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Deep learning reconstruction enhanced myocardial scar detection on late gadolinium enhancement MRI in patients with ventricular arrhythmias

Year: 2026

Type: article

Abstract: To evaluate the impact of a deep learning reconstruction (DLRecon) algorithm on the image quality and scar quantification in cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging for patients with ventricular arrhythmias (VAS) Seventy-two patients with suspected or known cardiomyopathy were prospectively scanned with 3.0T scanner. Short-axis LGE images were reconstructed using conventional reconstruction (ConRecon) and DLRecon, respectively. 4-point Likert score, contrast-to-noise radio (CNR) were used to assess the image quality of LGE and were compared between ConRecon and DLRecon, separately. Scar size (LGE extent) was quantified using the standard deviation (SD) thresholding and full width at half maximum (FWHM) methods and compared between ConRecon and DLRecon images. Sixty-four patients (27 VAS, 37 non-VAS) were included. DLRecon images received significantly higher Likert scores than ConRecon images overall, and within both VAs and non-VAs subgroups. DLRecon significantly improved CNR among scar, myocardium, and blood pool, particularly in patients with high heart rate (>75 bpm) or low left ventricular ejection fraction (≤35%). Significantly greater LGE extent was detected using DLRecon with the 5SD method in VAS patients with high heart rate, leading to a higher proportion being classified as high SCD risk. No significant differences were found between DLRecon and ConRecon for LGE quantification using FWHM or the resulting SCD risk stratification (based on LGE extent, with ≥15% defining high risk). DLRecon significantly improves LGE image quality and enhances myocardial scar detection in VAS patients, particularly those with high heart rates. This technique shows potential to aid in scar assessment and SCD risk stratification, warranting further clinical investigation. • Substantially Improved Diagnostic Image Quality: The deep learning-based reconstruction (DLRecon) technique demonstrated a significant and consistent enhancement in the overall subjective quality of late gadolinium enhancement (LGE) images. • Enhanced Diagnostic Confidence: By markedly improving the contrast between scar tissue and healthy myocardium, DLRecon reduces diagnostic uncertainty and facilitates more accurate identification of fibrotic lesions. • Direct Clinical Impact: The algorithm improves the detection rate of myocardial scars and, crucially, helps rectify the underestimation of sudden cardiac death (SCD) risk in patients with ventricular arrhythmia syndrome (VAS), underscoring its clinical applicability.

Source: Meta-Radiology

DOI: 10.1016/j.metrad.2025.100204