Seminar: Optimization of Photon-Counting Dual Energy Thoracic Imaging
- Date
- January 26, 2024
- Time
- 12:00 p.m. - 1:30 p.m. ET
- Location
- KHE 225
- Open To
- Students, Faculty, Adjunct Faculty, Staff and Post-Doctoral Fellows
Student: Jeffrey Dhari
Supervisor: Dr. Jesse Tanguay
Abstract
Lung cancer is the leading cause of cancer death with the survival rates being low in its advanced stages, therefore the key to survival is detection at earlier stages. However, smaller nodules may be obscured and not detected due to overlaying bone structures. Dual-energy (DE) thoracic imaging with photon counting detectors (PCDs) enables suppressing anatomic noise due to bone structures, potentially improving the detection of lung nodules. Relative to DE approaches based on the kV-switching method done with energy-integrating detectors (EIDs), a PCD-based, single-exposure approach may reduce the potential for motion artifacts and suppress electronic noise, thereby potentially providing higher image quality. While our ultimate goal is to perform a frequency-based assessment of single-exposure DE imaging with PCDs, as a first step, the purpose of this work is to investigate contrast-to-noise ratio (CNR) by both theory and experiment. The DE CNR has been modelled for a cadmium-telluride PCD and has been validated against experimental data. The CNR was computed as a function of high energy threshold separating the low and high energy bins for tube voltages of 90 kV to 130 kV, where the threshold varied from 30 keV to ~10% of the maximum energy of the beam. The experimental study was done with a phantom built to simulate attenuation, scatter, and contrast for lung cancer imaging. There was generally good agreement between the model and experimental CNR, with the model CNR being within 5% to 15% of the experimental CNR. The model was also able to predict the trends seen in the experimental data. The energy threshold that maximizes dual-energy CNR ranges from 50 keV to 55 keV for tube voltages of 120 kV to 130 kV. Future work will focus on optimizing the frequency-based detectability index for thoracic DE images acquired in a PCD and an EID and comparing the optimal image quality between x-ray detection systems.