Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | af169293-1b83-4bd9-a8cf-4708325cdf73 |
---|---|
Downloads | 987 |
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: |
UUID | c22a01be-8903-4ad3-b58d-3781b2d20bf8 |
---|---|
Downloads | 979 |
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: |
UUID | 3b2f97c1-6c7a-41b7-82bb-698f0b6fd3d0 |
---|---|
Downloads | 998 |
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. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 36ddbca0-c5fd-41a3-854d-4790649d89c2 |
---|---|
Downloads | 864 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4000 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:06 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | c155c00a-80af-421b-ae48-e7ed0dec9777 |
---|---|
Downloads | 877 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4000 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:05 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | d65e98c1-f893-48b5-b093-057b327a410c |
---|---|
Downloads | 813 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4260 ms |
Echo Time | 67 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 11.12 ms |
Upload Date | Aug. 7, 2018, 11:04 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 3c8b5e73-29ab-400d-b397-5f0bca4b7cb6 |
---|---|
Downloads | 840 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4100 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:03 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 34c9860c-c752-4062-aaf5-530d63272c98 |
---|---|
Downloads | 835 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4210 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:03 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 47a37b36-d970-47b4-ac8c-3b2c8d50d002 |
---|---|
Downloads | 829 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4310 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:02 a.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 2448503f-4fda-4e3e-b269-bfffa814962d |
---|---|
Downloads | 851 |
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 | AX |
Series Description | AX |
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 484 x 1 |
Field Of View | 280 mm x 211.7 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 | 4000 ms |
Echo Time | 65 ms |
Inversion Time | 100 ms |
Flip Angle | 150 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 9.33 ms |
Upload Date | Aug. 7, 2018, 11:01 a.m. |