Sonic DL Cine reconstructed highly under-sampled cine datasets (up to 12-fold acceleration) while preserving SNR, contrast, sharpness, and quantification accuracy compared to fully sampled images.
Does deep learning-based reconstruction (Sonic DL Cine) preserve image quality and quantification accuracy compared to fully sampled reference images in accelerated cardiac cine imaging?
Sonic DL Cine enables up to 12-fold acceleration of cardiac cine MRI while maintaining image quality and functional quantification accuracy comparable to fully sampled images.
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 study demonstrates that Sonic DL Cine is able to reconstruct highly under-sampled (up to 12-fold acceleration) cine datasets while preserving SNR, contrast, spatiotemporal sharpness, and quantification accuracy for cardiac function measurements. It also provides a feasible approach for thoroughly evaluating the deep learning-based method.
Ma et al. (Mon,) conducted a other in Healthy volunteers and digital phantom. Sonic DL Cine (deep learning-based reconstruction) vs. Fully sampled reference images was evaluated on Image quality, spatiotemporal sharpness, and biventricular cardiac function. Sonic DL Cine reconstructed highly under-sampled cine datasets (up to 12-fold acceleration) while preserving SNR, contrast, sharpness, and quantification accuracy compared to fully sampled images.