Universität zu Köln

Janssen, Jan Paul ORCID: 0000-0003-0980-4606, Kaya, Kenan ORCID: 0009-0008-7625-3457, Terzis, Robert ORCID: 0009-0007-1068-8477, Hahnfeldt, Robert ORCID: 0000-0001-7997-3216, Gertz, Roman Johannes ORCID: 0000-0002-6414-4105, Goertz, Lukas ORCID: 0000-0002-2620-7611, Skornitzke, Stephan, Tristram, Juliana, Dratsch, Thomas, Goezdas, Cansin, Kabbasch, Christoph ORCID: 0000-0003-3712-2258, Weiss, Kilian, Pennig, Lenhard ORCID: 0000-0002-6606-9313 and Gietzen, Carsten Herbert ORCID: 0000-0002-2354-3847 (2025). Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers. European Radiology Experimental, 9 (1). Springer Nature. ISSN 2509-9280

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Abstract

Background: We evaluated the acceleration of a three-dimensional isotropic flow-independent magnetic resonance angiography (MRA) (relaxation-enhanced angiography without contrast and triggering, REACT) of neck arteries using compressed SENSE (CS) combined with deep learning (adaptive intelligence, AI)-based reconstruction (CS-AI). Methods: Thirty-four volunteers received 3-T REACT MRA, acquired threefold: (i) CS acceleration factor 7 (CS7), scan time 1:20 min:s; (ii) CS acceleration factor 10 (CS10), scan time 0:55 min:s; and (iii) CS-AI acceleration factor 10 (CS10-AI), scan time 0:55 min:s. Two radiologists rated the image quality of seven arterial segments and overall image noise. Additionally, a pairwise forced-choice comparison was conducted. Apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR) were measured, and image sharpness was assessed using the edge-rise distance (ERD). Multiple t -tests and nonparametric tests with Bonferroni correction were performed for comparison to CS7 as the reference standard. Results: Compared to CS7, CS10 showed lower image quality ( p < 0.001) while CS10-AI obtained higher scores ( p = 0.010). Image noise was similar between CS7 and CS10 ( p = 0.138) while CS10-AI yielded a lower noise ( p = 0.008). Forced choice revealed preferences for CS7 over CS10 ( p < 0.001), but no preference between CS7 and CS10-AI ( p > 0.999). Compared to CS7, aSNR and aCNR were lower in CS10 ( p < 0.001) and the ERD was longer ( p = 0.004), while CS10-AI provided better aSNR and aCNR ( p = 0.001) and showed no difference in ERD ( p = 0.776). Conclusion: Sub-1-min CS-AI cervical REACT MRA was acquired without compromising image quality. Relevance statement The implementation of a fast and reliable non-contrast MRA has the potential to reduce costs and time while increasing patient comfort and safety. Clinical studies evaluating the diagnostic performance for stenosis or dissection are needed. Trial registration DRKS00030210 (German Clinical Trials Register; https://drks.de/). Key Points: Deep learning reconstruction enables sub-1-min non-contrast-enhanced MRA of extracranial arteries. Acceleration without deep learning reconstruction causes inferior image quality. Acceleration with deep learning reconstruction exceeds, in part, the clinical standard. Graphical abstract.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Janssen, Jan Paul UNSPECIFIED https://orcid.org/0000-0003-0980-4606 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 Gertz, Roman Johannes UNSPECIFIED https://orcid.org/0000-0002-6414-4105 UNSPECIFIED Goertz, Lukas UNSPECIFIED https://orcid.org/0000-0002-2620-7611 UNSPECIFIED Skornitzke, Stephan UNSPECIFIED UNSPECIFIED UNSPECIFIED Tristram, Juliana UNSPECIFIED UNSPECIFIED UNSPECIFIED Dratsch, Thomas UNSPECIFIED UNSPECIFIED UNSPECIFIED Goezdas, Cansin UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, Christoph UNSPECIFIED https://orcid.org/0000-0003-3712-2258 UNSPECIFIED Weiss, Kilian UNSPECIFIED UNSPECIFIED UNSPECIFIED Pennig, Lenhard UNSPECIFIED https://orcid.org/0000-0002-6606-9313 UNSPECIFIED Gietzen, Carsten Herbert UNSPECIFIED https://orcid.org/0000-0002-2354-3847 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-792760
Identification Number:	10.1186/s41747-025-00560-7
Journal or Publication Title:	European Radiology Experimental
Volume:	9
Number:	1
Date:	18 February 2025
Publisher:	Springer Nature
ISSN:	2509-9280
Language:	English
Faculty:	Faculty of Medicine
Divisions:	Faculty of Medicine > Radiologische Diagnostik > Institut und Poliklinik für Radiologische Diagnostik
Subjects:	Medical sciences Medicine
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/79276