Breath-hold diffusion-weighted MR imaging (DWI) using deep learning reconstruction: Comparison with navigator triggered DWI in patients with malignant liver tumors

M Tanabe; M Higashi; K Miyoshi; R Morooka; H Kiyoyama; K Ihara; Y Kawano; M Yamane; T Yamaguchi; K Ito

doi:10.1016/j.radi.2024.11.027

Breath-hold diffusion-weighted MR imaging (DWI) using deep learning reconstruction: Comparison with navigator triggered DWI in patients with malignant liver tumors

Radiography (Lond). 2024 Dec 11;31(1):275-280. doi: 10.1016/j.radi.2024.11.027. Online ahead of print.

Authors

M Tanabe¹, M Higashi², K Miyoshi², R Morooka², H Kiyoyama², K Ihara², Y Kawano², M Yamane³, T Yamaguchi³, K Ito²

Affiliations

¹ Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan. Electronic address: m-tanabe@yamaguchi-u.ac.jp.
² Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
³ Department of Radiological Technology, Yamaguchi University Hospital, Japan.

PMID: 39667265
DOI: 10.1016/j.radi.2024.11.027

Abstract

Introduction: This study investigated the feasibility of single breath-hold (BH) diffusion-weighted MR imaging (DWI) using deep learning reconstruction (DLR) compared to navigator triggered (NT) DWI in patients with malignant liver tumors.

Methods: This study included 91 patients who underwent both BH-DWI and NT-DWI with 3T MR system. Abdominal MR images were subjectively analyzed to compare visualization of liver edges, presence of ghosting artifacts, conspicuity of malignant liver tumors, and overall image quality. Then, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values of malignant liver tumors were objectively measured using regions of interest.

Results: Image quality except conspicuity of malignant liver tumors were significantly better in BH-DW image than in NT-DW image (p < 0.01). Regarding the conspicuity of malignant liver tumors, there was no statistically significant difference between BH-DWI and NT-DWI (p = 0.67). The conspicuity score of 1 or 2 was rendered in 19 (21 %) patients in NT-DWI group. Conversely, BH-DWI showed a score of 3 or 4 in 11 (58 %) of these 19 patients. The SNR was significantly higher in BH-DWI than in NT-DWI (29.5 ± 14.0 vs. 27.3 ± 14.7, p < 0.047). No significant difference was observed between CNR and ADC values of malignant liver tumors between BH-DWI and NT-DWI (5.67 ± 3.57 vs. 5.78 ± 3.08, p = 0.243; 997.2 ± 207.0 vs. 1021.0 ± 253.1, p = 0.547).

Conclusion: The BH-DWI using DLR is feasible for liver MRI by improving the SNR and overall image quality, and may play a complementary role to NT-DWI by improving the conspicuity of malignant liver tumor in patients with image distortion in NT-DWI.

Implications for practice: BH-DWI with DLR would be a preferred approach to achieving sufficient image quality in patients with an irregular triggering pattern, as an alternative to NT-DWI. A further reduction in BH duration (<15 s) should be achieved, taking into account patient tolerance.

Keywords: Breath holding; Deep learning; Diffusion magnetic resonance imaging; Liver.