Essential elements of proton computed tomography for practical applications.

Abstract

The work presented in this dissertation all pertains to developments of proton computed tomography (pCT) and the elements essential to its viability as a clinical imaging modality. This includes methodological and implementational developments for reducing reconstruction time and improving pCT image quality, each advancing pCT towards clinical viability. The corresponding methods are presented in the chronological order of their development. Hull-detection, a method for differentiating voxels internal and external to an object, is presented first. Hull-detection was specifically developed for pCT as a preferable means for obtaining a binary image of the object, a preconditioning step often referred to as object detection. The concept of hull-detection, similar to the way a sculptor chisels away portions of material to produce the desired sculpture, is that voxels along the paths of protons that completely miss the object can be carved away to reveal the object hull. However, this neglects to account for the ramifications of uncertainties in the data, which was accounted for in different ways. Several hull-detection algorithms were developed and compared to the classic object detection method based on thresholding the filtered backprojection image. The second topic presented is efficiently implementing the most-likely path (MLP) formalism for pCT. This formalism was developed to more accurately approximate proton paths within an object, increasing the achievable spatial resolution. Computing the MLP is, by far, the most computationally expensive task performed during image reconstruction, making it the biggest hurdle to achieving clinically viable image reconstruction times (below 10 minutes). A computationally efficient implementation of the MLP was developed by simplifying the associated equations and incorporating several software design principles to reduce the number of compute operations and improve numerical stability. The final topic presented is the incorporation of recent advancements of total variation superiorization (TVS) into pCT. A fixed parameter version of TVS was initially incorporated into the feasibility-seeking algorithms of pCT, which included a step verifying successful TV reduction. Presented here is the modern version of TVS applied to pCT, with user-control of parameters, removal of the verification step, and additional option to perform repeated perturbations.