Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction
Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessita...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on medical imaging 2019-08, Vol.38 (8), p.1935-1947 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1947 |
|---|---|
| container_issue | 8 |
| container_start_page | 1935 |
| container_title | IEEE transactions on medical imaging |
| container_volume | 38 |
| creator | Prakash, Jaya Sanny, Dween Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
| description | Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, ℓ 1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, ℓ 1 -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, ℓ 1 -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images. |
| doi_str_mv | 10.1109/TMI.2018.2889314 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_8586926</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8586926</ieee_id><sourcerecordid>2268432880</sourcerecordid><originalsourceid>FETCH-LOGICAL-c389t-3e94e4241e7c38ca845adf80c4e45b2576d73c2f39e4cb90143c831e36e59f863</originalsourceid><addsrcrecordid>eNpdkM9LwzAcxYMobk7vgiAFL14683vJUYbTwkTRCd5Kln27dbTLTFpB_3ozN3fwlPDyeY-Xh9A5wX1CsL6ZPGZ9ionqU6U0I_wAdYkQKqWCvx-iLqYDlWIsaQedhLDEmHCB9THqMCwUFYx30evIG9uUbmWq5AXmbWV8-W02QtK4JKvX3n1C8rxwjTPWtaEpbTJxtZt7s17Ee1abOUSndavQ-PY36hQdFaYKcLY7e-htdDcZPqTjp_tseDtOLVO6SRloDpxyAoMoWKO4MLNCYRtVMaViIGcDZmnBNHA71bE8s4oRYBKELpRkPXS9zY0lP1oITV6XwUJVmRXEqjklMpqUVCyiV__QpWt9_HSkqFScxQFxpPCWst6F4KHI176sjf_KCc43g-dx8HwzeL4bPFoud8HttIbZ3vC3cAQutkAJAPtnJZTUVLIf80eEUA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2268432880</pqid></control><display><type>article</type><title>Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction</title><source>IEEE Electronic Library (IEL)</source><creator>Prakash, Jaya ; Sanny, Dween ; Kalva, Sandeep Kumar ; Pramanik, Manojit ; Yalavarthy, Phaneendra K.</creator><creatorcontrib>Prakash, Jaya ; Sanny, Dween ; Kalva, Sandeep Kumar ; Pramanik, Manojit ; Yalavarthy, Phaneendra K.</creatorcontrib><description>Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, ℓ 1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, ℓ 1 -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, ℓ 1 -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2018.2889314</identifier><identifier>PMID: 30582534</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Acoustic noise ; Acoustics ; Algorithms ; Animals ; Biological tissues ; Brain - diagnostic imaging ; compressive sensing ; Computer Simulation ; Data recovery ; Detectors ; fractional methods ; Ill posed problems ; Image contrast ; Image processing ; Image Processing, Computer-Assisted - methods ; Image reconstruction ; Imaging ; Initial pressure ; Noise ; Noise levels ; Noise standards ; Phantoms, Imaging ; Photoacoustic effect ; Photoacoustic Techniques - methods ; Photoacoustic tomography ; Pressure ; Rats ; Regularization ; regularization theory ; Signal to noise ratio ; Stress concentration ; Tomography - methods ; Variation</subject><ispartof>IEEE transactions on medical imaging, 2019-08, Vol.38 (8), p.1935-1947</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-3e94e4241e7c38ca845adf80c4e45b2576d73c2f39e4cb90143c831e36e59f863</citedby><cites>FETCH-LOGICAL-c389t-3e94e4241e7c38ca845adf80c4e45b2576d73c2f39e4cb90143c831e36e59f863</cites><orcidid>0000-0003-1034-7246 ; 0000-0003-2865-5714 ; 0000-0002-2375-154X ; 0000-0003-4810-352X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8586926$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8586926$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30582534$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Prakash, Jaya</creatorcontrib><creatorcontrib>Sanny, Dween</creatorcontrib><creatorcontrib>Kalva, Sandeep Kumar</creatorcontrib><creatorcontrib>Pramanik, Manojit</creatorcontrib><creatorcontrib>Yalavarthy, Phaneendra K.</creatorcontrib><title>Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, ℓ 1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, ℓ 1 -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, ℓ 1 -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.</description><subject>Acoustic noise</subject><subject>Acoustics</subject><subject>Algorithms</subject><subject>Animals</subject><subject>Biological tissues</subject><subject>Brain - diagnostic imaging</subject><subject>compressive sensing</subject><subject>Computer Simulation</subject><subject>Data recovery</subject><subject>Detectors</subject><subject>fractional methods</subject><subject>Ill posed problems</subject><subject>Image contrast</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Initial pressure</subject><subject>Noise</subject><subject>Noise levels</subject><subject>Noise standards</subject><subject>Phantoms, Imaging</subject><subject>Photoacoustic effect</subject><subject>Photoacoustic Techniques - methods</subject><subject>Photoacoustic tomography</subject><subject>Pressure</subject><subject>Rats</subject><subject>Regularization</subject><subject>regularization theory</subject><subject>Signal to noise ratio</subject><subject>Stress concentration</subject><subject>Tomography - methods</subject><subject>Variation</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkM9LwzAcxYMobk7vgiAFL14683vJUYbTwkTRCd5Kln27dbTLTFpB_3ozN3fwlPDyeY-Xh9A5wX1CsL6ZPGZ9ionqU6U0I_wAdYkQKqWCvx-iLqYDlWIsaQedhLDEmHCB9THqMCwUFYx30evIG9uUbmWq5AXmbWV8-W02QtK4JKvX3n1C8rxwjTPWtaEpbTJxtZt7s17Ee1abOUSndavQ-PY36hQdFaYKcLY7e-htdDcZPqTjp_tseDtOLVO6SRloDpxyAoMoWKO4MLNCYRtVMaViIGcDZmnBNHA71bE8s4oRYBKELpRkPXS9zY0lP1oITV6XwUJVmRXEqjklMpqUVCyiV__QpWt9_HSkqFScxQFxpPCWst6F4KHI176sjf_KCc43g-dx8HwzeL4bPFoud8HttIbZ3vC3cAQutkAJAPtnJZTUVLIf80eEUA</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Prakash, Jaya</creator><creator>Sanny, Dween</creator><creator>Kalva, Sandeep Kumar</creator><creator>Pramanik, Manojit</creator><creator>Yalavarthy, Phaneendra K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1034-7246</orcidid><orcidid>https://orcid.org/0000-0003-2865-5714</orcidid><orcidid>https://orcid.org/0000-0002-2375-154X</orcidid><orcidid>https://orcid.org/0000-0003-4810-352X</orcidid></search><sort><creationdate>20190801</creationdate><title>Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction</title><author>Prakash, Jaya ; Sanny, Dween ; Kalva, Sandeep Kumar ; Pramanik, Manojit ; Yalavarthy, Phaneendra K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-3e94e4241e7c38ca845adf80c4e45b2576d73c2f39e4cb90143c831e36e59f863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acoustic noise</topic><topic>Acoustics</topic><topic>Algorithms</topic><topic>Animals</topic><topic>Biological tissues</topic><topic>Brain - diagnostic imaging</topic><topic>compressive sensing</topic><topic>Computer Simulation</topic><topic>Data recovery</topic><topic>Detectors</topic><topic>fractional methods</topic><topic>Ill posed problems</topic><topic>Image contrast</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Initial pressure</topic><topic>Noise</topic><topic>Noise levels</topic><topic>Noise standards</topic><topic>Phantoms, Imaging</topic><topic>Photoacoustic effect</topic><topic>Photoacoustic Techniques - methods</topic><topic>Photoacoustic tomography</topic><topic>Pressure</topic><topic>Rats</topic><topic>Regularization</topic><topic>regularization theory</topic><topic>Signal to noise ratio</topic><topic>Stress concentration</topic><topic>Tomography - methods</topic><topic>Variation</topic><toplevel>online_resources</toplevel><creatorcontrib>Prakash, Jaya</creatorcontrib><creatorcontrib>Sanny, Dween</creatorcontrib><creatorcontrib>Kalva, Sandeep Kumar</creatorcontrib><creatorcontrib>Pramanik, Manojit</creatorcontrib><creatorcontrib>Yalavarthy, Phaneendra K.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Prakash, Jaya</au><au>Sanny, Dween</au><au>Kalva, Sandeep Kumar</au><au>Pramanik, Manojit</au><au>Yalavarthy, Phaneendra K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2019-08-01</date><risdate>2019</risdate><volume>38</volume><issue>8</issue><spage>1935</spage><epage>1947</epage><pages>1935-1947</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, ℓ 1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, ℓ 1 -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, ℓ 1 -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30582534</pmid><doi>10.1109/TMI.2018.2889314</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-1034-7246</orcidid><orcidid>https://orcid.org/0000-0003-2865-5714</orcidid><orcidid>https://orcid.org/0000-0002-2375-154X</orcidid><orcidid>https://orcid.org/0000-0003-4810-352X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0278-0062 |
| ispartof | IEEE transactions on medical imaging, 2019-08, Vol.38 (8), p.1935-1947 |
| issn | 0278-0062 1558-254X |
| language | eng |
| recordid | cdi_ieee_primary_8586926 |
| source | IEEE Electronic Library (IEL) |
| subjects | Acoustic noise Acoustics Algorithms Animals Biological tissues Brain - diagnostic imaging compressive sensing Computer Simulation Data recovery Detectors fractional methods Ill posed problems Image contrast Image processing Image Processing, Computer-Assisted - methods Image reconstruction Imaging Initial pressure Noise Noise levels Noise standards Phantoms, Imaging Photoacoustic effect Photoacoustic Techniques - methods Photoacoustic tomography Pressure Rats Regularization regularization theory Signal to noise ratio Stress concentration Tomography - methods Variation |
| title | Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T23%3A19%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fractional%20Regularization%20to%20Improve%20Photoacoustic%20Tomographic%20Image%20Reconstruction&rft.jtitle=IEEE%20transactions%20on%20medical%20imaging&rft.au=Prakash,%20Jaya&rft.date=2019-08-01&rft.volume=38&rft.issue=8&rft.spage=1935&rft.epage=1947&rft.pages=1935-1947&rft.issn=0278-0062&rft.eissn=1558-254X&rft.coden=ITMID4&rft_id=info:doi/10.1109/TMI.2018.2889314&rft_dat=%3Cproquest_RIE%3E2268432880%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2268432880&rft_id=info:pmid/30582534&rft_ieee_id=8586926&rfr_iscdi=true |