Proton computed tomography (pCT) is an image modality that will improve treatment planning for patients receiving proton radiation therapy compared with the current techniques, which are based on X-ray CT. Images are reconstructed in pCT by solving a large and sparse system of linear equations. The size of the system necessitates matrix-partitioning and parallel reconstruction algorithms to be implemented across some sort of cluster computing architecture. The prototypical algorithm to solve the pCT system is the algebraic reconstruction technique (ART) that has been modified into parallel versions called block-iterative-projection (BIP) methods and string-averaging-projection (SAP) methods. General purpose graphics processing units (GPGPUs) have hundreds of stream processors for massively parallel calculations. A GPGPU cluster is a set of nodes, with each node containing a set of GPGPUs. This thesis describes a proton simulator that was developed to generate realistic pCT data sets. Simulated data sets were used to compare the performance of a BIP implementation against a SAP implementation on a single GPGPU with the data stored in a sparse matrix structure called the compressed sparse row (CSR) format. Both BIP and SAP algorithms allow for parallel computation by creating row partitions of the pCT linear system. The difference between these two general classes of algorithms is that BIP permits parallel computations within the row partitions yet sequential computations between the row partitions, whereas SAP permits parallel computations between the row partitions yet sequential computations within the row partitions. This thesis also introduces a general partitioning scheme to be applied to a GPGPU cluster to achieve a pure parallel ART algorithm while providing a framework for column partitioning to the pCT system, as well as show sparse visualization patterns that can be found via specified ordering of the equations within the matrix.
","abstract_html":"<p>Proton computed tomography (pCT) is an image modality that will improve treatment planning for patients receiving proton radiation therapy compared with the current techniques, which are based on X-ray CT. Images are reconstructed in pCT by solving a large and sparse system of linear equations. The size of the system necessitates matrix-partitioning and parallel reconstruction algorithms to be implemented across some sort of cluster computing architecture. The prototypical algorithm to solve the pCT system is the algebraic reconstruction technique (ART) that has been modified into parallel versions called block-iterative-projection (BIP) methods and string-averaging-projection (SAP) methods. General purpose graphics processing units (GPGPUs) have hundreds of stream processors for massively parallel calculations. A GPGPU cluster is a set of nodes, with each node containing a set of GPGPUs. This thesis describes a proton simulator that was developed to generate realistic pCT data sets. Simulated data sets were used to compare the performance of a BIP implementation against a SAP implementation on a single GPGPU with the data stored in a sparse matrix structure called the compressed sparse row (CSR) format. Both BIP and SAP algorithms allow for parallel computation by creating row partitions of the pCT linear system. The difference between these two general classes of algorithms is that BIP permits parallel computations within the row partitions yet sequential computations between the row partitions, whereas SAP permits parallel computations between the row partitions yet sequential computations within the row partitions. This thesis also introduces a general partitioning scheme to be applied to a GPGPU cluster to achieve a pure parallel ART algorithm while providing a framework for column partitioning to the pCT system, as well as show sparse visualization patterns that can be found via specified ordering of the equations within the matrix.</p>","abstract_has_math":false,"creators":["witt, micah"],"institution":null,"degree_name":"Master of Science in Computer Science","degree_level":"Thesis","degree_discipline":"School of Computer Science and Engineering","degree_department":null,"school":null,"contributors":["Ernesto Gomez"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-03-01T08:00:00Z","date_published":"2014-03-01T08:00:00Z","updated_at":"2026-07-24T01:52:37Z","subjects":["Proton Computed Tomography","General Purpose Graphics Processing Unit","Proton Simulator","Algebraic Reconstruction Technique","String Averaging Projection","Block Iterative Projection","Numerical Analysis and Scientific Computing","Systems Architecture"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.lib.csusb.edu/etd/2","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ernesto Gomez"]},{"key":"dc:creator","label":"Author","values":["witt, micah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-02-04T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Computer Science and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Proton Computed Tomography","General Purpose Graphics Processing Unit","Proton Simulator","Algebraic Reconstruction Technique","String Averaging Projection","Block Iterative Projection","Numerical Analysis and Scientific Computing","Systems Architecture"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.lib.csusb.edu/etd/2"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Proton computed tomography (pCT) is an image modality that will improve treatment planning for patients receiving proton radiation therapy compared with the current techniques, which are based on X-ray CT. Images are reconstructed in pCT by solving a large and sparse system of linear equations. The size of the system necessitates matrix-partitioning and parallel reconstruction algorithms to be implemented across some sort of cluster computing architecture. The prototypical algorithm to solve the pCT system is the algebraic reconstruction technique (ART) that has been modified into parallel versions called block-iterative-projection (BIP) methods and string-averaging-projection (SAP) methods. General purpose graphics processing units (GPGPUs) have hundreds of stream processors for massively parallel calculations. A GPGPU cluster is a set of nodes, with each node containing a set of GPGPUs. This thesis describes a proton simulator that was developed to generate realistic pCT data sets. Simulated data sets were used to compare the performance of a BIP implementation against a SAP implementation on a single GPGPU with the data stored in a sparse matrix structure called the compressed sparse row (CSR) format. Both BIP and SAP algorithms allow for parallel computation by creating row partitions of the pCT linear system. The difference between these two general classes of algorithms is that BIP permits parallel computations within the row partitions yet sequential computations between the row partitions, whereas SAP permits parallel computations between the row partitions yet sequential computations within the row partitions. This thesis also introduces a general partitioning scheme to be applied to a GPGPU cluster to achieve a pure parallel ART algorithm while providing a framework for column partitioning to the pCT system, as well as show sparse visualization patterns that can be found via specified ordering of the equations within the matrix.
"]},{"key":"dc:title","label":"Title","values":["Proton Computed Tomography: Matrix Data Generation Through General Purpose Graphics Processing Unit Reconstruction"]}]}],"canonical_facts":{"dc:contributor":["Ernesto Gomez"],"dc:creator":["witt, micah"],"dc:date.available":["2014-02-04T08:00:00Z"],"dc:description.abstract":["Proton computed tomography (pCT) is an image modality that will improve treatment planning for patients receiving proton radiation therapy compared with the current techniques, which are based on X-ray CT. Images are reconstructed in pCT by solving a large and sparse system of linear equations. The size of the system necessitates matrix-partitioning and parallel reconstruction algorithms to be implemented across some sort of cluster computing architecture. The prototypical algorithm to solve the pCT system is the algebraic reconstruction technique (ART) that has been modified into parallel versions called block-iterative-projection (BIP) methods and string-averaging-projection (SAP) methods. General purpose graphics processing units (GPGPUs) have hundreds of stream processors for massively parallel calculations. A GPGPU cluster is a set of nodes, with each node containing a set of GPGPUs. This thesis describes a proton simulator that was developed to generate realistic pCT data sets. Simulated data sets were used to compare the performance of a BIP implementation against a SAP implementation on a single GPGPU with the data stored in a sparse matrix structure called the compressed sparse row (CSR) format. Both BIP and SAP algorithms allow for parallel computation by creating row partitions of the pCT linear system. The difference between these two general classes of algorithms is that BIP permits parallel computations within the row partitions yet sequential computations between the row partitions, whereas SAP permits parallel computations between the row partitions yet sequential computations within the row partitions. This thesis also introduces a general partitioning scheme to be applied to a GPGPU cluster to achieve a pure parallel ART algorithm while providing a framework for column partitioning to the pCT system, as well as show sparse visualization patterns that can be found via specified ordering of the equations within the matrix.
"],"dc:identifier":["https://scholarworks.lib.csusb.edu/etd/2"],"dc:subject":["Proton Computed Tomography","General Purpose Graphics Processing Unit","Proton Simulator","Algebraic Reconstruction Technique","String Averaging Projection","Block Iterative Projection","Numerical Analysis and Scientific Computing","Systems Architecture"],"dc:title":["Proton Computed Tomography: Matrix Data Generation Through General Purpose Graphics Processing Unit Reconstruction"],"thesis:degree_discipline":["School of Computer Science and Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science"]},"updated_at":"2026-07-24T01:52:37Z"}