Motion compensated iterative reconstruction of a region of interest in cardiac cone-beam CT

Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT): Developing the next generation of cardiac cone beam CT imaging

PURPOSE

An important factor when considering the use of interventional cone beam computed tomography (CBCT) imaging during cardiac procedures is the trade-off between imaging dose and image quality. Accordingly, Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) presents an alternative acquisition method, adapting the gantry velocity and projection rate of CBCT imaging systems in accordance with a patient's electrocardiogram (ECG) signal in real-time. The aim of this study was to experimentally investigate that ACROBEAT acquisitions deliver improved image quality compared to conventional cardiac CBCT imaging protocols with fewer projections acquired.

METHODS

The Siemens ARTIS pheno (Siemens Healthcare, GmbH, Germany), a robotic CBCT C-arm system, was used to compare ACROBEAT with a commercially available conventional cardiac imaging protocol that utilizes multisweep retrospective ECG-gated acquisition. For ACROBEAT, real-time control of the gantry position was enabled through the Siemens Test Automation Control system. ACROBEAT and conventional image acquisitions of the CIRS Dynamic Cardiac Phantom were performed, using five patient-measured ECG traces. The traces had average heart rates of 56, 64, 76, 86, and 100 bpm. The total number of acquired projections was compared between the ACROBEAT and conventional acquisition methods. The image quality was assessed via the contrast-to-noise ratio (CNR), structural similarity index (SSIM), and root-mean square error (RMSE).

RESULTS

Compared to the conventional protocol, ACROBEAT reduced the total number of projections acquired by 90%. The visual image quality provided by the ACROBEAT acquisitions, across all traces, matched or improved compared to conventional acquisition and was independent of the patient's heart rate. Across all traces, ACROBEAT averaged 1.4 times increase in the CNR, a 23% increase in the SSIM and a 29% decrease in the RMSE compared to conventional and was independent of the patient's heart rate.

CONCLUSION

Adaptive patient imaging is feasible on a clinical robotic CBCT system, delivering higher quality images while reducing the number of projections acquired by 90% compared to conventional cardiac imaging protocols.

An Intelligent Body Posture Analysis Model Using Multi-Sensors for Long-Term Physical Rehabilitation

Sensors can be installed on various body parts to provide information for computer diagnosis to identify the current body state. However, as human posture is subject to gravity, the direction of the force on each limb differs. For example, the directions of gravitational force on legs and trunk differ. In addition, each person's height and structure of limbs differs, hence, the acceleration and rotation resulted from such differences on force and length of the limbs of a person in motion would be different, and be presented by cases of different postures. Thus, how to present body postures through skeleton system equations, and achieve an long-term physical rehabilitation, according to the different limb characteristics of each person, is a challenging research issue. This paper proposes a novel scheme named as "Intelligent Body Posture Analysis Model", which uses multiple acceleration sensors and gyroscopes to detect body motion patterns. The effectiveness of the proposed scheme is proved by conducting a large number of practical experiments and tests.