<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-04178641v1</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-23T21:44:34+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction</title>
            <author role="aut">
              <persName>
                <forename type="first">Arnaud</forename>
                <surname>Quillent</surname>
              </persName>
              <email type="md5">c4cdb3a36a404b21e81dfe459920fe64</email>
              <email type="domain">inria.fr</email>
              <idno type="idhal" notation="string">arnaud-quillent</idno>
              <idno type="idhal" notation="numeric">1275022</idno>
              <idno type="halauthorid" notation="string">2253420-1275022</idno>
              <idno type="ORCID">https://orcid.org/0000-0001-6358-1293</idno>
              <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=Fft8auYAAAAJ</idno>
              <idno type="IDREF">https://www.idref.fr/285172611</idno>
              <affiliation ref="#struct-484335"/>
              <affiliation ref="#struct-7191"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Vincent</forename>
                <surname>Bismuth</surname>
              </persName>
              <idno type="halauthorid">362946-0</idno>
              <affiliation ref="#struct-7191"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Isabelle</forename>
                <surname>Bloch</surname>
              </persName>
              <email type="md5">e709fe9afb0f8c7fedaa3290a0c3725b</email>
              <email type="domain">enst.fr</email>
              <idno type="idhal" notation="string">isabelle-bloch</idno>
              <idno type="idhal" notation="numeric">175825</idno>
              <idno type="halauthorid" notation="string">23925-175825</idno>
              <idno type="ORCID">https://orcid.org/0000-0002-6984-1532</idno>
              <idno type="IDREF">https://www.idref.fr/031277861</idno>
              <idno type="ISNI">http://isni.org/isni/0000000122800920</idno>
              <idno type="VIAF">https://viaf.org/viaf/54203991</idno>
              <idno type="RESEARCHERID">http://www.researcherid.com/rid/DUQ-9128-2022</idno>
              <idno type="RESEARCHERID">http://www.researcherid.com/rid/http://www.researcherid.com/rid/DUQ-9128-2022</idno>
              <affiliation ref="#struct-484335"/>
              <affiliation ref="#struct-541719"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Christophe</forename>
                <surname>Kervazo</surname>
              </persName>
              <email type="md5">7547781ef69e09db98ffc24b2c43ee3c</email>
              <email type="domain">telecom-paris.fr</email>
              <idno type="idhal" notation="string">christophe-kervazo</idno>
              <idno type="idhal" notation="numeric">752855</idno>
              <idno type="halauthorid" notation="string">54380-752855</idno>
              <idno type="ORCID">https://orcid.org/0009-0008-6990-5322</idno>
              <affiliation ref="#struct-484335"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Saïd</forename>
                <surname>Ladjal</surname>
              </persName>
              <email type="md5">a197cd7b4f80eef3ff676cde9334ab21</email>
              <email type="domain">enst.fr</email>
              <idno type="idhal" notation="numeric">1144321</idno>
              <idno type="halauthorid" notation="string">195393-1144321</idno>
              <idno type="ORCID">https://orcid.org/0000-0002-7205-4898</idno>
              <affiliation ref="#struct-484335"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Arnaud</forename>
                <surname>Quillent</surname>
              </persName>
              <email type="md5">22fbdeb21db45aaa5044534b75cd39ec</email>
              <email type="domain">gmail.com</email>
            </editor>
            <funder>French Ministry for Higher Education and Research, CIFRE grant No. 2021/1209</funder>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2023-08-08 12:18:14</date>
              <date type="whenModified">2026-01-19 16:46:21</date>
              <date type="whenReleased">2023-08-08 12:28:32</date>
              <date type="whenProduced">2023-07-10</date>
              <date type="whenEndEmbargoed">2023-08-08</date>
              <ref type="file" target="https://hal.science/hal-04178641v1/document">
                <date notBefore="2023-08-08"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://hal.science/hal-04178641v1/file/219_a_deep_learning_method_trained.pdf" id="file-4178641-3640943">
                <date notBefore="2023-08-08"/>
              </ref>
            </edition>
            <edition n="v2">
              <date type="whenSubmitted">2024-06-14 10:39:55</date>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="1443328">
                <persName>
                  <forename>Arnaud</forename>
                  <surname>Quillent</surname>
                </persName>
                <email type="md5">22fbdeb21db45aaa5044534b75cd39ec</email>
                <email type="domain">gmail.com</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-04178641</idno>
            <idno type="halUri">https://hal.science/hal-04178641</idno>
            <idno type="halBibtex">quillent:hal-04178641</idno>
            <idno type="halRefHtml">&lt;i&gt;6th International Conference on Medical Imaging with Deep Learning (MIDL 2023)&lt;/i&gt;, MIDL Foundation, Jul 2023, Nashville, TN, United States. pp.1813-1825</idno>
            <idno type="halRef">6th International Conference on Medical Imaging with Deep Learning (MIDL 2023), MIDL Foundation, Jul 2023, Nashville, TN, United States. pp.1813-1825</idno>
            <availability status="restricted">
              <licence target="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 - Attribution<ref corresp="#file-4178641-3640943"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="ENST">Ecole Nationale Supérieure des Télécommunications</idno>
            <idno type="stamp" n="PARISTECH">ParisTech</idno>
            <idno type="stamp" n="IDS" corresp="TELECOM-PARISTECH">Département Image, Données, Signal</idno>
            <idno type="stamp" n="IMAGES" corresp="TELECOM-PARISTECH">Equipe Image, Modélisation, Analyse, GEométrie, Synthèse</idno>
          </seriesStmt>
          <notesStmt>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Arnaud</forename>
                    <surname>Quillent</surname>
                  </persName>
                  <email type="md5">c4cdb3a36a404b21e81dfe459920fe64</email>
                  <email type="domain">inria.fr</email>
                  <idno type="idhal" notation="string">arnaud-quillent</idno>
                  <idno type="idhal" notation="numeric">1275022</idno>
                  <idno type="halauthorid" notation="string">2253420-1275022</idno>
                  <idno type="ORCID">https://orcid.org/0000-0001-6358-1293</idno>
                  <idno type="GOOGLE SCHOLAR">https://scholar.google.fr/citations?user=Fft8auYAAAAJ</idno>
                  <idno type="IDREF">https://www.idref.fr/285172611</idno>
                  <affiliation ref="#struct-484335"/>
                  <affiliation ref="#struct-7191"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Vincent</forename>
                    <surname>Bismuth</surname>
                  </persName>
                  <idno type="halauthorid">362946-0</idno>
                  <affiliation ref="#struct-7191"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Isabelle</forename>
                    <surname>Bloch</surname>
                  </persName>
                  <email type="md5">e709fe9afb0f8c7fedaa3290a0c3725b</email>
                  <email type="domain">enst.fr</email>
                  <idno type="idhal" notation="string">isabelle-bloch</idno>
                  <idno type="idhal" notation="numeric">175825</idno>
                  <idno type="halauthorid" notation="string">23925-175825</idno>
                  <idno type="ORCID">https://orcid.org/0000-0002-6984-1532</idno>
                  <idno type="IDREF">https://www.idref.fr/031277861</idno>
                  <idno type="ISNI">http://isni.org/isni/0000000122800920</idno>
                  <idno type="VIAF">https://viaf.org/viaf/54203991</idno>
                  <idno type="RESEARCHERID">http://www.researcherid.com/rid/DUQ-9128-2022</idno>
                  <idno type="RESEARCHERID">http://www.researcherid.com/rid/http://www.researcherid.com/rid/DUQ-9128-2022</idno>
                  <affiliation ref="#struct-484335"/>
                  <affiliation ref="#struct-541719"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Christophe</forename>
                    <surname>Kervazo</surname>
                  </persName>
                  <email type="md5">7547781ef69e09db98ffc24b2c43ee3c</email>
                  <email type="domain">telecom-paris.fr</email>
                  <idno type="idhal" notation="string">christophe-kervazo</idno>
                  <idno type="idhal" notation="numeric">752855</idno>
                  <idno type="halauthorid" notation="string">54380-752855</idno>
                  <idno type="ORCID">https://orcid.org/0009-0008-6990-5322</idno>
                  <affiliation ref="#struct-484335"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Saïd</forename>
                    <surname>Ladjal</surname>
                  </persName>
                  <email type="md5">a197cd7b4f80eef3ff676cde9334ab21</email>
                  <email type="domain">enst.fr</email>
                  <idno type="idhal" notation="numeric">1144321</idno>
                  <idno type="halauthorid" notation="string">195393-1144321</idno>
                  <idno type="ORCID">https://orcid.org/0000-0002-7205-4898</idno>
                  <affiliation ref="#struct-484335"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">Proceedings of Machine Learning Research</title>
                <meeting>
                  <title>6th International Conference on Medical Imaging with Deep Learning (MIDL 2023)</title>
                  <date type="start">2023-07-10</date>
                  <date type="end">2023-07-12</date>
                  <settlement>Nashville, TN</settlement>
                  <country key="US">United States</country>
                </meeting>
                <respStmt>
                  <resp>conferenceOrganizer</resp>
                  <name>MIDL Foundation</name>
                </respStmt>
                <imprint>
                  <publisher>PMLR</publisher>
                  <biblScope unit="volume">227</biblScope>
                  <biblScope unit="pp">1813-1825</biblScope>
                </imprint>
              </monogr>
              <ref type="publisher">https://proceedings.mlr.press/v227/quillent24a.html</ref>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">DBT reconstruction</term>
                <term xml:lang="en">inverse problem</term>
                <term xml:lang="en">deep learning</term>
                <term xml:lang="en">limited angle</term>
                <term xml:lang="en">sparse view</term>
                <term xml:lang="en">synthetic phantoms</term>
                <term xml:lang="en">2.5D</term>
              </keywords>
              <classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode>
              <classCode scheme="halDomain" n="info.info-im">Computer Science [cs]/Medical Imaging</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Digital Breast Tomosynthesis (DBT) is an X-ray imaging modality enabling the reconstruction of 3D volumes of breasts. DBT is mainly used for cancer screening, and is intended to replace conventional mammography in the coming years. However, DBT reconstructions are impeded by several types of artefacts induced by the geometry of the device itself, degrading the image quality and limiting its resolution along the thickness of the compressed breast. In this study, we propose a deep-learning-based pipeline to address the DBT reconstruction problem, focusing on the removal of sparse-view and limited-angle artefacts. Specifically, this procedure is composed of two steps: a classic reconstruction algorithm is first applied on normalised projections, then a deep neural network is tasked with erasing the artefacts present in the obtained volumes. A major difficulty to solve our problem is the lack of real conditions artefact-free data. To overcome this complication, we resort to a new dataset comprised of synthetic breast texture phantoms. We then show that our training method and database strategy are promising to tackle the problem as they improve the informational value of planes orthogonal to the detector, which are not currently used by radiologists due to their poor quality. Eventually, we assess the impact of removing the bias components from the network and using stacks of slices as inputs, with regard to the generalisation ability of our approach on both synthetic and clinical data.</p>
            </abstract>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="laboratory" xml:id="struct-484335" status="VALID">
          <idno type="IdRef">162384270</idno>
          <idno type="ISNI">0000 0000 9194 9502</idno>
          <idno type="RNSR">200319327Z</idno>
          <idno type="ROR">https://ror.org/057er4c39</idno>
          <orgName>Laboratoire Traitement et Communication de l'Information</orgName>
          <orgName type="acronym">LTCI</orgName>
          <date type="start">2017-01-01</date>
          <desc>
            <address>
              <addrLine>Télécom Paris 19 Place Marguerite Perey 91120 PALAISEAU</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.telecom-paris.fr/fr/recherche/laboratoires/laboratoire-traitement-et-communication-de-linformation-ltci</ref>
          </desc>
          <listRelation>
            <relation active="#struct-302102" type="direct"/>
            <relation active="#struct-1048346" type="direct"/>
            <relation active="#struct-563936" type="indirect"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-7191" status="VALID">
          <orgName>General Electric Medical Systems [Buc]</orgName>
          <orgName type="acronym">GE Healthcare</orgName>
          <desc>
            <address>
              <addrLine>283 rue de la Minière, 78 530 Buc</addrLine>
              <country key="FR"/>
            </address>
          </desc>
        </org>
        <org type="researchteam" xml:id="struct-541719" status="VALID">
          <orgName>Learning, Fuzzy and Intelligent systems</orgName>
          <orgName type="acronym">LFI</orgName>
          <date type="start">2018-01-01</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-541703" type="direct"/>
            <relation active="#struct-413221" type="indirect"/>
            <relation name="UMR7606" active="#struct-441569" type="indirect"/>
          </listRelation>
        </org>
        <org type="regroupinstitution" xml:id="struct-302102" status="VALID">
          <idno type="IdRef">192427156</idno>
          <idno type="ISNI">000000012202567X</idno>
          <idno type="ROR">https://ror.org/025vp2923</idno>
          <idno type="Wikidata">Q27962533</idno>
          <orgName>Institut Mines-Télécom [Paris]</orgName>
          <orgName type="acronym">IMT</orgName>
          <date type="start">2012-03-01</date>
          <desc>
            <address>
              <addrLine>19 Place Marguerite Perey, 91120 Palaiseau</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.imt.fr/</ref>
          </desc>
        </org>
        <org type="institution" xml:id="struct-1048346" status="VALID">
          <idno type="IdRef">026375273</idno>
          <idno type="ISNI">0000 0001 2108 2779</idno>
          <idno type="ROR">https://ror.org/01naq7912</idno>
          <orgName>Télécom Paris</orgName>
          <date type="start">2019-06-12</date>
          <desc>
            <address>
              <addrLine>19 Place Marguerite Perey 91120 Palaiseau</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.telecom-paris.fr</ref>
          </desc>
          <listRelation>
            <relation active="#struct-302102" type="direct"/>
            <relation active="#struct-563936" type="direct"/>
          </listRelation>
        </org>
        <org type="regroupinstitution" xml:id="struct-563936" status="VALID">
          <idno type="IdRef">238327159</idno>
          <idno type="ISNI">0000000502717600</idno>
          <idno type="ROR">https://ror.org/042tfbd02</idno>
          <idno type="Wikidata">Q48759778</idno>
          <orgName>Institut Polytechnique de Paris</orgName>
          <orgName type="acronym">IP Paris</orgName>
          <date type="start">2019-06-02</date>
          <desc>
            <address>
              <addrLine>Route de Saclay, 91120 Palaiseau Cedex, France</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.ip-paris.fr</ref>
          </desc>
        </org>
        <org type="laboratory" xml:id="struct-541703" status="VALID">
          <idno type="IdRef">13558292X</idno>
          <idno type="RNSR">199712651U</idno>
          <idno type="ROR">https://ror.org/05krcen59</idno>
          <orgName>LIP6</orgName>
          <date type="start">2018-01-01</date>
          <desc>
            <address>
              <addrLine>4 Place JUSSIEU 75252 PARIS CEDEX 05</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.lip6.fr/</ref>
          </desc>
          <listRelation>
            <relation active="#struct-413221" type="direct"/>
            <relation name="UMR7606" active="#struct-441569" type="direct"/>
          </listRelation>
        </org>
        <org type="regroupinstitution" xml:id="struct-413221" status="VALID">
          <idno type="IdRef">221333754</idno>
          <idno type="ROR">https://ror.org/02en5vm52</idno>
          <orgName>Sorbonne Université</orgName>
          <orgName type="acronym">SU</orgName>
          <date type="start">2018-01-01</date>
          <desc>
            <address>
              <addrLine>21 rue de l’École de médecine - 75006 Paris</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.sorbonne-universite.fr/</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-441569" status="VALID">
          <idno type="IdRef">02636817X</idno>
          <idno type="ISNI">0000000122597504</idno>
          <idno type="ROR">https://ror.org/02feahw73</idno>
          <orgName>Centre National de la Recherche Scientifique</orgName>
          <orgName type="acronym">CNRS</orgName>
          <date type="start">1939-10-19</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.cnrs.fr/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>