A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers

Low-dose Proton Computed Tomography (pCT) is an evolving imaging modality that is used in proton therapy planning which addresses the range uncertainty problem. The goal of pCT is generating a 3D map of Relative Stopping Power (RSP) measurements with high accuracy within clinically required time frames. Generating accurate RSP values within the shortest amount of time is considered a key goal when developing a pCT software. The existing pCT softwares have successfully met this time frame and even succeeded this time goal, but requiring clusters with hundreds of processors. This paper describes a novel reconstruction technique using two Graphics Processing Unit (GPU) cores, such as is available on a single Nvidia P100. The proposed reconstruction technique is tested on both simulated and experimental datasets and on two different systems namely Nvidia K40 and P100 GPUs from IBM and Cray. The experimental results demonstrate that our proposed reconstruction method meets both the timing and accuracy with the benefit of having reasonable cost, and efficient use of power.Comment: IEEE NSS/MIC 201

Robust and efficient methods for proton computed tomography.

Proton computed tomography (pCT) is a recent promising imaging modality with the goal of generating accurate 3D maps of relative stopping power (RSP) with respect to water. Since the early developments of this imaging technique in 1970's, there have been significant improvements regarding the reconstruction of accurate RSP which makes pCT a reliable alternative to X-ray CT for planning proton therapy treatments. There are several conditions in pCT that can negatively affect the accuracy of pCT images. The goal of this dissertation is developing efficient image reconstruction methods generating accurate RSP values under both normal and critical conditions

A fast seeding technique for k-means algorithm.

The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers are chosen, have a great effect on speed and quality of k-means. One of the most popular seeding techniques is k-means++ initialization, but this method needs k passes over the dataset. The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++

Reconstructing highly accurate relative stopping powers in proton computed tomography

Proton computed tomography (pCT) is an evolving tomographic imaging modality with applications in proton and ion therapy. The method allows direct reconstruction of relative stopping power of patient tissues in a 3D-fashion. The pCT collaboration has built first experimental prototypes of pCT scanning systems [1] and has developed approaches to reconstruct proton CT images based on registering the coordinates and water equivalent path length (WEPL) of individual protons traversing the scanned volume. From these data one reconstructs the object boundary (hull) and initial image based on filtered back projection (FBP), calculates a most likely path (MLP) for each proton, and improves the initial image iteratively by solving a large linear system of equations of the form Ax = b using an iterative projection algorithm [2]

Incorporating robustness in diagonally-relaxed orthogonal projections method for proton computed tomography

Iterative algorithms such as ART, DROP, and CARP are commonly used in reconstructing computed tomography images, but only account for errors in the measurements. Errors in the predicted path and intersection lengths, or even blocks of missing measurements can result in degraded image quality. Robust techniques allow for errors in other areas of the model and produce good images that show less sensitivity. In this paper we introduce a robust version of DROP and compare its performance advantages to the standard DROP algorithm on on real data

Recommendations Made Easy

Fueled by ever-growing data, the need to provide recommendations for consumers, and the considerable domain knowledge required to implement distributed large scale graph solutions we sought to provide recommendations for users with minimal required knowledge. For this reason in this paper we implement a generalizable 'API-like' access to collaborative filtering. Three algorithms are introduced with three execution plans in order to accomplish the collaborative filtering functionality. Execution is based on memory constraints for scalability and our initial tests show promising results. We believe this method of large-scale generalized 'API-like' graph computation provides not only good trade-off between performance and required knowledge, but also the future of distributed graph computation

Results from a Prototype Proton-CT Head Scanner

We are exploring low-dose proton radiography and computed tomography (pCT) as techniques to improve the accuracy of proton treatment planning and to provide artifact-free images for verification and adaptive therapy at the time of treatment. Here we report on comprehensive beam test results with our prototype pCT head scanner. The detector system and data acquisition attain a sustained rate of more than a million protons individually measured per second, allowing a full CT scan to be completed in six minutes or less of beam time. In order to assess the performance of the scanner for proton radiography as well as computed tomography, we have performed numerous scans of phantoms at the Northwestern Medicine Chicago Proton Center including a custom phantom designed to assess the spatial resolution, a phantom to assess the measurement of relative stopping power, and a dosimetry phantom. Some images, performance, and dosimetry results from those phantom scans are presented together with a description of the instrument, the data acquisition system, and the calibration methods

A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers

Low-dose Proton Computed Tomography (pCT) is an evolving imaging modality that is used in proton therapy planning which addresses the range uncertainty problem. The goal of pCT is generating a 3D map of relative stopping power measurements with high accuracy within clinically required time frames. Generating accurate relative stopping power values within the shortest amount of time is considered a key goal when developing an image reconstruction software. The existing image reconstruction softwares have successfully met this time frame and even exceeded this time goal, but require clusters with hundreds of processors. This paper describes a novel reconstruction technique using two graphics processing unit devices. The proposed reconstruction technique is tested on both simulated and experimental datasets and on two different systems namely Nvidia K40 and P100 graphics processing units from IBM and Cray. The experimental results demonstrate that our proposed reconstruction method meets both the timing and accuracy with the benefit of having reasonable cost and efficient use of power