Total 249 datasets
Project: Cells Test
Anatomy: Unknown
Fullysampled: Unknown
Uploader: dxmarty
Tags:

UUID a9c5a204-56ec-449b-ba7f-aa750d835337
Downloads 28
Protocol Name svs_se_breast_int_ref LEFT
Series Description svs_se_breast_int_ref LEFT
System Vendor SIEMENS
System Model Espree
System Field Strength 1.494 T
Receiver Bandwidth 0.793
Number of Channels 4
Institution Name Milpark
Matrix Size 2 x 2 x 1
Field Of View 900 mm x 450 mm x 20 mm
Number of Averages 1
Number of Slices 1
Number of Phases 1
Number of Repetition 1
Number of Contrasts 1
Trajectory cartesian
Parallel Imaging Factor 1.0 x 1.0
Repetition Time 2000 ms
Echo Time 100 ms
Flip Angle 90 °
Upload Date March 19, 2021, 3:57 a.m.

Project: Liv
Anatomy: Breast
Fullysampled: No
Uploader: bryankimmel
Tags:

UUID e3a5bed8-eb13-416e-bfba-0dee85f0ef5e
Downloads 27
Protocol Name svs_se_breast_int_ref
Series Description svs_se_breast_int_ref
System Vendor SIEMENS
System Model Espree
System Field Strength 1.494 T
Receiver Bandwidth 0.793
Number of Channels 4
Institution Name Milpark
Matrix Size 2 x 2 x 1
Field Of View 900 mm x 450 mm x 20 mm
Number of Averages 1
Number of Slices 1
Number of Phases 1
Number of Repetition 1
Number of Contrasts 1
Trajectory cartesian
Parallel Imaging Factor 1.0 x 1.0
Repetition Time 2000 ms
Echo Time 100 ms
Flip Angle 90 °
Upload Date March 16, 2021, 12:11 a.m.

Project: NYU machine learning data
Anatomy: Knee
Fullysampled: Yes
Uploader: florianknoll
Tags:
  • test
  • UUID 413469fd-9354-400c-88e3-b29e7c711a05
    Downloads 1891
    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, 7:12 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID 6a8fff64-9bba-4ce7-aa58-d024214b4d7a
    Downloads 4050
    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, 7:12 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID bd01dd30-46e7-4415-bf04-ed4cc6ac2b64
    Downloads 993
    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, 7:11 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID 6493682f-c9d3-44a7-8c0f-7fdf8b165410
    Downloads 931
    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, 7:10 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID 48c95dcf-c074-499b-a63c-74f0bf7dff1f
    Downloads 977
    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 32
    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, 7:09 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID de6d23d9-b1c4-46af-bc79-ce828f5cd63a
    Downloads 907
    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, 7:08 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID e3573a0f-34f7-4718-827a-027bf9dd4dea
    Downloads 893
    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 672 x 1
    Field Of View 280 mm x 245.1 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 2.0 x 1.0
    Repetition Time 2300 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, 7:07 a.m.

    Project: NYU machine learning data
    Anatomy: Knee
    Fullysampled: Yes
    Uploader: florianknoll
    Tags:

    UUID a496da39-848a-4fe8-8954-ff63df6785cb
    Downloads 880
    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 768 x 1
    Field Of View 280 mm x 280.3 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 2.0 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, 7:07 a.m.

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