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	413469fd-9354-400c-88e3-b29e7c711a05
Downloads	555
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.

UUID	6a8fff64-9bba-4ce7-aa58-d024214b4d7a
Downloads	1178
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.

UUID	bd01dd30-46e7-4415-bf04-ed4cc6ac2b64
Downloads	258
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.

UUID	6493682f-c9d3-44a7-8c0f-7fdf8b165410
Downloads	274
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.

UUID	48c95dcf-c074-499b-a63c-74f0bf7dff1f
Downloads	257
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.

UUID	de6d23d9-b1c4-46af-bc79-ce828f5cd63a
Downloads	246
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.

UUID	e3573a0f-34f7-4718-827a-027bf9dd4dea
Downloads	241
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.

UUID	a496da39-848a-4fe8-8954-ff63df6785cb
Downloads	236
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.

UUID	516288da-d22b-4e92-9282-96665134c84a
Downloads	248
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.

UUID	90bc0b15-eea2-4abb-b0d2-f3fdad57bb43
Downloads	247
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.

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