TY - JOUR
T1 - A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality with PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging
AU - Saleh, Mohammed
AU - Virarkar, Mayur
AU - Javadi, Sanaz
AU - Mathew, Manoj
AU - Vulasala, Sai Swarupa Reddy
AU - Son, Jong Bum
AU - Sun, Jia
AU - Bayram, Ersin
AU - Wang, Xinzeng
AU - Ma, Jingfei
AU - Szklaruk, Janio
AU - Bhosale, Priya
N1 - Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Objectives Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. Methods Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. Results Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images (P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images (P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. Conclusion The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.
AB - Objectives Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. Methods Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. Results Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images (P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images (P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. Conclusion The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.
KW - artifacts
KW - deep learning
KW - genital neoplasm female
KW - magnetic resonance imaging
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U2 - 10.1097/RCT.0000000000001491
DO - 10.1097/RCT.0000000000001491
M3 - Article
C2 - 37707401
AN - SCOPUS:85171239039
SN - 0363-8715
VL - 47
SP - 721
EP - 728
JO - Journal of computer assisted tomography
JF - Journal of computer assisted tomography
IS - 5
ER -