Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 3b9624fd-87dc-4b52-a348-65b883797c13 |
---|---|
Downloads | 894 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
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Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 2930 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:36 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 9a4547c6-ff93-480b-a6e7-ed7300e1af60 |
---|---|
Downloads | 892 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:33 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | cb6b0a02-7a18-4676-884d-7668e609e351 |
---|---|
Downloads | 912 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 3010 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:30 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | e12bd02b-6456-4d31-8b01-c3b7843da0a6 |
---|---|
Downloads | 914 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:28 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 405a8fee-8aa7-495a-bbff-f202deae1d49 |
---|---|
Downloads | 863 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 40 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3170 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:27 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | d12107c4-5509-40b5-b02b-724efcf698e8 |
---|---|
Downloads | 915 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:24 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | e457ccd3-a275-4117-8710-ff110cf1ccd7 |
---|---|
Downloads | 865 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:22 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | bce10a38-b1a6-4478-b359-3b4060dbf5e7 |
---|---|
Downloads | 899 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 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 | 3010 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:20 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 6c10376d-2825-4275-9d37-7cbac95b73d1 |
---|---|
Downloads | 874 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 41 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3250 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:18 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | a5d10e70-627b-4a4c-943b-0639fd2cee8d |
---|---|
Downloads | 882 |
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 | COR |
Series Description | COR |
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 | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 39 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3170 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:16 p.m. |