This work proposes a method for the geometric calibration of photon counting detector (PCD) based cone-beam CT systems. The method iteratively searches for the optimal geometric system parameters in the reconstruction domain. It involves the reconstruction of metal ball bearings (BBs) placed in random and non-overlapping locations in the angular projection images. The PCD-based cone-beam system is assumed to have a mechanically stable rotation center, and the PCD to have no severe out-of-plane rotations (< 2°). In the reconstruction domain, a figure of merit (FOM) is defined based on the BBs mean sphericity (Ψ) and standard deviation ( 𝜎𝜎𝐵𝐵𝐵𝐵) among their estimated volumes. Computer simulations were performed to test the validity of the method. Results from computer simulations revealed that the proposed method is valid and easy to implement. The estimated geometric parameters yielded values of Ψ close to unity with minimum 𝜎𝜎𝐵𝐵𝐵𝐵 enabling the FOM to produce system parameters close to the defined ground truth in simulations.
We demonstrate a quadrant detector-based method for aligning image sensors to the optical axis of augmented reality (AR) and virtual reality (VR) head mounted displays (HMDs). The sensor location that gives optimal image quality is known as the eyepoint. While a wide variety of methods have been explored for determining the eyepoint, the sensitivity and results of these methods depend on the optical design of the HMD. In our quadrant detector-based method, a test pattern consisting of dots is displayed on the HMD and imaged by a camera. The signal from the camera is divided into four quadrants and the camera is positioned at the eyepoint when the signal difference between the quadrants is minimized. This method has the advantage of giving the direction of the eyepoint from a single measurement without mapping the eyebox of the HMDs. The measurements were performed using an FDA developed setup for characterizing HMDs with the flexibility to incorporate a variety of light measuring devices and HMDs using commercially available components, which is described in detail. These results demonstrate a generalizable eyepoint alignment method, which allows for simpler automation of aligning the camera to the eyepoint.
X-ray photon counting is a novel imaging technology gaining increasing academic and industrial interest due to its potential for high-resolution computed tomography (CT) and spectral CT. These photon-counting detector systems could have numerous benefits in medical imaging because they provide a substantial increase in the amount of available raw imaging information (e.g., multi-energy information). However, new algorithms are needed for using this information to increase the accuracy of diagnostic decisions. In this work, we focused on addressing technical challenges to consider when designing and building a laboratory benchtop for photon counting CT systems. We developed a photon counting cone-beam CT system containing a photon counting detector with a small detective area based on a translate-rotate geometry and a step-and-shoot acquisition mode. To control and synchronize the CT system components, we developed a desktop computer program with a user-friendly graphical user interface. Furthermore, we established a fully automated method for estimating some of the parameters that describe the geometry of the cone-beam CT system. This method is based on an iterative optimization technique in which the reconstructed image of a small sphere is evaluated to find the required geometrical parameters for better image reconstruction. Finally, we used images reconstructed from different scans to confirm the methodology used in the study.
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