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. 2022 Oct;103(10):479-485.
doi: 10.1016/j.diii.2022.05.001. Epub 2022 May 18.

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence

Sébastien Mulé  1 , Rym Kharrat  2 , Pierre Zerbib  2 , Aurélien Massire  3 , Marcel Dominik Nickel  4 , Khalid Ambarki  3 , Edouard Reizine  5 , Laurence Baranes  2 , Benhalima Zegai  2 , Frederic Pigneur  2 , Hicham Kobeiter  6 , Alain Luciani  5
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Free article

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence

Sébastien Mulé et al. Diagn Interv Imaging. 2022 Oct.
Free article

Abstract

Purpose: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and the image quality of a single-shot fat-suppressed turbo spin-echo (TSE) T2-weighted sequence with deep learning reconstruction (HASTEDL) with that of a fat-suppressed T2-weighted BLADE TSE sequence in patients with focal liver lesions.

Materials and methods: Ninety-five patients (52 men, 43 women; mean age: 61 ± 14 [SD]; age range: 28-87 years) with 42 focal liver lesions (17 hepatocellular carcinomas, 10 sarcoidosis lesions, 9 myeloma lesions, 3 liver metastases and 3 focal nodular hyperplasias) who underwent liver MRI at 1.5 T including HASTEDL and BLADE sequences were retrospectively included. Overall image quality, noise level in the liver, lesion conspicuity and sharpness of liver lesion contours were assessed by two independent readers. Liver signal-to-noise ratio (SNR) and lesion contrast-to-noise ratio (CNR) were measured and compared between the two sequences, as well as the mean duration of the sequences (Student t-test or Wilcoxon test for paired data).

Results: Median overall quality on HASTEDL images (3; IQR: 3, 3) was significantly greater than that on BLADE images (2; IQR: 1, 3) (P < 0.001). Median noise level in the liver on HASTEDL images (0; IQR: 0, 0.5) was significantly lower than that on BLADE images (1; IQR: 1, 2) (P < 0.001). On HASTEDL images, mean liver SNR (107.3 ± 39.7 [SD]) and mean focal liver lesion CNR (87.0 ± 76.6 [SD]) were significantly greater than those on BLADE images (67.1 ± 23.8 [SD], P < 0.001 and 48.6 ± 43.9 [SD], P = 0.027, respectively). Acquisition time was significantly shorter with the HASTEDL sequence (18 ± [0] s; range: 18-18 s) compared to BLADE sequence (152 ± 47 [SD] s; range: 87-263 s) (P < 0.001).

Conclusion: By comparison with the BLADE sequence, HASTEDL sequence significantly reduces acquisition time while improving image quality, liver SNR and focal liver lesions CNR.

Keywords: Deep learning; Liver neoplasms; Magnetic resonance imaging.

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Conflict of interest statement

Declaration of Competing Interest Three authors (A.M., M.D.N. and K.A.) are Siemens Healthcare employees. The remaining authors had control of inclusion of all data and information that might present a conflict of interest for authors who are employees for Siemens Healthcare.

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