Qualitative and Quantitative Evaluation of a Deep Learning-Based Reconstruction for Accelerated Cardiac Cine Imaging

作者：Junjie Ma, Xucheng Zhu, Suryanarayanan Kaushik, Eman Ali, Liangliang Li, Kavitha Manickam, K. Li, Martin Janich · 发表于：Bioengineering · 年份：2025 · DOI：10.3390/bioengineering12030231 · 被引用次数：4 · 研究领域：Advanced MRI Techniques and Applications、Cardiac Imaging and Diagnostics、Cardiovascular Function and Risk Factors

Two-dimensional (2D) cine imaging is essential in routine clinical cardiac MR (CMR) exams for assessing cardiac structure and function. Traditional cine imaging requires patients to hold their breath for extended periods and maintain consistent heartbeats for optimal image quality, which can be challenging for those with impaired breath-holding capacity or irregular heart rhythms. This study aims to systematically assess the performance of a deep learning-based reconstruction (Sonic DL Cine, GE HealthCare, Waukesha, WI, USA) for accelerated cardiac cine acquisition. Multiple retrospective experiments were designed and conducted to comprehensively evaluate the technique using data from an MR-dedicated extended cardiac torso anatomical phantom (digital phantom) and healthy volunteers on different cardiac planes. Image quality, spatiotemporal sharpness, and biventricular cardiac function were qualitatively and quantitatively compared between Sonic DL Cine-reconstructed images with various accelerations (4-fold to 12-fold) and fully sampled reference images. Both digital phantom and in vivo experiments demonstrate that Sonic DL Cine can accelerate cine acquisitions by up to 12-fold while preserving comparable SNR, contrast, and spatiotemporal sharpness to fully sampled reference images. Measurements of cardiac function metrics indicate that function measurements from Sonic DL Cine-reconstructed images align well with those from fully sampled reference images. In conclusion, this ...