aslprep.utils.confounds module

Functions for calculating and collecting confounds.

average_cbf_by_tissue(cbf, gm, wm, csf, thresh)

Compute mean GM, WM, and CSF CBF values.

Parameters:

• cbf (str) – Path to CBF file.

• gm, wm, csf (str) – Paths to GM, WM, and CSF tissue probability maps, in same space and resolution as cbf.

• thresh (float) – Threshold to apply to the TPMs. Default is 0.7.

Returns:

mean_tissue_cbfs – Mean CBF values from binarized versions of the tissue maps.

Return type:

list of float

compute_qei(gm, wm, csf, img, thresh)

Compute quality evaluation index (QEI) of CBF.

The QEI is based on Dolui et al.[1].

References

dice(input1, input2)

Calculate Dice coefficient between two arrays.

Computes the Dice coefficient (also known as Sorensen index) between two binary images.

The metric is defined as

\[DC=\frac{2|A\cap B|}{|A|+|B|}\]

, where \(A\) is the first and \(B\) the second set of samples (here: binary objects). This method was first proposed in and .

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – The Dice coefficient between input1 and input2. It ranges from 0 (no overlap) to 1 (perfect overlap).

Return type:

float

References

[1]

Sudipto Dolui, Ronald Wolf, Seyed Ali Nabavizadeh, David A. Wolk, and John A. Detre. Automated quality evaluation index for 2d asl cbf maps. International Society for Magnetic Resonance in Medicine, 2016. doi:http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0682.html.

jaccard(input1, input2)

Compute Jaccard coefficient between the binary objects in two images.

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – The Jaccard coefficient between input1 and input2. It ranges from 0 (no overlap) to 1 (perfect overlap).

Return type:

float

negativevoxel(cbf, gm, thresh)

Compute percentage of negative voxels within grey matter mask.

overlap(input1, input2)

Calculate overlap coefficient between two images.

The metric is defined as

\[DC=\frac{|A \cap B||}{min(|A|,|B|)}\]

, where \(A\) is the first and \(B\) the second set of samples (here: binary objects).

The overlap coefficient is also known as the Szymkiewicz-Simpson coefficient .

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – Coverage between two images.

Return type:

float

References

pearson(input1, input2)

Calculate Pearson product moment correlation between two images.

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – Correlation between the two images.

Return type:

float