Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

Autor: Koechli M; Department of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland.; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland., Callaghan FM; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.; Center for MR-Research, University Children's Hospital Zurich, Zurich, Switzerland., Burkhardt BEU; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.; Pediatric Heart Center, University Children's Hospital Zurich, Zurich, Switzerland., Lohézic M; GE HealthCare, Zurich, Switzerland., Zhu X; GE HealthCare, Menlo Park, CA, USA., Rücker B; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.; Pediatric Heart Center, University Children's Hospital Zurich, Zurich, Switzerland., Valsangiacomo Buechel ER; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.; Pediatric Heart Center, University Children's Hospital Zurich, Zurich, Switzerland., Kellenberger CJ; Department of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland.; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland., Geiger J; Department of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland. julia.geiger@kispi.uzh.ch.; Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland. julia.geiger@kispi.uzh.ch.
Jazyk: angličtina
Zdroj: Pediatric radiology [Pediatr Radiol] 2024 Sep; Vol. 54 (10), pp. 1674-1685. Date of Electronic Publication: 2024 Jul 17.
DOI: 10.1007/s00247-024-05978-6
Abstrakt: Background: Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds.
Objective: To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences.
Material and Methods: Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experienced readers who visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland-Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC).
Results: Mean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1 g vs. 69.9 ± 32.5 g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05).
Conclusion: Deep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.
(© 2024. The Author(s).)
Databáze: MEDLINE
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