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  <doc>
    <id>5333</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1555</pageFirst>
    <pageLast>1565</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>42</volume>
    <type>article</type>
    <publisherName>American Association of Physicists in Medicine</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">X-ray computed tomography using curvelet sparse regularization</title>
    <abstract language="eng">PURPOSE&#13;
Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.&#13;
METHODS&#13;
In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.&#13;
RESULTS&#13;
Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.&#13;
CONCLUSIONS&#13;
The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.</abstract>
    <parentTitle language="eng">Medical physics</parentTitle>
    <identifier type="doi">10.1118/1.4914368</identifier>
    <identifier type="issn">0094-2405</identifier>
    <identifier type="issn">2473-4209</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Matthias Wieczorek</author>
    <author>Jürgen Frikel</author>
    <author>Jakob Vogel</author>
    <author>Elena Eggl</author>
    <author>Felix Kopp</author>
    <author>Peter B. Noël</author>
    <author>Franz Pfeiffer</author>
    <author>Laurent Demaret</author>
    <author>Tobias Lasser</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
  <doc>
    <id>5334</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>489</pageFirst>
    <pageLast>492</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Curvelet sparse regularization for differential phase-contrast X-ray imaging</title>
    <abstract language="eng">Differential phase contrast imaging (DPCI) enables the visualization of soft tissue contrast using X-rays. In this work we introduce a reconstruction framework based on curvelet expansion and sparse regularization for DPCI. We will show that curvelets provide a suitable data representation for DPCI reconstruction that allows preservation of edges as well as an exact analytic representation of the system matrix. As a first evaluation, we show results using simulated phantom data</abstract>
    <parentTitle language="eng">Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine : Proceedings ; June 16-21, 2013, Granlibakken Resort, Lake Tahoe, California</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/255913330_Curvelet_sparse_regularization_for_differential_phase-contrast_X-ray_imaging</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Matthias Wieczorek</author>
    <author>Jürgen Frikel</author>
    <author>Jakob Vogel</author>
    <author>Franz Pfeiffer</author>
    <author>Laurent Demaret</author>
    <author>Tobias Lasser</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
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