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. 2025 May;50(5):2312-2320.
doi: 10.1007/s00261-024-04675-0. Epub 2024 Nov 13.

Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen

Masahiro Tanabe  1 , Yosuke Kawano  2 , Kenichiro Ihara  2 , Keisuke Miyoshi  2 , Jo Ishii  2 , Kanako Nomura  2 , Ryoko Morooka  2 , Mayumi Higashi  2 , Katsuyoshi Ito  2
Affiliations

Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen

Masahiro Tanabe et al. Abdom Radiol (NY). 2025 May.

Abstract

Purpose: To evaluate the feasibility of a high-precision single-shot fast spin-echo (SS-FSE) sequence using the deep learning-based Precise IQ Engine (PIQE) algorithm in comparison with standard SS-FSE for T2-weighted MR imaging of the abdomen, and to compare the image quality with a multi-shot (MS)-FSE sequence using the PIQE algorithm.

Methods: This retrospective study included 105 patients who underwent abdominal MR including T2-weighted sequences using the PIQE reconstruction algorithm. The image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in high-precision SS-FSE sequences using PIQE were compared to those in standard SS-FSE without PIQE and MS-FSE sequences using PIQE.

Results: The scores for all qualitative parameters were significantly higher in high-precision SS-FSE sequence using PIQE than in standard SS-FSE sequence without PIQE (all p < 0.001). In the comparison between two high-precision sequences using PIQE, the SS-FSE sequence showed significantly better scores for the blurring, ghosts or motion/flow artifacts, conspicuity of intrahepatic structures, focal nonsolid hepatic and pancreatic cystic lesions, and overall image quality, in comparison to the MS-FSE sequence (all p < 0.001). Additionally, the SS-FSE sequence using PIQE showed significantly higher SNR of the liver and CNR of nonsolid hepatic lesions than the MS-FSE sequence using PIQE (p < 0.001).

Conclusions: A high-precision SS-FSE sequence using the PIQE algorithm is a feasible alternative to the standard FSE sequence in T2-weighted MR imaging of the abdomen. It can improve image quality, the SNR of the liver, and the ability to visualize nonsolid focal liver lesions and pancreatic cystic lesions in comparison to a high-precision MS-FSE sequence using PIQE although this study was limited by single-center design and lack of pathological confirmation.

Keywords: Abdomen; Breath holding; Deep learning; Image reconstruction; MRI.

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