Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers

Jan Paul Janssen; Kenan Kaya; Robert Terzis; Robert Hahnfeldt; Roman Johannes Gertz; Lukas Goertz; Stephan Skornitzke; Juliana Tristram; Thomas Dratsch; Cansin Goezdas; Christoph Kabbasch; Kilian Weiss; Lenhard Pennig; Carsten Herbert Gietzen

doi:10.1186/s41747-025-00560-7

Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers

Eur Radiol Exp. 2025 Feb 18;9(1):19. doi: 10.1186/s41747-025-00560-7.

Authors

Affiliations

¹ Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. jan.janssen@uk-koeln.de.
² Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
³ Philips GmbH, Hamburg, Germany.

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.

Keywords: Carotid arteries; Deep learning; Healthy volunteers; Magnetic resonance angiography; Neck.

MeSH terms

Adult
Deep Learning*
Female
Healthy Volunteers
Humans
Image Processing, Computer-Assisted* / methods
Imaging, Three-Dimensional
Magnetic Resonance Angiography* / methods
Male
Middle Aged
Neck* / blood supply
Neck* / diagnostic imaging
Signal-To-Noise Ratio
Young Adult