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    <title language="eng">Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate</title>
    <abstract language="eng">Hyperpolarized13C imaging allows real-timein vivomeasurements of metabolite levels. Quantification of metabolite conversion between [1-13C]pyruvate and downstream metabolites [1-13C]alanine, [1-13C]lactate, and [13C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its downstream metabolites, the purpose of this work is the determination of parameter values through a multisite, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multisite model to time-domain dynamic metabolite data. The results for different pyruvate doses were compared with those of different two-site models to evaluate the hypothesis that for identical data the uncertainty of a model and the signal-to-noise ratio determine the sensitivity in detecting small physiological differences in the target metabolism. In comparison to the two-site exchange models, the multisite model yielded metabolic conversion rates with smaller bias and smaller standard deviation, as demonstrated in simulations with different signal-to-noise ratio. Pyruvate dose effects observed previously were confirmed and quantified through metabolic conversion rate values. Parameter interdependency allowed an accurate quantification and can therefore be useful for monitoring metabolic activity in different tissues.</abstract>
    <parentTitle language="eng">Radiology Research and Practice</parentTitle>
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      <first_name>Pedro A.</first_name>
      <last_name>Gómez Damián</last_name>
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      <first_name>Jonathan I.</first_name>
      <last_name>Sperl</last_name>
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      <first_name>Martin A.</first_name>
      <last_name>Janich</last_name>
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      <first_name>Oleksandr</first_name>
      <last_name>Khegai</last_name>
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      <first_name>Florian</first_name>
      <last_name>Wiesinger</last_name>
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      <first_name>Steffen J.</first_name>
      <last_name>Glaser</last_name>
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    <author>
      <first_name>Axel</first_name>
      <last_name>Haase</last_name>
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      <first_name>Markus</first_name>
      <last_name>Schwaiger</last_name>
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    <author>
      <first_name>Rolf F.</first_name>
      <last_name>Schulte</last_name>
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    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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    <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>
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    <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>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
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    <author>
      <first_name>Lioba</first_name>
      <last_name>Grundl</last_name>
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    <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>
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    <edition/>
    <issue>118</issue>
    <volume>2016</volume>
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    <publisherName>MyJoVE Corporation</publisherName>
    <publisherPlace>Cambridge</publisherPlace>
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    <title language="eng">Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging</title>
    <parentTitle language="eng">Journal of Visualized Experiments</parentTitle>
    <identifier type="issn">1940-087X</identifier>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Eugen</first_name>
      <last_name>Kubala</last_name>
    </author>
    <author>
      <first_name>Kim A.</first_name>
      <last_name>Muñoz-Álvarez</last_name>
    </author>
    <author>
      <first_name>Geoffrey</first_name>
      <last_name>Topping</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hundshammer</last_name>
    </author>
    <author>
      <first_name>Benedikt</first_name>
      <last_name>Feuerecker</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Giorgio</first_name>
      <last_name>Pariani</last_name>
    </author>
    <author>
      <first_name>Franz</first_name>
      <last_name>Schilling</last_name>
    </author>
    <author>
      <first_name>Steffen J.</first_name>
      <last_name>Glaser</last_name>
    </author>
    <author>
      <first_name>Rolf F.</first_name>
      <last_name>Schulte</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Schwaiger</last_name>
    </author>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>1879</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue>18</issue>
    <volume>65</volume>
    <articleNumber>185010</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging</title>
    <abstract language="eng">This study aims to develop a silent, fast and 3D method for T1 and proton density (PD) mapping, while generating time series of T1-weighted (T1w) images with bias-field correction. Undersampled T1w images at different effective inversion times (TIs) were acquired using the inversion recovery prepared RUFIS sequence with an interleaved k-space trajectory. Unaliased images were reconstructed by constraining the signal evolution to a temporal subspace which was learned from the signal model. Parameter maps were obtained by fitting the data to the signal model, and bias-field correction was conducted on T1w images. Accuracy and repeatability of the method was accessed in repeated experiments with phantom and volunteers. For the phantom study, T1 values obtained by the proposed method were highly consistent with values from the gold standard method, R2 = 0.9976. Coefficients of variation (CVs) ranged from 0.09% to 0.83%. For the volunteer study, T1 values from gray and white matter regions were consistent with literature values, and peaks of gray and white matter can be clearly delineated on whole-brain T1 histograms. CVs ranged from 0.01% to 2.30%. The acoustic noise measured at the scanner isocenter was 2.6 dBA higher compared to the in-bore background. Rapid and with low acoustic noise, the proposed method is shown to produce accurate T1 and PD maps with high repeatability by reconstructing sparsely sampled T1w images at different TIs using temporal subspace. Our approach can greatly enhance patient comfort during examination and therefore increase the acceptance of the procedure.</abstract>
    <parentTitle language="eng">Physics in Medicine &amp; Biology</parentTitle>
    <identifier type="issn">1361-6560</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-18793</identifier>
    <identifier type="issn">0031-9155</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1088/1361-6560/aba5e8</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>Xin</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Ana Beatriz</first_name>
      <last_name>Solana</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Wiesinger</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>T1 mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>proton density</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>silent MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inversion recovery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>temporal subspace</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1879/Liu_2020_Phys._Med._Biol._65_185010.pdf</file>
  </doc>
  <doc>
    <id>1883</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>20</pageFirst>
    <pageLast>32</pageLast>
    <pageNumber/>
    <edition/>
    <issue>61</issue>
    <volume>2019</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting</title>
    <parentTitle language="eng">Magnetic Resonance Imaging</parentTitle>
    <identifier type="issn">0730-725X</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.mri.2019.04.014</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4619</enrichment>
    <author>
      <first_name>Arnold Julian Vinoj</first_name>
      <last_name>Benjamin</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <author>
      <first_name>Zaid</first_name>
      <last_name>Bin Mahbub</last_name>
    </author>
    <author>
      <first_name>Tim</first_name>
      <last_name>Sprenger</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <author>
      <first_name>Ian</first_name>
      <last_name>Marshall</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cartesian MRF</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-shot EPI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quantitative maps</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>1885</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume>9</volume>
    <articleNumber>8468</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging</title>
    <abstract language="eng">Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue’s structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 – to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 – to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design using smooth flip angle excitation patterns that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-18854</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-019-44832-w</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
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    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4648</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</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="open_access" number="">open_access</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1885/s41598-019-44832-w.pdf</file>
  </doc>
  <doc>
    <id>1887</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>7825</pageFirst>
    <pageLast>7829</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Geometry of Deep Learning for Magnetic Resonance Fingerprinting</title>
    <parentTitle language="eng">ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</parentTitle>
    <identifier type="isbn">978-1-5386-4658-8</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICASSP.2019.8683549</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton (UK), 12.-17.05.2019</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4647</enrichment>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <author>
      <first_name>Dongdong</first_name>
      <last_name>Chen</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>magnetic resonance fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inverse problem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dictionary</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>manifold compressed sensing</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>1876</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue>69</issue>
    <volume>2021</volume>
    <articleNumber>101945</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks</title>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="issn">1361-8415</identifier>
    <identifier type="issn">1361-8423</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.media.2020.101945</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://ars.els-cdn.com/content/image/1-s2.0-S1361841520303091-mmc1.pdf</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4649</enrichment>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</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>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>magnetic resonance fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>compressed sensing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>convex model-based reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>residual network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>encoder-decoder network</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>2817</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>639</pageFirst>
    <pageLast>654</pageLast>
    <pageNumber/>
    <edition/>
    <issue>121</issue>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>PMLR</publisherName>
    <publisherPlace>[s. l.]</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-09-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting</title>
    <parentTitle language="eng">Proceedings of Machine Learning Research</parentTitle>
    <identifier type="issn">2640-3498</identifier>
    <identifier type="url">https://proceedings.mlr.press/v121/pirk20a.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 2020), Montreal (Canada), 06.-08.07.2020</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4468</enrichment>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Ilona</first_name>
      <last_name>Lipp</last_name>
    </author>
    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
    </author>
    <author>
      <first_name>Anjany</first_name>
      <last_name>Sekuboyina</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Dannenberg</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
    </author>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Merola</last_name>
    </author>
    <author>
      <first_name>Joseph R.</first_name>
      <last_name>Whittaker</last_name>
    </author>
    <author>
      <first_name>Valentina</first_name>
      <last_name>Tomassini</last_name>
    </author>
    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
    </author>
    <author>
      <first_name>Derek K.</first_name>
      <last_name>Jones</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>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional Neural Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image Reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusion Tensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiple Sclerosis</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>2821</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2155</pageFirst>
    <pageLast>2172</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>80</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Hoboken</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-09-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization</title>
    <abstract language="eng">Purpose&#13;
&#13;
The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill-posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free-water component.&#13;
&#13;
Theory and Methods&#13;
&#13;
Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization.&#13;
&#13;
Results&#13;
&#13;
Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free-water contamination and to estimate tissue parameters.&#13;
&#13;
Conclusion&#13;
&#13;
Formulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination.</abstract>
    <parentTitle language="eng">Magnetic Resonance in Medicine</parentTitle>
    <identifier type="issn">1522-2594</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-28210</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Jonathan I.</first_name>
      <last_name>Sperl</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Czisch</last_name>
    </author>
    <author>
      <first_name>Philipp G.</first_name>
      <last_name>Sämann</last_name>
    </author>
    <author>
      <first_name>Derek K.</first_name>
      <last_name>Jones</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>blind source separation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>brain microstructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diffusion MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>free-water elimination</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MR relaxometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>non-negative matrix factorization</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2821/Magnetic_Resonance_2018_Molina-Romero.pdf</file>
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    <publishedYear>2018</publishedYear>
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    <pageFirst>98</pageFirst>
    <pageLast>106</pageLast>
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    <completedDate>2022-09-06</completedDate>
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    <title language="eng">Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases</title>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention – MICCAI 2018</parentTitle>
    <identifier type="isbn">978-3-030-00931-1</identifier>
    <identifier type="isbn">978-3-030-00930-4</identifier>
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    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
    </author>
    <author>
      <first_name>Benedikt</first_name>
      <last_name>Wiestler</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Glioblastoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain tumor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DTI</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Free-water elimination</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data harmonization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fractional anisotropy</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
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  <doc>
    <id>3116</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
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    <title language="eng">Deep Fully Convolutional Network for MR Fingerprinting</title>
    <abstract language="eng">This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction.  We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions.</abstract>
    <parentTitle language="eng">Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track</parentTitle>
    <identifier type="url">https://openreview.net/forum?id=SJxUdvJTtN</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31163</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Dongdong</first_name>
      <last_name>Chen</last_name>
    </author>
    <editor>
      <first_name>M. Jorge</first_name>
      <last_name>Cardoso</last_name>
    </editor>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <editor>
      <first_name>Aasa</first_name>
      <last_name>Feragen</last_name>
    </editor>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <editor>
      <first_name>Ben</first_name>
      <last_name>Glocker</last_name>
    </editor>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <editor>
      <first_name>Ender</first_name>
      <last_name>Konukoglu</last_name>
    </editor>
    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <editor>
      <first_name>Ipek</first_name>
      <last_name>Oguz</last_name>
    </editor>
    <editor>
      <first_name>Gozde</first_name>
      <last_name>Unal</last_name>
    </editor>
    <editor>
      <first_name>Tom</first_name>
      <last_name>Vercauteren</last_name>
    </editor>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3116/28_deep_fully_convolutional_netwo.pdf</file>
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    <publishedYear>2019</publishedYear>
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    <title language="eng">Spatio-temporal regularization for deep MR Fingerprinting</title>
    <abstract language="eng">We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which ap- pear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series.</abstract>
    <parentTitle language="eng">Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track</parentTitle>
    <identifier type="url">https://openreview.net/forum?id=ryx64UL6YE</identifier>
    <identifier type="url">https://2019.midl.io/program/extended-abstracts.html</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31174</identifier>
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    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
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    <editor>
      <first_name>M. Jorge</first_name>
      <last_name>Cardoso</last_name>
    </editor>
    <author>
      <first_name>Dongdong</first_name>
      <last_name>Chen</last_name>
    </author>
    <editor>
      <first_name>Aasa</first_name>
      <last_name>Feragen</last_name>
    </editor>
    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <editor>
      <first_name>Ben</first_name>
      <last_name>Glocker</last_name>
    </editor>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <editor>
      <first_name>Ender</first_name>
      <last_name>Konukoglu</last_name>
    </editor>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <editor>
      <first_name>Ipek</first_name>
      <last_name>Oguz</last_name>
    </editor>
    <editor>
      <first_name>Gozde</first_name>
      <last_name>Unal</last_name>
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    <editor>
      <first_name>Tom</first_name>
      <last_name>Vercauteren</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>magnetic resonance fingerprinting</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>regularisation</value>
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    <title language="eng">A Fully Convolutional Network for MR Fingerprinting</title>
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      <first_name>Dongdong</first_name>
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    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
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    <author>
      <first_name>Pedro A.</first_name>
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      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
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    <publishedYear>2018</publishedYear>
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    <pageNumber>5</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <title language="eng">Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF</title>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.1809.02506</enrichment>
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    <author>
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    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
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    <author>
      <first_name>Mohammad</first_name>
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    <author>
      <first_name>Tim</first_name>
      <last_name>Sprenger</last_name>
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      <first_name>Marion Irene</first_name>
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      <first_name>Mike E.</first_name>
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      <first_name>Ian</first_name>
      <last_name>Marshall</last_name>
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    <title language="eng">Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.1906.08195</enrichment>
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    <author>
      <first_name>Arnold Julian Vinoj</first_name>
      <last_name>Benjamin</last_name>
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    <author>
      <first_name>Zaid</first_name>
      <last_name>Bin Mahbub</last_name>
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      <first_name>Tim</first_name>
      <last_name>Sprenger</last_name>
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      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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      <first_name>Mike E.</first_name>
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    <title language="eng">Deep MR Fingerprinting with total-variation and low-rank subspace priors</title>
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      <first_name>Mohammad</first_name>
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    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
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    <author>
      <first_name>Marion Irene</first_name>
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      <last_name>Buonincontri</last_name>
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      <last_name>Gómez</last_name>
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    <title language="eng">Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation</title>
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