Computational models of Variable Resolution (VRX) CT scanners

Variable Resolution X-ray (VRX) CT scanners allow imaging of different sized anatomy at the same proportional level of detail using the same device. For example, the same scanner can be used to image large animals such as dogs, and small ones, like mice, with the same level of anatomical detail. This is achieved by tilting the x-ray detectors so that the projected size of the detecting elements is varied producing reconstructions of smaller fields of view with higher spatial resolution [1], [2]. The resolution of central region of a relatively large field of view can be further enhanced by using two or more detectors with different degrees of tilting [3], [4]. Scanners with such “target” regions are useful for following the evolution of pre-diagnosed lesions or any other situation in which a high resolution is indispensable only for a small region of the patient's anatomy, but a larger field of view is needed to avoid artifacts. A computational model of multi-arm VRX scanners was developed as a fundamental aid in the study and development of these devices [4], [5]. This work studies the effect of varying several parameters of the computational model on the quality of the reconstructed image. This will affect the usefulness of the simulator in predicting the quality of the images produced by the scanner being modeled. The parameters studied include the number of X-ray beams per detector cell, the number of energy bins in which the incoming polychromatic beam is divided, and the number of views used in the reconstruction. All these parameters will affect the performance of the simulator both in terms of the time required to perform the computations, and the accuracy of the resulting image. The ideal combination of parameters will allow for faster computation without compromising significantly the resulting quality.