Convolutional Neural Networks in Tomographic Image Enhancement

Convolutional Neural Networks have seen a huge rise in popularity in image applications. They have been used in medical imaging contexts to enhance the overall quality of the digital representation of the patient's scanned body region and have been very useful when dealing with limited-angle tomographic data. In this thesis, a particular type of convolutional neural network called Unet will be used as the starting point to explore the effectiveness of different networks in enhancing tomographic image reconstructions. We will first make minor tweaks to the 2-dimensional convolutional network and train it on two different datasets. After that, we will take advantage of the shape of the reconstructions we are considering to extend the convolutions to the third dimension. The scanner layout that has been considered for projecting and reconstructing volumes in this thesis indeed consits of a cone-beam geometry, whose output is a volume that approximates the original scanned object. We will then discuss the results in order to try to understand if the proposed solutions could be viable approaches for enhancing tomographic images.