Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | cafc2ace-a826-4344-8cf9-896ec8bc6120 |
---|---|
Downloads | 1574 |
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: |
UUID | 936c76f0-b3af-41f0-9b2d-f468f6a71225 |
---|---|
Downloads | 1508 |
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: |
UUID | e5bea69e-3b5e-44e9-9307-c182b8caf6db |
---|---|
Downloads | 1543 |
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: |
UUID | 7953af76-63f4-4b64-984a-adbc67ade280 |
---|---|
Downloads | 1519 |
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: |
UUID | cc52722b-8649-45b0-a1ea-8727c1687ad5 |
---|---|
Downloads | 1529 |
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: |
UUID | 18fe726f-1085-4c93-989b-0f79f084fbe4 |
---|---|
Downloads | 1515 |
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: |
UUID | 907e4462-c45d-4a62-8ade-553f2c217312 |
---|---|
Downloads | 1489 |
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: |
UUID | af169293-1b83-4bd9-a8cf-4708325cdf73 |
---|---|
Downloads | 1454 |
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 | 1449 |
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 | 1461 |
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. |