Universität zu Köln

Klein, Konstantin ORCID: 0009-0004-8411-2049, Langenbach, Marcel Christian ORCID: 0000-0002-8168-2714, Pennig, Lenhard ORCID: 0000-0002-6606-9313, Schömig, Thomas ORCID: 0009-0001-7560-4285, Terzis, Robert ORCID: 0009-0007-1068-8477, Langenbach, Isabel Luisa ORCID: 0000-0001-7410-0635, Maintz, David ORCID: 0000-0002-8942-3776, Gajzler, Matej, Sonnabend, Kristina ORCID: 0000-0003-3064-0247 and Nähle, Claas Philip (2025). Qualitative and quantitative analysis of functional cardiac MRI using a novel compressed SENSE sequence with artificial intelligence image reconstruction. Magnetic Resonance Imaging, 122. pp. 1-7. Elsevier. ISSN 0730-725X

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Abstract

[Artikel-Nr.: 110448] Background: To evaluate the feasibility of combining Compressed SENSE (CS) with a newly developed deep learning-based algorithm (CS-AI) using a Convolutional Neural Network to accelerate balanced steady-state free precession (bSSFP)-sequences for cardiac magnetic resonance imaging (MRI). Methods: 30 healthy volunteers were examined prospectively with a 3 T MRI scanner. We acquired CINE bSSFP sequences for short axis (SA, multi-breath-hold) and four-chamber (4CH)-view of the heart. For each sequence, four different CS accelerations and CS-AI reconstructions with three different denoising parameters, CS-AI medium, CS-AI strong, and CS-AI complete, were used. Cardiac left ventricular (LV) function (i.e., ejection fraction, end-diastolic volume, end-systolic volume, and LV mass) was analyzed using the SA sequences in every CS factor and each AI level. Two readers, blinded to the acceleration and denoising levels, evaluated all sequences regarding image quality and artifacts using a 5-point Likert scale. Friedman and Dunn's multiple comparison tests were used for qualitative evaluation, ANOVA and Tukey Kramer test for quantitative metrics. Results: Scan time could be decreased up to 57 % for the SA-Sequences and up to 56 % for the 4CH-Sequences compared to the clinically established sequences consisting of SA-CS3 and 4CH-CS2,5 (SA-CS3: 112 s vs. SA-CS6: 48 s; 4CH-CS2,5: 9 s vs. 4CH-CS5: 4 s, p < 0.001). LV-functional analysis was not compromised by using accelerated MRI sequences combined with CS-AI reconstructions (all p > 0.05). The image quality loss and artifact increase accompanying increasing acceleration levels could be entirely compensated by CS-AI post-processing, with the best results for image quality using the combination of the highest CS factor with strong AI (SA-CINE: Coef.:1.31, 95 %CI:1.05–1.58; 4CH-CINE: Coef.:1.18, 95 %CI:1.05–1.58; both p < 0.001), and with complete AI regarding the artifact score (SA-CINE: Coef.:1.33, 95 %CI:1.06–1.60; 4CH-CINE: Coef.:1.31, 95 %CI:0.86–1.77; both p < 0.001). Conclusion: Combining CS sequences with AI-based image reconstruction for denoising significantly decreases scan time in cardiac imaging while upholding LV functional analysis accuracy and delivering stable outcomes for image quality and artifact reduction. This integration presents a promising advancement in cardiac MRI, promising improved efficiency without compromising diagnostic quality.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Klein, Konstantin UNSPECIFIED https://orcid.org/0009-0004-8411-2049 UNSPECIFIED Langenbach, Marcel Christian UNSPECIFIED https://orcid.org/0000-0002-8168-2714 UNSPECIFIED Pennig, Lenhard UNSPECIFIED https://orcid.org/0000-0002-6606-9313 UNSPECIFIED Schömig, Thomas UNSPECIFIED https://orcid.org/0009-0001-7560-4285 UNSPECIFIED Terzis, Robert UNSPECIFIED https://orcid.org/0009-0007-1068-8477 UNSPECIFIED Langenbach, Isabel Luisa UNSPECIFIED https://orcid.org/0000-0001-7410-0635 UNSPECIFIED Maintz, David UNSPECIFIED https://orcid.org/0000-0002-8942-3776 UNSPECIFIED Gajzler, Matej UNSPECIFIED UNSPECIFIED UNSPECIFIED Sonnabend, Kristina UNSPECIFIED https://orcid.org/0000-0003-3064-0247 UNSPECIFIED Nähle, Claas Philip UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-805888
Identification Number:	10.1016/j.mri.2025.110448
Journal or Publication Title:	Magnetic Resonance Imaging
Volume:	122
Page Range:	pp. 1-7
Number of Pages:	7
Date:	October 2025
Publisher:	Elsevier
ISSN:	0730-725X
Language:	English
Faculty:	Faculty of Medicine
Divisions:	Faculty of Medicine > Radiologische Diagnostik > Institut und Poliklinik für Radiologische Diagnostik
Subjects:	Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language Magnetic resonance imaging ; Cardiac MRI ; Artificial intelligence ; Deep learning ; Accelerated MRI scans English
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/80588