A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance

Objective. Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are limited clinical applications of real-time CMR for volumetric measurements because real-time images cannot provide sufficient quality for accurate segmentation. The presented work aims to develop a new deep learning approach to measuring ventricular volumes without image reconstruction and demonstrate that this 'imageless' approach would improve volumetric measurements with real-time CMR. Approach. We have developed a deep learning model for measuring ventricular volumes directly from real-time CMR raw data without image reconstruction. This novel 'imageless' deep learning model, not being as sensitive to image quality, provided reliable volumetric measurements for real-time CMR. To demonstrate 'imageless' volumetric measurements, we conducted a real-time CMR study with healthy volunteers. Several performance metrics, including mean absolute error (MAE), the Pearson correlation coefficient, and Bland-Altman analysis, were used to evaluate the proposed 'imageless' deep learning model in reference to U-net and fully convolutional neural network (FCNN) models based on conventional image reconstruction and segmentation. Main results. With the same raw data, the 'imageless' deep learning model gave a lower MAE ('imageless' <= 9.6 ml; 'image-based' >= 12.1 ml), a higher correlation coefficient ('imageless' >= 0.75; 'image-based' <= 0.51) and smaller measurement difference ranges in Bland-Altman analysis ('imageless' <= 23.1 ml; 'image-based' >= 33.8 ml). To achieve comparable performance, the 'imageless' deep learning model needed 2/3 of the raw data used in image reconstruction for U-net and FCNN models, indicating there was a gain in imaging acceleration for real-time CMR. Significance. We have demonstrated a novel deep learning framework that can provide reliable volumetric measurements from real-time CMR raw data without image reconstruction. This 'imageless' approach to real-time volumetric measurements will improve the quantitative assessment of cardiac function in clinical cardiology.