Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis

Felix Kubicka; Qinxuan Tan; Tom Meyer; Dominik Nickel; Elisabeth Weiland; Moritz Wagner; Stephan Rodrigo Marticorena Garcia

doi:10.3390/curroncol32010030

Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis

Curr Oncol. 2025 Jan 3;32(1):30. doi: 10.3390/curroncol32010030.

Authors

Felix Kubicka¹, Qinxuan Tan¹, Tom Meyer¹, Dominik Nickel², Elisabeth Weiland², Moritz Wagner¹, Stephan Rodrigo Marticorena Garcia¹

Affiliations

¹ Department of Radiology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.
² MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany.

Abstract

Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 ± 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p < 0.001; Likert scale, p < 0.001). Compared with DL-HASTE-5 mm (1.10 × 10^-5 ± 6.93 × 10^-6), DL-HASTE-3 mm (1.56 × 10^-5 ± 8.69 × 10^-6) provided a significantly higher SNR Laplacian variance (p < 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p < 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4-5]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4-5]; p < 0.001). DL-HASTE reduced the acquisition time by 63-69% compared with standard-HASTE-5 mm (p < 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR.

Keywords: HASTE; MRI; abdomen; deep learning; high resolution; thin slice.

MeSH terms

Abdomen* / diagnostic imaging
Adult
Aged
Breath Holding*
Deep Learning*
Female
Humans
Image Processing, Computer-Assisted / methods
Magnetic Resonance Imaging* / methods
Male
Middle Aged
Prospective Studies
Signal-To-Noise Ratio
Young Adult

Grants and funding

The authors gratefully acknowledge the financial support from the German Research Foundation (Bonn, Nordrhein-Westfalen, Germany; Funding number: DFG 467843609 and FOR5628 to Stephan Rodrigo Marticorena Garcia). Siemens Healthcare (Erlangen, Bavara, Germany) provided financial and technical support for conducting the study. Siemens Healthcare did not have control over the conducting of the study or the data analysis.