Total 100 datasets
Project: NYU machine learning data
Anatomy: Knee
Fullysampled: Yes
Uploader: florianknoll
Tags:
  • t
  • UUID cafc2ace-a826-4344-8cf9-896ec8bc6120
    Downloads 127
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 676 x 1
    Field Of View 280 mm x 246.1 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 38
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 7:03 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID 936c76f0-b3af-41f0-9b2d-f468f6a71225
    Downloads 125
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 33
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 7:02 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID e5bea69e-3b5e-44e9-9307-c182b8caf6db
    Downloads 128
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 35
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 7:01 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID 7953af76-63f4-4b64-984a-adbc67ade280
    Downloads 122
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 33
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 7 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID cc52722b-8649-45b0-a1ea-8727c1687ad5
    Downloads 120
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 36
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:58 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID 18fe726f-1085-4c93-989b-0f79f084fbe4
    Downloads 123
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 31
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:57 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID 907e4462-c45d-4a62-8ade-553f2c217312
    Downloads 122
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 38
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:56 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID af169293-1b83-4bd9-a8cf-4708325cdf73
    Downloads 120
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 35
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:54 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID c22a01be-8903-4ad3-b58d-3781b2d20bf8
    Downloads 111
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 31
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:52 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:
  • t
  • UUID 3b2f97c1-6c7a-41b7-82bb-698f0b6fd3d0
    Downloads 122
    References Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)
    Comments This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork.
    Funding Support NIH P41 EB017183
    Protocol Name SAG
    Series Description SAG
    System Vendor SIEMENS
    System Model Skyra
    System Field Strength 2.89362 T
    Receiver Bandwidth 0.793
    Number of Channels 15
    Coil Name TxRx_15Ch_Knee:1:K5
    Institution Name HJD
    Matrix Size 768 x 770 x 1
    Field Of View 280 mm x 280.7 mm x 4.5 mm
    Number of Averages 1
    Number of Slices 34
    Number of Phases 1
    Number of Repetition 1
    Number of Contrasts 1
    Trajectory cartesian
    Parallel Imaging Factor 1.0 x 1.0
    Repetition Time 2800 ms
    Echo Time 22 ms
    Inversion Time 100 ms
    Flip Angle 150 °
    Sequence Type TurboSpinEcho
    Echo Spacing 11.12 ms
    Upload Date Aug. 8, 2018, 6:51 a.m.

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