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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

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

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.

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Authors and Affiliations

  1. Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA

    Luke A. Ginocchio, Paul N. Smereka, Angela Tong, Vinay Prabhu, Hersh Chandarana & Krishna P. Shanbhogue

  2. MR Applications Predevelopment, Siemens Healthcare GmbH, 91052, Erlangen, Germany

    Dominik Nickel

  3. Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, 08540, USA

    Simon Arberet

Authors
  1. Luke A. Ginocchio
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  2. Paul N. Smereka
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  3. Angela Tong
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  4. Vinay Prabhu
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Correspondence to Luke A. Ginocchio.

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Conflict of interest

Dominik Nickel: Siemens Healthcare employee, who provided technical assistance, but was not involved in the data acquisition or evaluation, nor did he have direct control of the data - Simon Arberet: Siemens Healthcare employee, who provided technical assistance, but was not involved in the data acquisition or evaluation, nor did he have direct control of the data - Other Authors: No relevant disclosures.

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Ginocchio, L.A., Smereka, P.N., Tong, A. et al. 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. Abdom Radiol 48, 282–290 (2023). https://doi.org/10.1007/s00261-022-03687-y

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