| Project: | NYU machine learning data |
| Anatomy: | Knee |
| Fullysampled: | Yes |
| Uploader: | florianknoll |
| UUID | 413469fd-9354-400c-88e3-b29e7c711a05 |
|---|---|
| Downloads | 307 |
| 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 |
| UUID | 6a8fff64-9bba-4ce7-aa58-d024214b4d7a |
|---|---|
| Downloads | 532 |
| 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 |
| UUID | bd01dd30-46e7-4415-bf04-ed4cc6ac2b64 |
|---|---|
| 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 | 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 |
| UUID | 6493682f-c9d3-44a7-8c0f-7fdf8b165410 |
|---|---|
| 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 | 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 |
| UUID | 48c95dcf-c074-499b-a63c-74f0bf7dff1f |
|---|---|
| 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 | 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 |
| UUID | de6d23d9-b1c4-46af-bc79-ce828f5cd63a |
|---|---|
| Downloads | 121 |
| 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 |
| UUID | e3573a0f-34f7-4718-827a-027bf9dd4dea |
|---|---|
| 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 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 |
| UUID | a496da39-848a-4fe8-8954-ff63df6785cb |
|---|---|
| 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 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. |
| Project: | NYU machine learning data |
| Anatomy: | Knee |
| Fullysampled: | Yes |
| Uploader: | florianknoll |
| UUID | 516288da-d22b-4e92-9282-96665134c84a |
|---|---|
| Downloads | 121 |
| 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, 7:06 a.m. |
| Project: | NYU machine learning data |
| Anatomy: | Knee |
| Fullysampled: | Yes |
| Uploader: | florianknoll |
| UUID | 90bc0b15-eea2-4abb-b0d2-f3fdad57bb43 |
|---|---|
| Downloads | 114 |
| 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, 7:05 a.m. |