Coronary CT angiography (CCTA) is a challenging imaging task currently limited by the achievable temporal resolution of modern Multi-Detector CT (MDCT) scanners. In this paper, the recently proposed SMARTRECON method has been applied in MDCT-based CCTA imaging to improve the image quality without any prior knowledge of cardiac motion. After the prospective ECG-gated data acquisition from a short-scan angular span, the acquired data were sorted into several sub-sectors of view angles; each corresponds to a 1/4th of the short-scan angular range. Information of the cardiac motion was thus encoded into the data in each view angle sub-sector. The SMART-RECON algorithm was then applied to jointly reconstruct several image volumes, each of which is temporally consistent with the data acquired in the corresponding view angle sub-sector. Extensive numerical simulations were performed to validate the proposed technique and investigate the performance dependence.
Ray-tracing based simulation methods are widely used in modeling X-ray propagation, detection and imaging. While
most of the existing simulation methods rely on analytical modeling, a novel hybrid approach comprising of statistical
modeling and analytical approaches, is proposed here.
Our hybrid simulator is a unique combination of analytical modeling for evoking the fundamentals of X-ray transport
through ray-tracing, and a look-up-table (LUT) based approach for integrating it with the Monte Carlo simulations that
model optical photon-transport within scintillator. The LUT approach for scintillation-based X-ray detection invokes
depth-dependent gain factors to account for intra-pixel absorption and light-transport, together with incident-angle
dependent effects for inter-pixel X-ray absorption (parallax effect). The model simulates the post-patient collimator for
scatter-rejection, as an X-ray shadow on scintillator, while handling its position with respect to the pixel boundary, by a
smart over-sampling strategy for high efficiency.
We have validated this simulator for computed tomography system-simulations, by using real data from GE Brivo
CT385. The level of accuracy of image noise and spatial resolution is better than 98%. We have used the simulator for
designing the post-patient collimator, and measured modulation transfer function (MTF) for different widths of the
Validation and simulation study clearly demonstrates that the hybrid simulator is an accurate, reliable, efficient tool for
realistic system-level simulations. It could be deployed for research, design and development purposes to model any
scintillator-based X-ray imaging-system (2-dimensional and 3-dimensional), while being equally applicable for medical
and industrial imaging.
Today lowering patient radiation dose while maintaining image quality in Computed Tomography has become a very
active research field. Various iterative reconstruction algorithms have been designed to improve/maintain image quality
for low dose patient scans. Typically radiation dose variation will result in detectability variation for low contrast
objects. This paper assesses the low contrast detectability performance of the images acquired at different dose levels
and obtained using different image generation algorithms via two-alterative forced choice human observer method.
Filtered backprojection and iterative reconstruction algorithms were used in the study. Results showed that for the
objects and scan protocol used, the iterative algorithm employed in this study has similar low contrast detectability
performance compared to filtered backprojection algorithm at a 4 times lower dose level. It also demonstrated that well
controlled human observer study is feasible to assess the image quality of a CT system.