Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.

Autor: Ginocchio LA; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA. Luke.Ginocchio@nyulangone.org., Smereka PN; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA., Tong A; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA., Prabhu V; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA., Nickel D; MR Applications Predevelopment, Siemens Healthcare GmbH, 91052, Erlangen, Germany., Arberet S; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, 08540, USA., Chandarana H; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA., Shanbhogue KP; Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
Jazyk: angličtina
Zdroj: Abdominal radiology (New York) [Abdom Radiol (NY)] 2023 Jan; Vol. 48 (1), pp. 282-290. Date of Electronic Publication: 2022 Sep 28.
DOI: 10.1007/s00261-022-03687-y
Abstrakt: Purpose: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI prototype with deep-learning-based image reconstruction (DL HASTE-FS) with a standard T2-FS sequence for 3 T liver MRI.
Methods: 41 consecutive patients with 3 T abdominal MRI examinations including standard T2-FS and DL HASTE-FS, between 5/6/2020 and 11/23/2020, comprised the study cohort. Three radiologists independently reviewed images using a 5-point Likert scale for artifact and image quality measures, while also assessing for liver lesions.
Results: DL HASTE-FS acquisition time was 54.93 ± 16.69, significantly (p < .001) shorter than standard T2-FS (114.00 ± 32.98 s). DL HASTE-FS received significantly higher scores for sharpness of liver margin (4.3 vs 3.3; p < .001), hepatic vessel margin (4.2 vs 3.3; p < .001), pancreatic duct margin (4.0 vs 1.9; p < .001); in-plane (4.0 vs 3.2; p < .001) and through-plane (3.9 vs 3.4; p < .001) motion artifacts; other ghosting artifacts (4.3 vs 2.9; p < .001); and overall image quality (4.0 vs 2.9; p < .001), in addition to receiving a higher score for homogeneity of fat suppression (3.7 vs 3.4; p = .04) and liver-fat contrast (p = .03). For liver lesions, DL HASTE-FS received significantly higher scores for sharpness of lesion margin (4.4 vs 3.7; p = .03).
Conclusion: Novel single-shot T2-weighted MRI with deep-learning-based image reconstruction demonstrated superior image quality compared with the standard T2-FS sequence for 3 T liver MRI, while being acquired in less than half the time.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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