Project: | MRS TEST |
Anatomy: | Unknown |
Fullysampled: | Yes |
Uploader: | krylova.official |
Tags: |
UUID | a133799b-e621-4f97-aedb-1882df644372 |
---|---|
Downloads | 315 |
Protocol Name | mslaser_wref1_onlyRFoff |
Series Description | mslaser_wref1_onlyRFoff |
System Vendor | SIEMENS |
System Model | Prisma_fit |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 34 |
Coil Name | HeadNeck_64:1:H42 |
Institution Name | PFL |
Matrix Size | 2 x 2 x 1 |
Field Of View | 1000 mm x 500 mm x 20 mm |
Number of Averages | 4 |
Number of Slices | 1 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Number of Sets | 1 |
Trajectory | trajectoryType.CARTESIAN |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3000 ms |
Echo Time | 68.8 ms |
Flip Angle | 90 ° |
Upload Date | Aug. 26, 2021, 1:39 p.m. |
Project: | Cells Test |
Anatomy: | Unknown |
Fullysampled: | Unknown |
Uploader: | dxmarty |
Tags: |
UUID | a9c5a204-56ec-449b-ba7f-aa750d835337 |
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Downloads | 606 |
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 |
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Downloads | 560 |
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: |
UUID | 413469fd-9354-400c-88e3-b29e7c711a05 |
---|---|
Downloads | 2549 |
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 | 5814 |
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 | 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 | 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 | 1376 |
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 | 1416 |
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 | 1334 |
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 | 1317 |
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. |