Sensor-free physiological guidance for free-breathing cardiac cine MRI using implicit neural representation CineJENSE reconstruction

The aim of this study was to explore free-breathing cardiac cine images reconstructed with sensor-free physiological signals estimates. Such signals were estimated using the noise variance of the radio frequency receiver coils. Reconstructions with reference signals acquired during MR scan were compared with the sensor-free reconstructions using an extended CineJENSE algorithm. Approach: Free-breathing untriggered MRI cine data from 27 patients and 22 healthy volunteers in various slice orientations were acquired simultaneously with physiological signals using external sensors (ECG and respiratory belts). Physiological signals were estimated using the noise variance of receiver coils and specific signal processing with source separation. CineJENSE reconstruction, based on implicit neural representations was adapted to free-breathing data. Correlation coefficient between both respiration signals and F1-score of the cardiac peak detections were computed for quantitative results. The reconstructed images were visually inspected to asses their quality and presence of motion artefacts and an automatic segmentation was performed and compared to the manual segmentation with DICE scores computation. Main results: An average correlation coefficient of 0.69 ± 0.22 and F1-score of 0.73 ± 0.23 for all subjects was found. Reconstructed images quality was close to that of the reconstructed images with reference signals, although slightly lower (2.51 ± 0.8 and 2.84 ± 0.7). Dice scores for LV was 0.86 ± 0.13 for reconstructed images with sensor-free estimations compared to 0.85 ± 0.12 with external sensors. Significance: This study demonstrated overall good quality images of free-breathing acquisitions using cardiac and respiration motion estimations based on the RF noise navigator.