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	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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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