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  <doc>
    <id>7299</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-04-25</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent</title>
    <abstract language="eng">Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. This paper describes an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation and the Grüneisen parameter from in silico 3D phantom images for different radiance approximations. The scattering coefficient was assumed to be homogeneous and known a priori.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-72995</identifier>
    <identifier type="doi">10.1117/1.JBO.24.6.066001</identifier>
    <enrichment key="SourceTitle">Appeared in: Journal of Biomedical Optics, 24, p. 066001, 2019</enrichment>
    <author>Jens Buchmann</author>
    <submitter>Steffen Prohaska</submitter>
    <author>Bernhard Kaplan</author>
    <author>Samuel Powell</author>
    <author>Steffen Prohaska</author>
    <author>Jan Laufer</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-16</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quantitative photoacoustic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>blood oxygen saturation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inverse problem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model-based inversion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monte Carlo</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>spectral unmixing</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="pacs" number="80.00.00">INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY</collection>
    <collection role="msc" number="65-XX">NUMERICAL ANALYSIS</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="prohaska">Prohaska, Steffen</collection>
    <collection role="projects" number="dfg-photom">dfg-photom</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7299/zibreport-19-16_v1.pdf</file>
  </doc>
  <doc>
    <id>7541</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-12-10</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantitative PA tomography of high resolution 3-D images: experimental validation in tissue phantoms</title>
    <abstract language="eng">Quantitative photoacoustic tomography aims recover the spatial distribution of absolute chromophore concentrations and their ratios from deep tissue, high-resolution images. In this study, a model-based inversion scheme based on a Monte-Carlo light transport model is experimentally validated on 3-D multispectral images of a tissue phantom acquired using an all-optical scanner with a planar detection geometry. A calibrated absorber allowed scaling of the measured data during the inversion, while an acoustic correction method was employed to compensate the effects of limited view detection. Chromophore- and fluence-dependent step sizes and Adam optimization were implemented to achieve rapid convergence. High resolution 3-D maps of absolute concentrations and their ratios were recovered with high accuracy. Potential applications of this method include quantitative functional and molecular photoacoustic tomography of deep tissue in preclinical and clinical studies.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-75416</identifier>
    <identifier type="doi">10.1016/j.pacs.2019.100157</identifier>
    <enrichment key="SourceTitle">Photoacoustics 17:100157, 2020</enrichment>
    <author>Jens Buchmann</author>
    <submitter>Steffen Prohaska</submitter>
    <author>Bernhard Kaplan</author>
    <author>Samuel Powell</author>
    <author>Steffen Prohaska</author>
    <author>Jan Laufer</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-60</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quantitative photoacoustic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>blood oxygen saturation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inverse problem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model-based inversion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monte Carlo</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>spectral unmixing</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="pacs" number="80.00.00">INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY</collection>
    <collection role="msc" number="65-XX">NUMERICAL ANALYSIS</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="prohaska">Prohaska, Steffen</collection>
    <collection role="projects" number="dfg-photom">dfg-photom</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7541/zibreport-19-60_v1.pdf</file>
  </doc>
</export-example>
