Evaluation of combined late gadolinium-enhancement and functional cardiac magnetic resonance imaging using spiral real-time acquisition.
Johannes PortmannTobias WechPhilipp EirichJulius Frederik HeidenreichNils PetriBernhard PetritschThorsten Alexander BleyHerbert KöstlerPublished in: NMR in biomedicine (2022)
The purpose of the current study was to implement and validate joint real-time acquisition of functional and late gadolinium-enhancement (LGE) cardiac magnetic resonance (MR) images during free breathing. Inversion recovery cardiac real-time images with a temporal resolution of 50 ms were acquired using a spiral trajectory (IR-CRISPI) with a pre-emphasis based on the gradient system transfer function during free breathing. Functional and LGE cardiac MR images were reconstructed using a low-rank plus sparse model. Late gadolinium-enhancement appearance, image quality, and functional parameters of IR-CRISPI were compared with clinical standard balanced steady-state free precession breath-hold techniques in 10 patients. The acquisition of IR-CRISPI in free breathing of the entire left ventricle took 97 s on average. Bland-Altman analysis and Wilcoxon tests showed a higher artifact level for the breath-hold technique (p = 0.003), especially for arrhythmic patients or patients with dyspnea, but an increased noise level for IR-CRISPI of the LGE images (p = 0.01). The estimated transmural extent of the enhancement differed by not more than 25% and did not show a significant bias between the techniques (p = 0.50). The ascertained functional parameters were similar for the breath-hold technique and IR-CRISPI, that is, with a minor, nonsignificant (p = 0.16) mean difference of the ejection fraction of 2.3% and a 95% confidence interval from -4.8% to 9.4%. IR-CRISPI enables joint functional and LGE imaging in free breathing with good image quality but distinctly shorter scan times in comparison with breath-hold techniques.
Keyphrases
- ejection fraction
- image quality
- magnetic resonance
- contrast enhanced
- magnetic resonance imaging
- end stage renal disease
- computed tomography
- aortic stenosis
- deep learning
- convolutional neural network
- left ventricular
- optical coherence tomography
- newly diagnosed
- chronic kidney disease
- prognostic factors
- heart failure
- high resolution
- peritoneal dialysis
- machine learning
- mass spectrometry
- air pollution
- photodynamic therapy
- atrial fibrillation
- pulmonary arterial hypertension
- pulmonary hypertension
- mitral valve
- patient reported outcomes
- coronary artery
- patient reported