fastMRI

Slice thickness about the brain dataset?

#1

Hi.
I have a question about the slice thickness and gap information of the brain dataset.

We already know the slice thickness of the knee dataset, which is 3mm, and no gap between slices. But how about the brain dataset. We found that the neighbor slices are quite different in content. Could you offer the slice thickness and gap information about the brain dataset?

Thanks in advance.

#2

Hello, you can find the slice information in the ‘ismrmrd_header’ field. You can use the ISMRMRD Python package to parse it.

I checked with a few people and to my knowledge no slice gaps are in the data set, but some files I checked did have bigger slices than the knee data set (5 mm brain vs. 3 mm knee).

#3

Okay, thank you again!

#4

Hi, could you please point me to where the slice thickness is in the ISMRMRD header?

For example copied below is (a) a list of all dictionary keys in the flattened XML and (b) the XML string read from file_brain_AXFLAIR_200_6002471.h5:

Thank you!

(a)
{http://www.w3.org/2001/XMLSchema-instance}schemaLocation studyInformation_studyTime measurementInformation_measurementID measurementInformation_patientPosition measurementInformation_protocolName measurementInformation_frameOfReferenceUID acquisitionSystemInformation_systemVendor acquisitionSystemInformation_systemModel acquisitionSystemInformation_systemFieldStrength_T acquisitionSystemInformation_relativeReceiverNoiseBandwidth acquisitionSystemInformation_receiverChannels acquisitionSystemInformation_coilLabel_coilNumber acquisitionSystemInformation_coilLabel_coilName acquisitionSystemInformation_institutionName experimentalConditions_H1resonanceFrequency_Hz encoding_encodedSpace_matrixSize_x encoding_encodedSpace_matrixSize_y encoding_encodedSpace_matrixSize_z encoding_encodedSpace_fieldOfView_mm_x encoding_encodedSpace_fieldOfView_mm_y encoding_encodedSpace_fieldOfView_mm_z encoding_reconSpace_matrixSize_x encoding_reconSpace_matrixSize_y encoding_reconSpace_matrixSize_z encoding_reconSpace_fieldOfView_mm_x encoding_reconSpace_fieldOfView_mm_y encoding_reconSpace_fieldOfView_mm_z encoding_trajectory encoding_encodingLimits_kspace_encoding_step_1_minimum encoding_encodingLimits_kspace_encoding_step_1_maximum encoding_encodingLimits_kspace_encoding_step_1_center encoding_encodingLimits_kspace_encoding_step_2_minimum encoding_encodingLimits_kspace_encoding_step_2_maximum encoding_encodingLimits_kspace_encoding_step_2_center encoding_encodingLimits_average_minimum encoding_encodingLimits_average_maximum encoding_encodingLimits_average_center encoding_encodingLimits_slice_minimum encoding_encodingLimits_slice_maximum encoding_encodingLimits_slice_center encoding_encodingLimits_contrast_minimum encoding_encodingLimits_contrast_maximum encoding_encodingLimits_contrast_center encoding_encodingLimits_phase_minimum encoding_encodingLimits_phase_maximum encoding_encodingLimits_phase_center encoding_encodingLimits_repetition_minimum encoding_encodingLimits_repetition_maximum encoding_encodingLimits_repetition_center encoding_encodingLimits_set_minimum encoding_encodingLimits_set_maximum encoding_encodingLimits_set_center encoding_encodingLimits_segment_minimum encoding_encodingLimits_segment_maximum encoding_encodingLimits_segment_center encoding_parallelImaging_accelerationFactor_kspace_encoding_step_1 encoding_parallelImaging_accelerationFactor_kspace_encoding_step_2 encoding_parallelImaging_calibrationMode sequenceParameters_TR sequenceParameters_TE sequenceParameters_TI sequenceParameters_flipAngle_deg sequenceParameters_sequence_type sequenceParameters_echo_spacing userParameters_userParameterDouble
(b)

<?xml version="1.0" encoding="utf-8"?> 15:56:46 45219_144817763_144817772_2442 HFS AX FLAIR_FBB 1.3.12.2.1107.5.2.19.45219.1.20190621155211902.0.0.4988 SIEMENS Skyra 2.8936 0.793 16 26 HeadNeck_20:1:H14 25 HeadNeck_20:1:H13 42 HeadNeck_20:1:H23 41 HeadNeck_20:1:H24 34 HeadNeck_20:1:H32 33 HeadNeck_20:1:H31 38 HeadNeck_20:1:H12 37 HeadNeck_20:1:H11 30 HeadNeck_20:1:H42 29 HeadNeck_20:1:H41 18 HeadNeck_20:1:H44 17 HeadNeck_20:1:H43 45 HeadNeck_20:1:H34 46 HeadNeck_20:1:H33 21 HeadNeck_20:1:H21 22 HeadNeck_20:1:H22 NYU LANGONE CBI 123181954 640 320 1 440 220 7.5 320 320 1 220 220 5 cartesian 0 223 112 0 0 0 0 0 0 0 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 other 9000 81 2500 150 TurboSpinEcho 9.02 MaxwellCoefficient_0 0 MaxwellCoefficient_1 0 MaxwellCoefficient_2 0 MaxwellCoefficient_3 0 MaxwellCoefficient_4 0 MaxwellCoefficient_5 0 MaxwellCoefficient_6 0 MaxwellCoefficient_7 0 MaxwellCoefficient_8 0 MaxwellCoefficient_9 0 MaxwellCoefficient_10 0 MaxwellCoefficient_11 0 MaxwellCoefficient_12 0 MaxwellCoefficient_13 0 MaxwellCoefficient_14 0 MaxwellCoefficient_15 0
#5

@RGG I apologize for not replying to this earlier - I don’t get as many notifications from the forum. It should be something like the following, in case people are still looking at this thread.

import h5py
import xml.etree.ElementTree as etree
from fastmri.data.mri_data import et_query

with h5py.File(fname, "r") as hf:
    et_root = etree.fromstring(hf["ismrmrd_header"][()])
    print(int(et_query(et_root, ["encoding", "reconSpace", "fieldOfView_mm", "z"])))