X-ray computed tomography (CT) imaging is an important medical diagnostic technique where a rotating X-ray source and detector are used to image the patient from all directions, allowing a 3D map of the tissue density to be reconstructed. This imaging modality is widely used in the clinic with an estimated 85 million examinations per year in the USA alone in 2012. In children, CT examinations are used for diagnosing congenital heart disease, finding tumors, identifying post-traumatic changes in the brain and planning surgery, among other applications. However, the radiation dose must be kept as low as reasonably achievable to minimize the risk of cancer, in particular for children. One way to lower the radiation dose is to employ photon-counting detectors. This new detector type promises several improvements: lower dose, higher spatial resolution and better measurement accuracy in the images, which is expected to translate to improved diagnostic accuracy and lower cancer risk for the patient. Also, the ability of this detector type to remove beam-hardening artifacts allows them to be used with low X-ray tube voltages (70-100 kV), which is preferable when imaging small patients. These advantages are particularly important for imaging of small children.
The purpose of this project is to use the photon-counting silicon-strip detector with a new reconstruction algorithm, combining fast reconstruction time with optimal dose utilization, to show a proof of principle for a new technique: low-dose photon counting CT. To achieve this, the project has the following specific objectives:
Objective 1 is to develop a computationally inexpensive reconstruction algorithm for low-dose photon counting CT that makes optimal use of the measured data in terms of low-contrast detectability, spatial resolution and accuracy of the reconstructed CT numbers.
Objective 2 is to compare the image quality of low-dose photon-counting CT images to a state-of-the-art CT scanner in clinical use, at equal dose.
Objective 3 is to compare the detectability in photon-counting low-dose CT images of phantoms to the detectability of state-of-the-art x-ray radiography images at equal dose.
However, when carrying out the action, it was decided to change objective 3 to "Develop a deep-learning based image reconstruction algorithm for material-selective photon-counting imaging and evaluate this method in a simulation study."
