{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:37:55Z","timestamp":1771076275691,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health\/National Cancer Institute","award":["R01-CA244532"],"award-info":[{"award-number":["R01-CA244532"]}]},{"name":"National Institutes of Health\/National Cancer Institute","award":["P30-CA008748"],"award-info":[{"award-number":["P30-CA008748"]}]},{"name":"Department of Radiology at Memorial Sloan Kettering Cancer Center","award":["R01-CA244532"],"award-info":[{"award-number":["R01-CA244532"]}]},{"name":"Department of Radiology at Memorial Sloan Kettering Cancer Center","award":["P30-CA008748"],"award-info":[{"award-number":["P30-CA008748"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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\u2013L2 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 \u00b1 0.5) and fourfold acceleration with higher image quality (3.0 \u00b1 0.6) than the clinical standard (2.5 \u00b1 0.8) for improved diagnosis of rectal cancer.<\/jats:p>","DOI":"10.3390\/bioengineering10030359","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T06:57:11Z","timestamp":1678777031000},"page":"359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network"],"prefix":"10.3390","volume":"10","author":[{"given":"Mohaddese","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena A.","family":"Kaye","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3785-589X","authenticated-orcid":false,"given":"Or","family":"Alus","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngwook","family":"Kee","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1076-7948","authenticated-orcid":false,"given":"Jennifer S.","family":"Golia Pernicka","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8483-3653","authenticated-orcid":false,"given":"Maria","family":"El Homsi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4373-6494","authenticated-orcid":false,"given":"Iva","family":"Petkovska","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-4930","authenticated-orcid":false,"given":"Ricardo","family":"Otazo","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"},{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer Statistics, 2021","volume":"71","author":"Siegel","year":"2021","journal-title":"CA A Cancer J. 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