<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1386</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>77</issue>
    <volume>2022</volume>
    <articleNumber>102387</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning residual motion correction for fast and robust 3D multiparametric MRI</title>
    <abstract language="eng">Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-13861</identifier>
    <identifier type="issn">1361-8423</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.media.2022.102387</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Jan W.</first_name>
      <last_name>Kurzawski</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Hongwei</first_name>
      <last_name>Li</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D Motion correction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Residual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiscale CNN</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1386/1-s2.0-S1361841522000391-main.pdf</file>
  </doc>
  <doc>
    <id>1388</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1831</pageFirst>
    <pageLast>1851</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>63</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accelerated 3D whole-brain T1, T2, and proton density mapping</title>
    <abstract language="eng">Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints.</abstract>
    <parentTitle language="eng">Neuroradiology</parentTitle>
    <subTitle language="eng">feasibility for clinical glioma MR imaging</subTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-13885</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00234-021-02703-0</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Laura</first_name>
      <last_name>Nunez-Gonzalez</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Kofler</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Lioba</first_name>
      <last_name>Grundl</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
    </author>
    <author>
      <first_name>Rolf F.</first_name>
      <last_name>Schulte</last_name>
    </author>
    <author>
      <first_name>Marion</first_name>
      <last_name>Smits</last_name>
    </author>
    <author>
      <first_name>Benedikt</first_name>
      <last_name>Wiestler</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Juan A.</first_name>
      <last_name>Hernandez-Tamames</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image-based biomarkers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Glioma imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1388/Pirkl2021_Article_Accelerated3D.pdf</file>
  </doc>
  <doc>
    <id>2816</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>OpenReview</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-09-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Residual learning for 3D motion corrected quantitative MRI</title>
    <parentTitle language="eng">Medical Imaging with Deep Learning MIDL 2021</parentTitle>
    <subTitle language="eng">Robust clinical T1, T2 and proton density mapping</subTitle>
    <identifier type="url">https://openreview.net/forum?id=hxgQM71AuRA</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/CarolinMA/MRP_MoCo</enrichment>
    <enrichment key="THI_conferenceName">Medical Imaging with Deep Learning: MIDL 2021, Lübeck (Germany), 07.-09.07.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Jan W.</first_name>
      <last_name>Kurzawski</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Hongwei</first_name>
      <last_name>Li</last_name>
    </author>
    <author>
      <first_name>Anjany</first_name>
      <last_name>Sekuboyina</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Luca</first_name>
      <last_name>Peretti</last_name>
    </author>
    <author>
      <first_name>Graziella</first_name>
      <last_name>Donatelli</last_name>
    </author>
    <author>
      <first_name>Rosa</first_name>
      <last_name>Pasquariello</last_name>
    </author>
    <author>
      <first_name>Mauro</first_name>
      <last_name>Costagli</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
    </author>
    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D multiparametric MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>motion correction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>residual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multiscale CNN</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>5304</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>128</pageFirst>
    <pageLast>137</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-11-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations</title>
    <parentTitle language="eng">Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II</parentTitle>
    <identifier type="isbn">978-3-031-73290-4</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T04:13:24Z","timestamp":1729656804800,"version":"3.28.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732928","type":"print"},{"value":"9783031732904","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73290-4_13","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:02:21Z","timestamp":1729576941000},"page":"128-137","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StoDIP: Efficient 3D MRF Image Reconstruction with\u00a0Deep Image Priors and\u00a0Stochastic Iterations"],"prefix":"10.1007","author":[{"given":"Perla","family":"Mayo","sequence":"first","affiliation":[]},{"given":"Matteo","family":"Cencini","sequence":"additional","affiliation":[]},{"given":"Carolin M.","family":"Pirkl","sequence":"additional","affiliation":[]},{"given":"Marion I.","family":"Menzel","sequence":"additional","affiliation":[]},{"given":"Michela","family":"Tosetti","sequence":"additional","affiliation":[]},{"given":"Bjoern H.","family":"Menze","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Golbabaee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"issue":"1","key":"13_CR1","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/mrm.26639","volume":"79","author":"J Assl\u00e4nder","year":"2018","unstructured":"Assl\u00e4nder, J., et al.: Low rank alternating direction method of multipliers reconstruction for MR fingerprinting. Magn. Reson. Med. 79(1), 83\u201396 (2018)","journal-title":"Magn. Reson. Med."},{"key":"13_CR2","doi-asserted-by":"publisher","unstructured":"Cardoso, M.J., et\u00a0al.: MONAI: an open-source framework for deep learning in healthcare (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.02701","DOI":"10.48550\/arXiv.2211.02701"},{"key":"13_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-030-59713-9_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D Chen","year":"2020","unstructured":"Chen, D., Davies, M.E., Golbabaee, M.: Compressive MR fingerprinting reconstruction with neural proximal gradient iterations. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 13\u201322. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_2"},{"key":"13_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116329","volume":"206","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Fang, Z., Hung, S.C., Chang, W.T., Shen, D., Lin, W.: High-resolution 3D MR fingerprinting using parallel imaging and deep learning. Neuroimage 206, 116329 (2020)","journal-title":"Neuroimage"},{"issue":"3","key":"13_CR5","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/mrm.27198","volume":"80","author":"O Cohen","year":"2018","unstructured":"Cohen, O., Zhu, B., Rosen, M.S.: MR fingerprinting deep reconstruction network (drone). Magn. Reson. Med. 80(3), 885\u2013894 (2018)","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"13_CR6","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1137\/130947246","volume":"7","author":"M Davies","year":"2014","unstructured":"Davies, M., Puy, G., Vandergheynst, P., Wiaux, Y.: A compressed sensing framework for magnetic resonance fingerprinting. SIAM J. Imag. Sci. 7(4), 2623\u20132656 (2014)","journal-title":"SIAM J. Imag. Sci."},{"issue":"10","key":"13_CR7","doi-asserted-by":"publisher","first-page":"2364","DOI":"10.1109\/TMI.2019.2899328","volume":"38","author":"Z Fang","year":"2019","unstructured":"Fang, Z., et al.: Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting. IEEE Trans. Med. Imaging 38(10), 2364\u20132374 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-32248-9_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Fang","year":"2019","unstructured":"Fang, Z., Chen, Y., Nie, D., Lin, W., Shen, D.: RCA-U-Net: residual channel attention U-net for fast tissue quantification in magnetic resonance fingerprinting. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 101\u2013109. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_12"},{"key":"13_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101945","volume":"69","author":"M Golbabaee","year":"2021","unstructured":"Golbabaee, M., et al.: Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks. Med. Image Anal. 69, 101945 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"13_CR10","doi-asserted-by":"publisher","first-page":"8468","DOI":"10.1038\/s41598-019-44832-w","volume":"9","author":"PA G\u00f3mez","year":"2019","unstructured":"G\u00f3mez, P.A., Molina-Romero, M., Buonincontri, G., Menzel, M.I., Menze, B.H.: Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging. Sci. Rep. 9(1), 8468 (2019)","journal-title":"Sci. Rep."},{"issue":"7","key":"13_CR11","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TMI.2018.2888491","volume":"38","author":"K Gong","year":"2018","unstructured":"Gong, K., Catana, C., Qi, J., Li, Q.: Pet image reconstruction using deep image prior. IEEE Trans. Med. Imaging 38(7), 1655\u20131665 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.928546","volume":"9","author":"JI Hamilton","year":"2022","unstructured":"Hamilton, J.I.: A self-supervised deep learning reconstruction for shortening the breathhold and acquisition window in cardiac magnetic resonance fingerprinting. Front. Cardiovasc. Med. 9, 928546 (2022)","journal-title":"Front. Cardiovasc. Med."},{"issue":"4","key":"13_CR13","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1002\/jmrs.413","volume":"67","author":"JJ Hsieh","year":"2020","unstructured":"Hsieh, J.J., Svalbe, I.: Magnetic resonance fingerprinting: from evolution to clinical applications. J. Med. Radiat. Sci. 67(4), 333\u2013344 (2020)","journal-title":"J. Med. Radiat. Sci."},{"issue":"7440","key":"13_CR14","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1038\/nature11971","volume":"495","author":"D Ma","year":"2013","unstructured":"Ma, D., et al.: Magnetic resonance fingerprinting. Nature 495(7440), 187\u2013192 (2013)","journal-title":"Nature"},{"issue":"12","key":"13_CR15","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.1109\/TMI.2014.2337321","volume":"33","author":"D McGivney","year":"2014","unstructured":"McGivney, D., et al.: SVD compression for magnetic resonance fingerprinting in the time domain. IEEE Trans. Med. Imaging 33(12), 2311\u20132322 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR16","unstructured":"Muckley, M.J., Stern, R., Murrell, T., Knoll, F.: TorchKbNufft: a high-level, hardware-agnostic non-uniform fast Fourier transform. In: ISMRM Workshop on Data Sampling &amp; Image Reconstruction (2020). https:\/\/github.com\/mmuckley\/torchkbnufft"},{"issue":"3","key":"13_CR17","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1002\/jmri.26836","volume":"51","author":"ME Poorman","year":"2020","unstructured":"Poorman, M.E., et al.: Magnetic resonance fingerprinting part 1: potential uses, current challenges, and recommendations. J. Magn. Reson. Imaging 51(3), 675\u2013692 (2020)","journal-title":"J. Magn. Reson. Imaging"},{"key":"13_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Shih, Y.H., Wright, G., And\u00e9n, J., Blaschke, J., Barnett, A.H.: cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTS. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 688\u2013697. IEEE (2021)","DOI":"10.1109\/IPDPSW52791.2021.00105"},{"issue":"3","key":"13_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143\u2013155 (2002)","journal-title":"Hum. Brain Mapp."},{"key":"13_CR21","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446\u20139454 (2018)"},{"key":"13_CR22","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.neucom.2015.09.077","volume":"174","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Li, H., Zhang, Q., Yuan, J., Wang, X.: Magnetic resonance fingerprinting with compressed sensing and distance metric learning. Neurocomputing 174, 560\u2013570 (2016)","journal-title":"Neurocomputing"},{"issue":"12","key":"13_CR23","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TMI.2021.3084288","volume":"40","author":"J Yoo","year":"2021","unstructured":"Yoo, J., Jin, K.H., Gupta, H., Yerly, J., Stuber, M., Unser, M.: Time-dependent deep image prior for dynamic MRI. IEEE Trans. Med. Imaging 40(12), 3337\u20133348 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"13_CR24","doi-asserted-by":"publisher","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","volume":"44","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6360\u20136376 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"13_CR25","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1002\/mrm.26701","volume":"79","author":"B Zhao","year":"2018","unstructured":"Zhao, B., et al.: Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling. Magn. Reson. Med. 79(2), 933\u2013942 (2018)","journal-title":"Magn. Reson. Med."},{"issue":"8","key":"13_CR26","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1109\/TMI.2016.2531640","volume":"35","author":"B Zhao","year":"2016","unstructured":"Zhao, B., Setsompop, K., Ye, H., Cauley, S.F., Wald, L.L.: Maximum likelihood reconstruction for magnetic resonance fingerprinting. IEEE Trans. Med. Imaging 35(8), 1812\u20131823 (2016)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73290-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:04:56Z","timestamp":1729577096000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73290-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,23]]},"ISBN":["9783031732928","9783031732904"],"references-count":26,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-031-73290-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,23]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}</enrichment>
    <enrichment key="local_crossrefDocumentType">book-chapter</enrichment>
    <enrichment key="local_crossrefLicence">https://www.springernature.com/gp/researchers/text-and-data-mining</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PublisherName,PublisherPlace,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-031-73290-4_13</enrichment>
    <enrichment key="THI_DownloadUrl">https://static-content.springer.com/esm/chp%3A10.1007%2F978-3-031-73290-4_13/MediaObjects/639529_1_En_13_MOESM1_ESM.pdf</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">15th International Workshop on Machine Learning in Medical Imaging (MLMI 2024), Marrakesh (Morocco), 06.10.2024</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Perla</first_name>
      <last_name>Mayo</last_name>
    </author>
    <editor>
      <first_name>Xuanang</first_name>
      <last_name>XU</last_name>
    </editor>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <editor>
      <first_name>Zhiming</first_name>
      <last_name>Cui</last_name>
    </editor>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <editor>
      <first_name>Islem</first_name>
      <last_name>Rekik</last_name>
    </editor>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <editor>
      <first_name>Xi</first_name>
      <last_name>Ouyang</last_name>
    </editor>
    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
    </author>
    <editor>
      <first_name>Kaicong</first_name>
      <last_name>Sun</last_name>
    </editor>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>4470</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>618</pageFirst>
    <pageLast>632</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>PMLR</publisherName>
    <publisherPlace>[s. l.]</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-02-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping</title>
    <parentTitle language="eng">Proceedings of Machine Learning Research</parentTitle>
    <identifier type="url">https://proceedings.mlr.press/v143/pirkl21a.html</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">Medical Imaging with Deep Learning (MIDL 2021), online, 07.-09.07.2021</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Jan W.</first_name>
      <last_name>Kurzawski</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Hongwei</first_name>
      <last_name>Li</last_name>
    </author>
    <author>
      <first_name>Anjany</first_name>
      <last_name>Sekuboyina</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Luca</first_name>
      <last_name>Peretti</last_name>
    </author>
    <author>
      <first_name>Graziella</first_name>
      <last_name>Donatelli</last_name>
    </author>
    <author>
      <first_name>Rosa</first_name>
      <last_name>Pasquariello</last_name>
    </author>
    <author>
      <first_name>Mauro</first_name>
      <last_name>Costagli</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
    </author>
    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>5532</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders</title>
    <parentTitle language="eng">IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings</parentTitle>
    <identifier type="isbn">979-8-3503-1333-8</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE International Symposium on Biomedical Imaging (ISBI)</enrichment>
    <enrichment key="conference_place">Athens, Greece</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_conferenceName">2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens (Greece), 27.-30.05.2024</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ISBI56570.2024.10635677</enrichment>
    <author>
      <first_name>Perla</first_name>
      <last_name>Mayo</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Ketan</first_name>
      <last_name>Fatania</last_name>
    </author>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
</export-example>
