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    <issue>11</issue>
    <volume>63</volume>
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    <publisherPlace>Berlin</publisherPlace>
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    <completedDate>2022-02-22</completedDate>
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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>
    </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>
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    <author>
      <first_name>Benedikt</first_name>
      <last_name>Wiestler</last_name>
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    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
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    <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>
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    <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>
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    <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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    <completedDate>2022-09-05</completedDate>
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    <title language="eng">A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers</title>
    <parentTitle language="eng">Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)</parentTitle>
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    <author>
      <first_name>Ketan</first_name>
      <last_name>Fatania</last_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>
      <last_name>Menzel</last_name>
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    <author>
      <first_name>Peter</first_name>
      <last_name>Hall</last_name>
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    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantitative MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Compressed Sensing</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse Problems</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Iterative Image Reconstruction</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plug-and-Play</value>
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    <pageNumber>22</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <title language="eng">Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI</title>
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    <author>
      <first_name>Ketan</first_name>
      <last_name>Fatania</last_name>
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      <first_name>Kwai Y.</first_name>
      <last_name>Chau</last_name>
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      <first_name>Carolin</first_name>
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      <first_name>Marion Irene</first_name>
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      <first_name>Mohammad</first_name>
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      <value>Self-Supervised Deep Learning</value>
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