
Faculty, Staff and Student Publications
Publication Date
5-1-2025
Journal
Journal of Applied Clinical Medical Physics
Abstract
Purpose: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Methods: The study included 50 patients with rectal cancer who underwent rectal MRI consecutively between July 7, 2020 and January 20, 2021 using a T2W 3D FSE sequence with DLR and conventional reconstruction. Three radiologists reviewed the two sets of images, scoring overall SNR, motion artifacts, and overall image quality on a 3-point scale and indicating clinical preference for DLR or conventional reconstruction based on those three criteria as well as image characterization of bowel wall layer definition, tumor invasion of muscularis propria, residual disease, fibrosis, nodal margin, and extramural venous invasion.
Results: Image quality was rated as moderate or good for both DLR and conventional reconstruction for most cases. DLR was preferred over conventional reconstruction in all of the categories except for bowel wall layer definition.
Conclusion: Both conventional reconstruction and DLR provide acceptable image quality for T2W 3D FSE imaging of rectal cancer. DLR was clinically preferred over conventional reconstruction in almost all categories.
Keywords
Humans, Deep Learning, Rectal Neoplasms, Male, Imaging, Three-Dimensional, Female, Middle Aged, Aged, Magnetic Resonance Imaging, Image Processing, Computer-Assisted, Adult, Retrospective Studies, Aged, 80 and over, 3‐dimensional magnetic resonance imaging, deep learning reconstruction, rectal cancer
DOI
10.1002/acm2.70031
PMID
39976552
PMCID
PMC12059301
PubMedCentral® Posted Date
2-20-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Health and Medical Physics Commons, Medical Genetics Commons, Oncology Commons