Current issue
Archive
Manuscripts accepted
About the journal
Editorial board
Abstracting and indexing
Contact
Instructions for authors
Ethical standards and procedures
Editorial System
Submit your Manuscript
|
1/2023
vol. 88 Musculoskeletal radiology
abstract:
Original paper
Feasibility of the fat-suppression image-subtraction method using deep learning for abnormality detection on knee MRI
Shusuke Kasuya
1
,
Tsutomu Inaoka
1
,
Akihiko Wada
2
,
Tomoya Nakatsuka
1
,
Koichi Nakagawa
3
,
Hitoshi Terada
1
1.
Department of Radiology, Toho University Sakura Medical Center, Sakura, Japan
2.
Department of Radiology, Juntendo University, Tokyo, Japan
3.
Department of Orthopaedic Surgery, Toho University Sakura Medical Center, Sakura, Japan
Pol J Radiol 2023; 88: e562-e573
Online publish date: 2023/12/08
View full text
Get citation
ENW EndNote
BIB JabRef, Mendeley
RIS Papers, Reference Manager, RefWorks, Zotero
AMA
APA
Chicago
Harvard
MLA
Vancouver
Purpose
To evaluate the feasibility of using a deep learning (DL) model to generate fat-suppression images and detect abnormalities on knee magnetic resonance imaging (MRI) through the fat-suppression image-subtraction method. Material and methods A total of 45 knee MRI studies in patients with knee disorders and 12 knee MRI studies in healthy volunteers were enrolled. The DL model was developed using 2-dimensional convolutional neural networks for generating fat-suppression images and subtracting generated fat-suppression images without any abnormal findings from those with normal/abnormal findings and detecting/classifying abnormalities on knee MRI. The image qualities of the generated fat-suppression images and subtraction-images were assessed. The accuracy, average precision, average recall, F-measure, sensitivity, and area under the receiver operator characteristic curve (AUROC) of DL or each abnormality were calculated. Results A total of 2472 image datasets, each consisting of one slice of original T1WI, original intermediate-weighted images, generated fat-suppression (FS)-intermediate-weighted images without any abnormal findings, generated FS-intermediate-weighted images with normal/abnormal findings, and subtraction images between the generated FS-intermediate-weighted images at the same cross-section, were created. The generated fat-suppression images were of adequate image quality. Of the 2472 subtraction-images, 2203 (89.1%) were judged to be of adequate image quality. The accuracies for overall abnormalities, anterior cruciate ligament, bone marrow, cartilage, meniscus, and others were 89.5-95.1%. The average precision, average recall, and F-measure were 73.4-90.6%, 77.5-89.4%, and 78.4-89.4%, respectively. The sensitivity was 57.4-90.5%. The AUROCs were 0.910-0.979. Conclusions The DL model was able to generate fat-suppression images of sufficient quality to detect abnormalities on knee MRI through the fat-suppression image-subtraction method. keywords:
deep learning, 2-dimensional convolutional neural network, fat suppression, subtraction image, knee, MRI |