Noninvasive and multi-purposed, with uses ranging from fetal monitoring to cancer detection, medical ultrasound is one of the most widely utilized diagnostic tools in medicine today. Recent trends to proliferate its use and improve patient care require that ultrasound systems become more portable and less expensive. However, high resolution images needed in medical ultrasound rely on multichannel ultrasonic transducers requiring expensive, dedicated hardware to perform beamforming. This dissertation explores the reduction of hardware requirements for multi-channel ultrasound image formation through the use of software-based beamforming.
First, the requirements and computational complexity of software-based ultrasound image formation are investigated. Then, a new beamforming algorithm is presented, introducing sparse upsampling as a means to reduce computational complexity. Performance and noise implications of this new algorithm are also investigated. Finally, a framework for parallelizing beamforming and other image processing functions is introduced. This framework is used to implement real-time, software-based ultrasound image formation for an annular array use case. This new beamforming algorithm, used in conjunction with the parallel processing framework eliminates the need for hardware-based beamforming, reducing the overall cost and complexity of multichannel ultrasound probes.
","abstract_html":"<p>Noninvasive and multi-purposed, with uses ranging from fetal monitoring to cancer detection, medical ultrasound is one of the most widely utilized diagnostic tools in medicine today. Recent trends to proliferate its use and improve patient care require that ultrasound systems become more portable and less expensive. However, high resolution images needed in medical ultrasound rely on multichannel ultrasonic transducers requiring expensive, dedicated hardware to perform beamforming. This dissertation explores the reduction of hardware requirements for multi-channel ultrasound image formation through the use of software-based beamforming.</p><p>First, the requirements and computational complexity of software-based ultrasound image formation are investigated. Then, a new beamforming algorithm is presented, introducing sparse upsampling as a means to reduce computational complexity. Performance and noise implications of this new algorithm are also investigated. Finally, a framework for parallelizing beamforming and other image processing functions is introduced. This framework is used to implement real-time, software-based ultrasound image formation for an annular array use case. This new beamforming algorithm, used in conjunction with the parallel processing framework eliminates the need for hardware-based beamforming, reducing the overall cost and complexity of multichannel ultrasound probes.</p>","abstract_has_math":false,"creators":["Steelman, William Avery"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["William D Richard","Roger D Chamberlain, Robert E Morley, David E Taylor"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-15T07:00:00Z","date_published":"2013-05-15T07:00:00Z","updated_at":"2026-07-24T06:13:55Z","subjects":["beamforming","multi-threaded","real-time","software","ultrasound","Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/40"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/40","href":"https://openscholarship.wustl.edu/eng_etds/40","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/K70R9MB5","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["William D Richard","Roger D Chamberlain, Robert E Morley, David E Taylor"]},{"key":"dc:creator","label":"Author","values":["Steelman, William Avery"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2115-01-16T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["beamforming","multi-threaded","real-time","software","ultrasound","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/K70R9MB5","https://openscholarship.wustl.edu/eng_etds/40"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Permanent URL: https://doi.org/10.7936/K70R9MB5"]},{"key":"dc:description.abstract","label":"Abstract","values":["Noninvasive and multi-purposed, with uses ranging from fetal monitoring to cancer detection, medical ultrasound is one of the most widely utilized diagnostic tools in medicine today. Recent trends to proliferate its use and improve patient care require that ultrasound systems become more portable and less expensive. However, high resolution images needed in medical ultrasound rely on multichannel ultrasonic transducers requiring expensive, dedicated hardware to perform beamforming. This dissertation explores the reduction of hardware requirements for multi-channel ultrasound image formation through the use of software-based beamforming.
First, the requirements and computational complexity of software-based ultrasound image formation are investigated. Then, a new beamforming algorithm is presented, introducing sparse upsampling as a means to reduce computational complexity. Performance and noise implications of this new algorithm are also investigated. Finally, a framework for parallelizing beamforming and other image processing functions is introduced. This framework is used to implement real-time, software-based ultrasound image formation for an annular array use case. This new beamforming algorithm, used in conjunction with the parallel processing framework eliminates the need for hardware-based beamforming, reducing the overall cost and complexity of multichannel ultrasound probes.
"]},{"key":"dc:title","label":"Title","values":["Real-time, Software-based Ultrasound Image Formation for Array Transducer Systems"]}]}],"canonical_facts":{"dc:contributor":["William D Richard","Roger D Chamberlain, Robert E Morley, David E Taylor"],"dc:creator":["Steelman, William Avery"],"dc:date.available":["2115-01-16T08:00:00Z"],"dc:description":["Permanent URL: https://doi.org/10.7936/K70R9MB5"],"dc:description.abstract":["Noninvasive and multi-purposed, with uses ranging from fetal monitoring to cancer detection, medical ultrasound is one of the most widely utilized diagnostic tools in medicine today. Recent trends to proliferate its use and improve patient care require that ultrasound systems become more portable and less expensive. However, high resolution images needed in medical ultrasound rely on multichannel ultrasonic transducers requiring expensive, dedicated hardware to perform beamforming. This dissertation explores the reduction of hardware requirements for multi-channel ultrasound image formation through the use of software-based beamforming.
First, the requirements and computational complexity of software-based ultrasound image formation are investigated. Then, a new beamforming algorithm is presented, introducing sparse upsampling as a means to reduce computational complexity. Performance and noise implications of this new algorithm are also investigated. Finally, a framework for parallelizing beamforming and other image processing functions is introduced. This framework is used to implement real-time, software-based ultrasound image formation for an annular array use case. This new beamforming algorithm, used in conjunction with the parallel processing framework eliminates the need for hardware-based beamforming, reducing the overall cost and complexity of multichannel ultrasound probes.
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