A deep learning-based reconstruction algorithm significantly improved late gadolinium enhancement image quality compared to standard reconstruction (score 3.6 vs 3.3, p<0.001), but significantly impacted scar size quantification when using standard deviation thresholding methods.
Observational (n=60)
No
Does a deep learning-based reconstruction algorithm improve image quality and alter myocardial scar quantification in patients with suspected or known cardiomyopathy?
Deep learning-based reconstruction improves LGE image quality but significantly alters scar size quantification when using standard deviation-based thresholding, making the FWHM method preferable.
Absolute Event Rate: 3.6% vs 3.3%
p-value: p=<0.001
OBJECTIVES: The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. METHODS: Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. RESULTS: DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). CONCLUSIONS: LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. KEY POINTS: • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning-based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.
Velde et al. (Sat,) conducted a observational in Cardiomyopathy (n=60). Deep learning-based reconstruction algorithm (DLRecon) vs. Conventional reconstruction was evaluated on Subjective image quality score (5-point scale) (p=<0.001). A deep learning-based reconstruction algorithm significantly improved late gadolinium enhancement image quality compared to standard reconstruction (score 3.6 vs 3.3, p<0.001), but significantly impacted scar size quantification when using standard deviation thresholding methods.