Keyword	Usage	Notes
Uploader	uploader: <uploader> , uploader: me	Keywords and values are case insensitive . "me" returns all the datasets that you uploaded.
Tags	tag: <tag 1> and <tag 2> , tags: <tag 1> and <tag 2> , tag: <tag 1>	Keywords and value are case insensitive. Inserting "and" will allow you to search for multiple tags. tags and tag are the same.
Project	project: <project name> ,	Keywords and value are case insensitive.
Anatomy	anatomy: <anatomy> ,	Keywords and value are case insensitive.
References	references: <references> , ref: <references>	Keywords and value are case insensitive. Ref and References are the same thing, but ref is a shortcut for references.
Comments	comments: <comment>	Keywords and value are case insensitive. Furthermore, it does NOT have to be an exact match, but rather if it contains.
Funding Support	funding_support: <supporter> , funding: <supporter> , support: <supporter> ,	Keywords and value are case insensitive. "funding" and "support" are both shortcuts for "funding_support".
System Vendor	system_vendor: <vendor> , vendor: <vendor>	Keywords and value are case insensitive. "vendor" is a shortcut for "system_vendor".
System Model	system_model: <model> , model: <model>	Keywords and value are case insensitive. "model" is a shortcut for "system_model".
Protocol Name	protocol_name: <protocol> , protocol: <protocol>	Keywords and value are case insensitive. "protocol" is a shortcut for "protocol_name".
Coil Name	coil_name: <coil> , coil: <coil>	Keywords and value are case insensitive. "coil" is a shortcut for "coil_name".
Sequence Type	sequence_type: <sequence> , sequence: <sequence>	Keywords and value are case insensitive. "sequence" is a shortcut for "sequence_type".
UUID	uuid: <uuid> , id: <uuid>	Keywords and value are case insensitive. "id" is a shortcut for "uuid".
Fully Sampled	fullysampled: <yes or no> , sampled: <yes or no>	Keywords and value are case insensitive. If sampled is unknown, state "unknown" or leave it blank. Response can also be "y" for "yes" and "n" for "no". Responses can also take the form of "1" for "yes" and "0" for "no". "sampled" is a shortcut for "fullysampled".

Project:	NYU machine learning data
Anatomy:	Knee
Fullysampled:	Yes
Uploader:	florianknoll
Tags:

UUID	dd096d52-40a6-48a2-b55e-39dcc9115691
Downloads	1064
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	4300 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:12 a.m.

UUID	4ee8c9fe-cee4-47db-824e-f49862797f66
Downloads	1109
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	4900 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:12 a.m.

UUID	38b97dec-7d88-4746-94a0-47c7e3295115
Downloads	1101
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	640 x 646 x 1
Field Of View	280 mm x 282.8 mm x 4.5 mm
Number of Averages	1
Number of Slices	37
Number of Phases	1
Number of Repetition	1
Number of Contrasts	1
Trajectory	cartesian
Parallel Imaging Factor	1.0 x 1.0
Repetition Time	5030 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:11 a.m.

UUID	c5313660-3d9f-4337-815d-cb9dbf35e55a
Downloads	1106
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	5440 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:10 a.m.

UUID	6c3e7462-d1af-4f81-85ec-ba01e142e098
Downloads	1073
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	4630 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:09 a.m.

UUID	708d3627-7e12-48bf-b830-205c74323e77
Downloads	1053
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	5440 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:08 a.m.

UUID	60418802-cd0c-4753-8582-d26bd7f0f7cb
Downloads	1106
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	4900 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:06 a.m.

UUID	e222e8f8-5e08-46fd-97d8-3c68c2aabb40
Downloads	1074
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	5170 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:05 a.m.

UUID	ed9b83c0-ca84-4777-8038-e7c5dcbe8543
Downloads	1093
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	640 x 646 x 1
Field Of View	280 mm x 282.8 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	4490 ms
Echo Time	50 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	10.01 ms
Upload Date	Aug. 7, 2018, 7:04 a.m.

UUID	b03f0bf5-200e-45b5-818e-e4a37142e2f5
Downloads	1147
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	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	2750 ms
Echo Time	27 ms
Inversion Time	100 ms
Flip Angle	180 °
Sequence Type	TurboSpinEcho
Echo Spacing	8.85 ms
Upload Date	Aug. 7, 2018, 6:49 a.m.

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