva, Otazo, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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