Universität zu Köln

Janssen, Jan P. ORCID: 0000-0003-0980-4606, Gertz, Roman J. ORCID: 0000-0002-6414-4105, Tristram, Juliana ORCID: 0000-0003-0987-7987, Spurek, Marvin A. ORCID: 0000-0002-6129-9267, Kaya, Kenan ORCID: 0009-0008-7625-3457, Terzis, Robert ORCID: 0009-0007-1068-8477, Hahnfeldt, Robert ORCID: 0000-0001-7997-3216, Gietzen, Thorsten ORCID: 0000-0001-7948-202X, Maintz, David ORCID: 0000-0002-8942-3776, Persigehl, Thorsten ORCID: 0000-0001-5928-4405, Weiss, Kilian, Pennig, Lenhard ORCID: 0000-0002-6606-9313 and Gietzen, Carsten ORCID: 0000-0002-2354-3847 (2025). Accelerating non-contrast MR angiography of the thoracic aorta using compressed SENSE with deep learning reconstruction. European Journal of Radiology, 192. pp. 1-10. Elsevier. ISSN 0720-048X

PDF ejrad.2025.112403.pdf Bereitstellung unter der CC-Lizenz: Creative Commons Attribution. Download (5MB)

Abstract

[Artikel-Nr.: 112403] Purpose: REACT (Relaxation-Enhanced Angiography without ContrasT) is a reliable non-contrast magnetic resonance angiography for imaging of the thoracic aorta but remains time-consuming. This study evaluates acceleration of image acquisition using compressed sensing and parallel imaging (Compressed SENSE, CS) combined with deep learning-based image reconstruction (CS-AI). Methods: In this prospective single-center study, 40 volunteers underwent ECG- and navigator-triggered 3D REACT at 3 T using CS acceleration factor 4 (CS4; reference standard) and 8 (CS8). CS8 data were reconstructed with standard and CS-AI methods (CS8-AI). Two radiologists measured aortic diameters, rated subjective image quality and performed pairwise comparisons. Additionally, objective image quality metrics were calculated. Results: Median scan time was reduced by 44 % (CS4: 8:41 min; CS8/CS8-AI: 4:52 min). All techniques showed excellent agreement in aortic diameter measurements (mean differences < 0.2 mm; P > 0.999). CS8-AI demonstrated reduced mean absolute deviation from CS4 compared to CS8 (0.67 vs. 0.77 mm; P = 0.003), and measurement variance was 40–50 % lower with CS8-AI than with CS8 (inter-/intrarater: P < 0.001), and comparable to CS4. CS8 showed significantly lower subjective image quality scores than CS4 (3.70[3.33–4.00] vs. 4.25[3.90–4.50]; P < 0.001), while CS8-AI showed comparable or higher scores (4.40[4.00–4.70]; P = 0.076). Forced-choice comparisons favored CS4 over CS8 (90 % vs. 2.5 %; P < 0.001), but no preference was observed between CS4 and CS8-AI (42.5 % vs. 37.5 %; P > 0.999). Objective metrics predominantly confirmed the subjective results. Conclusion: Deep learning-based reconstruction enables the acquisition of REACT of the thoracic aorta in less than five minutes while preserving high image quality and maintaining excellent measurement reproducibility.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Janssen, Jan P. UNSPECIFIED https://orcid.org/0000-0003-0980-4606 UNSPECIFIED Gertz, Roman J. UNSPECIFIED https://orcid.org/0000-0002-6414-4105 UNSPECIFIED Tristram, Juliana UNSPECIFIED https://orcid.org/0000-0003-0987-7987 UNSPECIFIED Spurek, Marvin A. UNSPECIFIED https://orcid.org/0000-0002-6129-9267 UNSPECIFIED Kaya, Kenan UNSPECIFIED https://orcid.org/0009-0008-7625-3457 UNSPECIFIED Terzis, Robert UNSPECIFIED https://orcid.org/0009-0007-1068-8477 UNSPECIFIED Hahnfeldt, Robert UNSPECIFIED https://orcid.org/0000-0001-7997-3216 UNSPECIFIED Gietzen, Thorsten UNSPECIFIED https://orcid.org/0000-0001-7948-202X UNSPECIFIED Maintz, David UNSPECIFIED https://orcid.org/0000-0002-8942-3776 UNSPECIFIED Persigehl, Thorsten UNSPECIFIED https://orcid.org/0000-0001-5928-4405 UNSPECIFIED Weiss, Kilian UNSPECIFIED UNSPECIFIED UNSPECIFIED Pennig, Lenhard UNSPECIFIED https://orcid.org/0000-0002-6606-9313 UNSPECIFIED Gietzen, Carsten UNSPECIFIED https://orcid.org/0000-0002-2354-3847 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-810069
Identification Number:	10.1016/j.ejrad.2025.112403
Journal or Publication Title:	European Journal of Radiology
Volume:	192
Page Range:	pp. 1-10
Number of Pages:	10
Date:	November 2025
Publisher:	Elsevier
ISSN:	0720-048X
Language:	English
Faculty:	Faculty of Medicine
Divisions:	Faculty of Medicine > Innere Medizin > Klinik III für Innere Medizin - Kardiologie, Pneumologie, Angiologie und internistische Intensivmedizin Faculty of Medicine > Radiologische Diagnostik > Institut und Poliklinik für Radiologische Diagnostik
Subjects:	Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language Thoracic aorta ; Magnetic resonance angiography ; Non-contrast-enhanced magnetic resonance ; angiography ; Compressed sensing ; Deep Learning English
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/81006