The detector panel on a typical CT machine today is made of more than 500 detector boards, nicknamed chiclets. Each chiclet contains a number of detectors (i.e., pixels). In the manufacturing process, the chiclets on the panel need to go through an iterative test, swap, and test (TST) process, till some image quality level is achieved. Currently, this process is largely manual and can take hours to several days to complete. This is inefficient and the results can also be inconsistent. In this work, we investigate techniques that can be used to automate the iterative TST process. Specifically, we develop novel prediction techniques that can be used to simulate the iterative TST process. Our results indicate that deep neural networks produce significantly better results than linear regression in the more difficult prediction scenarios.
X-ray machines are widely used for medical imaging and their cost is highly dependent on their image resolution.
Due to economic reasons, lower-resolution (lower-res) machines still have a lot of customers, especially in developing
economies. Software based resolution enhancement can potentially enhance the capabilities of the lower-res
machines without significantly increasing their cost hence, is highly desirable. In this work, we developed an
algorithm for X-ray image resolution enhancement. In this algorithm, the fractal idea and cross-resolution patch
matching are used to identify low-res patches that can be used as samples for high-res patch/pixel estimation.
These samples are then used to generate a prior distribution and used in a Bayesian MAP (maximum a posteriori)
optimization to produce the high-res image estimate. The efficacy of our algorithm is demonstrated by
Compressed sensing can recover a signal that is sparse in some way from a small number of samples. For computed tomography (CT) imaging, this has the potential to obtain good reconstruction from a smaller number of projections or views, thereby reducing the amount of radiation that a patient is exposed to In this work, we applied compressed sensing to fan beam CT image reconstruction, which is a special case of an important 3-D CT problem (cone beam CT). We compared the performance of two compressed sensing algorithms, denoted as the LP and the QP, in simulation. Our results indicate that the LP generally provides smaller reconstruction error and converges faster; therefore, it is preferable.