MO-AB-BRA-10: Super High Temporal Resolution Cardiac CT Imaging Using SMART-RECON.
PURPOSE
Coronary CT angiography is a challenging task currently limited by the achievable temporal resolution of modern MDCT scanners. In this work, a highly innovative method has been developed and validated to improve temporal resolution of the MDCT by a factor of four with the newly developed SMART-RECON method to enable high quality coronary CTA exams. The primary purpose of this paper is to investigate the relationships between: (1) SMART-RECON and the motion pattern; (2) SMART-RECON and the average speed of moving vessels; (3) SMART-RECON and the position and direction of moving vessels.


METHODS
Using data acquired from a short scan angular range, the entire cardiac window is divided into 4-5 narrower cardiac windows, each corresponding to a 60-degree angular sector. These 4-5 sub-cardiac phase images can be jointly reconstructed with SMART-RECON to globally improve temporal resolution and noise properties. A hybrid phantom consisting of realistic cardiac anatomy and eight moving objects was constructed to validate the method under a wide variety of conditions and studied quantitatively. Additionally, in vivo data from twenty human subjects were used to demonstrate that SMART-RECON can significantly improve the quality of CTA using a Discovery CT 750 HD (GE Healthcare, WI, USA) with 350 ms gantry rotation time.


RESULTS
The performance of the proposed SMART-RECON cardiac CT imaging method is independent of motion speed, orientation, and location as long as there is a motionless phase that corresponds to 60° angular range. In contrast, the currently available FBP cardiac reconstruction with Parker weights demonstrates significant motion artifacts. The human subject results also demonstrate the significant improvement of coronary CTA quality cross all subjects.


CONCLUSION
With a single short-scan acquisition, SMART-RECON can be used to systematically improve the temporal resolution for MDCT cardiac CT imaging by a factor of 4 with no prior knowledge of motion. Funding support: This work was support in part by GE Healthcare. Disclosures: Y. Li: None. X. Cao, Z. Xing, X. Sun and H. Jiang: GE employee. G. Chen: Research contract with GE Healthcare; Research contract with Siemens AX; Royalty received from GE Healthcare.