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Welcome to the slice_timings wiki!

Small module to calculate fMRI slice timings

fmriprep (https://github.com/poldracklab/fmriprep) requires a JSON file to contain meta information.

Here is an example JSON file
{ "RepetitionTime": 3.0,
"Instruction": "Lie still and keep your eyes open",
"TaskName": "rest",
"EchoTime": 0.030,
"PhaseEncodingDirection" : "j",
"EffectiveEchoSpacing" : 0.0354,
"ParallelReductionFactorInPlane":1,
"NumberOfVolumesDiscardedByScanner" : 4,
"SliceTiming": [ 0.0, 1.5, 0.0625, 1.5625, 0.125, 1.625, 0.1875, 1.6875, 0.25, 1.75, 0.3125, 1.8125, 0.375, 1.875, 0.4375, 1.9375, 0.5, 2.0, 0.5625, 2.0625, 0.625, 2.125, 0.6875, 2.1875, 0.75, 2.25, 0.8125, 2.3125, 0.875, 2.375, 0.9375, 2.4375, 1.0, 2.5, 1.0625, 2.5625, 1.125, 2.625, 1.1875, 2.6875, 1.25, 2.75, 1.3125, 2.8125, 1.375, 2.875, 1.4375, 2.9375 ]
}

Not all of the information needs to be in the JSON file. In particular, an array for slice timing needs to be calculated. This calculation is straight forward.

slice_timings serves as a python command line script and importable module/function to calculate this for the user.

SliceTiming is in seconds from the first slice (0s) to the last slice (TR-<slice_acquisition_time>). The values of SliceTiming are stored in slice order not acquisition order.

Here is the calculation

Inputs:

TR = 3 # Repetition Time
n_slices = 48 # Number of Slices
order_slices = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47] # This is a Siemens Interleaved ordering scheme. Even slices first, then odd
slices = list(range(1,nSlices-1))

Intermediate results:
delta = TR/n_slices
slice_timing = sorted(list( zip(order, slice*delta))) # You want to sort on first

Output:
return slice_timing[:,2]

Here is an example
TR = 3
nSlices = 10
order = [0, 2, 4, 6, 8, 1, 3, 5, 7, 9]
slice = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

[{slice number, sliceTiming},] == [{0, 0.}, {1, 1.5}, {2, 0.3}, {3, 1.8}, {4, 0.6}, {5, 2.1}, {6, 0.9}, {7, 2.4}, {8, 1.2}, {9, 2.7}]

return [0.0, 1.5, 0.3, 1.8, 0.6, 2.1, 0.9, 2.4, 1.2, 2.7]