CS-SR cardiac MRI significantly reduced acquisition time to 165.6s compared to 411.1s for standard SENSE (p<0.001), without impairing left ventricular volumetric analysis.
Observational (n=31)
Blinded readers
No
Does deep learning-based super-resolution reconstruction with C-SENSE reduce acquisition time without impairing left ventricular volumetry and image quality compared to standard SENSE in patients undergoing cardiac MRI?
Deep learning-based super-resolution reconstruction of cardiac MRI significantly reduces acquisition time while maintaining accurate left ventricular volumetric analysis and comparable overall image quality.
Estimación del efecto: Mean difference 0.04 ml (95% CI -11.19 to 11.26)
Tasa de eventos absoluta: 167.5% vs 167.5%
valor p: p=0.970
Abstract To assess differences in volumetry, image quality and acquisition time between balanced steady-state free precession cine sequences acquired using (a) a standardized sensitivity encoding (SENSE) approach and (b) deep learning-based super-resolution reconstruction based on high-resolution images acquired with compressed sensitivity encoding (C-SENSE). We retrospectively evaluated 31 consecutive patients (mean age 61.2 ± 13.1 years, 26% female (8/31) and 74% male (23/31)) undergoing cardiac magnetic resonance imaging (MRI) examinations to assess for the presence of ischemic and non-ischemic cardiomyopathies. Cine images were acquired using a 1.5T Philips Ingenia MRI scanner, with classic parallel imaging (SENSE) and compressed sensing (C-SENSE) accelerated acquisition techniques ( R = 2 and R = 4, respectively). C-SENSE datasets were reconstructed using a deep learning-based denoising and super-resolution algorithm to enhance image resolution and quality (CS-SR). To evaluate cardiac function, manual left ventricular (LV) segmentation and volumetric analysis were performed on both datasets by two readers, who were blinded to the clinical data. Image quality was rated independently by three readers using Likert scales. Correlation between SENSE and CS-SR datasets with respect to LV volumetry was high ( r = 0.98-1.00), with no significant differences found for end-diastolic volume (mean difference 0.04 ml, limits of agreement (LoA) -11.19 to 11.26 ml; p = 0.970) or end-systolic volume (mean difference 1.60 ml, LoA − 7.48 to 10.68 ml; p = 0.064). Overall subjective image quality was comparable ( p = 0.061), with CS-SR offering better image sharpness at the cost of increased artifacts ( p < 0.001 respectively). Image acquisition time was significantly accelerated with C-SENSE acquisition (SENSE: 411.1 ± 47.7 s, C-SENSE: 165.6 ± 21.5 s; p < 0.001). CS-SR shows promise in streamlining routine cardiac imaging by significantly shortening acquisition times, without impairing LV volumetric analysis, while preserving overall image quality and resolution.
Adomat et al. (Mon,) conducted a observational in Ischemic and non-ischemic cardiomyopathies (n=31). Compressed sensing with deep learning-based super-resolution reconstruction (CS-SR) vs. Standardized sensitivity encoding (SENSE) was evaluated on End-diastolic volume (EDV) (Mean difference 0.04 ml, 95% CI -11.19 to 11.26, p=0.970). CS-SR cardiac MRI significantly reduced acquisition time to 165.6s compared to 411.1s for standard SENSE (p<0.001), without impairing left ventricular volumetric analysis.