francopestilli
commented
6 years ago I attach our communication here:
INPUTS. Inputs and out puts should theoretically be a DataType containing multishell DWI data. BVECS/BVALS. Ideally the App should create its own internal files that dipy might need to read these files, for example like the GTAB. Does this make sense to you? Perhaps we can start a thread with Ariel and Soichi about this issue? I Started an issue for us on GitHub.
PARAMETERS. Parameters should be set by the user but have default that work well with the HCP 3T dataset. For example parameters such as radial order.
OUTPUTS. Outputs should be all NIFTI files with the same header orientation of the input file. No plots. We should not be worried if many output files are created, but instead, create as many ask possible.
Below all the outputs for which we should create a NIFTI: • 1 Mean Squared Displacement (MSD) is a measure of how far protons are able to diffuse. a decrease in MSD indicates protons are hindered/restricted more, as can be seen by the high MSD in the CSF, but low in the white matter. • 2 Q-space Inverse Variance (QIV) is a measure of variance in the signal, which is said to have higher contrast to white matter than the MSD [Hosseinbor2013]. We also showed that QIV has high sensitivity to tissue composition change in a simulation study [Fick2016b]. • 3 Return to origin probability (RTOP) quantifies the probability that a proton will be at the same position at the first and second diffusion gradient pulse. A higher RTOP indicates that the volume a spin is inside of is smaller, meaning more overall restriction. This is illustrated by the low values in CSF and high values in white matter. • 4 Return to axis probability (RTAP) is a directional index that quantifies the probability that a proton will be along the axis of the main eigenvector of a diffusion tensor during both diffusion gradient pulses. RTAP has been related to the apparent axon diameter [Ozarslan2013] [Fick2016a] under several strong assumptions on the tissue composition and acquisition protocol. • 5 Return to plane probability (RTPP) is a directional index that quantifies the probability that a proton will be on the plane perpendicular to the main eigenvector of a diffusion tensor during both gradient pulses. RTPP is related to the length of a pore [Ozarslan2013] but in practice should be similar to that of Gaussian diffusion. • 6 Non-Gaussian diffusion in every voxel [Ozarslan2013]. This quantity is estimated through the ratio between Gaussian volume (MAPMRI’s first basis function) and the non-Gaussian volume (all other basis functions) of the fitted signal. For this value to be physically meaningful we must use a b-value threshold in the MAPMRI model. This threshold makes the scale estimation in MAPMRI only use samples that realistically describe Gaussian diffusion, i.e., at low b-values.
So 6 NIFTIs as output in the DataType for this App.
arokem
aarya22
soichih
It would be great to discuss the needs for the workflow behind this App so to design a proper INPUT/OUTPUT and DataType. @soichih @aarya22