MARS-CT: Biomedical Spectral X-ray Imaging with Medipix
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
Computed Tomography is one of the most important image modalities in medical imaging nowadays. Recent developments have led to a new acquisition technique called 'dual-energy', where images are taken with different x-ray spectra. This enables for the first time spectral information in the CT dataset.
Our approach was to use an energy resolving detector (Medipix) and investigate its potential in the medical imaging domain. Images are taken in different energy bins. For acquisition of the data, a CT scanner called 'Medipix All Resolution System' (MARS) scanner was constructed. It was upgraded to achieve better image quality as well as faster scan time and a stable operation.
In medical imaging, it is important to achieve a high contrast and a good detail recognition at a low dose. Therefore, it is common practice to use contrast agents to highlight certain regions of the body like e.g. the vascular system. But with a broad spectrum acquisition, it is often impossible to distinguish highly absorbing body elements like bones from the contrast agent. We target this problem by a contrast agent study using different energy bins.
This so called spectral contrast agent study has been conducted with small animals using the MARS scanner. The data has been processed to create an optimal CT reconstruction. The image enhancement techniques consist of corrections for noisy pixels, intensity fluctuations and eliminating streaks in the sinograms to reduce ring artifacts.
In order to evaluate the data, we used two methods of material identification. The material reconstruction method works on projection data and uses a maximum-likelihood estimation to reconstruct images of base materials. The second method, the principal component analysis (PCA), identifies the relevant information from the spectral dataset in a few derived variables that account for most of the variance in the dataset. This resulted in images with enhanced contrast and removed redundancies. It is possible to combine these images in one colour image where anatomical structures are shown in good detail and certain materials show up in different colors.
Based on this new information from spectral data, we could show that it is possible to distinguish the spinal bone from contrast agent.