In:
Investigative Radiology, Ovid Technologies (Wolters Kluwer Health), Vol. 58, No. 2 ( 2023-2), p. 166-172
Kurzfassung:
The aim of this study was to develop and validate a deep learning-based algorithm (DLA) for automatic detection and grading of motion-related artifacts on arterial phase liver magnetic resonance imaging (MRI). Materials and Methods Multistep DLA for detection and grading of motion-related artifacts, based on the modified ResNet-101 and U-net, were trained using 336 arterial phase images of gadoxetic acid-enhanced liver MRI examinations obtained in 2017 (training dataset; mean age, 68.6 years [range, 18–95]; 254 men). Motion-related artifacts were evaluated in 4 different MRI slices using a 3-tier grading system. In the validation dataset, 313 images from the same institution obtained in 2018 (internal validation dataset; mean age, 67.2 years [range, 21–87] ; 228 men) and 329 from 3 different institutions (external validation dataset; mean age, 64.0 years [range, 23–90]; 214 men) were included, and the per-slice and per-examination performances for the detection of motion-related artifacts were evaluated. Results The per-slice sensitivity and specificity of the DLA for detecting grade 3 motion-related artifacts were 91.5% (97/106) and 96.8% (1134/1172) in the internal validation dataset and 93.3% (265/284) and 91.6% (948/1035) in the external validation dataset. The per-examination sensitivity and specificity were 92.0% (23/25) and 99.7% (287/288) in the internal validation dataset and 90.0% (72/80) and 96.0% (239/249) in the external validation dataset, respectively. The processing time of the DLA for automatic grading of motion-related artifacts was from 4.11 to 4.22 seconds per MRI examination. Conclusions The DLA enabled automatic and instant detection and grading of motion-related artifacts on arterial phase gadoxetic acid-enhanced liver MRI.
Materialart:
Online-Ressource
ISSN:
1536-0210
,
0020-9996
DOI:
10.1097/RLI.0000000000000914
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2023
ZDB Id:
2041543-6
