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Make_AIF brick

Compute the Arterial Input Function (AIF) from dynamic MRI perfusion data

  • Loading Data: The code begins by loading 4D MRI data (3D spatial data over time) and determining its dimensions.

  • Thresholding and Masking: It applies an intensity threshold to create a mask that identifies relevant brain tissue, excluding non-brain areas and noisy voxels.

  • Filtering Candidates: The algorithm selects candidate voxels based on their dynamic signal characteristics, such as intensity changes and peak widths.

  • Peak Analysis: It calculates key metrics like the baseline intensity, minimum intensity, and the width of the dynamic peak for each voxel.

  • Excluding Artifacts: Voxels with artifacts or unrealistic dynamic behaviors are excluded to ensure accuracy.

  • Scoring and Selection: Each voxel is scored based on its dynamic properties. The top candidate voxels are then selected, and their signals are averaged to compute the final AIF.

  • Saving Results: The resulting AIF and the scoring details of the selected voxels are saved in a JSON file for further analysis.

Inputs parameters:

  • func_file

    T2* functional Magnetic Resonance Imaging (fMRI) experiment recorded during gadolinium bolus. Ideally, the data will have been pre-processed (realignment, segmentation, etc.). An existing, uncompressed file (valid extensions: [.img, .nii, .hdr]).

    ex. '/home/username/data/raw_data/swrfunc.nii'
  • bat_window_size

    Number of time points (or dynamics) used to determine the bolus arrival time (t0) in the MRI signal analysis (an integer). Acts as a sliding window that moves in the time dimension of the MRI data, allowing the algorithm to analyse the local temporal behaviour at each voxel. A large window provides smoother estimates of the signal, but can delay the detection of sudden changes, such as the arrival of the bolus. It increases stability, but can also blur the sharp transition at bolus arrival. A smaller window size will be more sensitive to rapid changes, but may also pick up more noise or fluctuations in the signal. The default value (8) is a moderately sized window to smooth out short-term fluctuations while detecting significant changes in the MRI signal value.

    ex. 8
  • bat_th

    Threshold multiplier (a float) is used to detect significant changes in the signal intensity of MRI data (controls the sensitivity of the bolus arrival detection). A high th value (e.g., th = 3.0) would make the algorithm less sensitive, meaning it will only consider larger, more pronounced signal drops as valid bolus arrival points. This reduces false positives but may miss subtle or gradual bolus arrivals. A low th value (e.g., th = 1.0) would make the algorithm more sensitive, catching even smaller signal drops. However, this may lead to false positives, where noise or natural fluctuations in the signal are incorrectly identified as bolus arrival. The default value (2.0) ensures only signal drops larger than 2 standard deviations below the mean are considered significant, providing a balance between sensitivity and robustness against noise.

    ex. 2.0
  • wmaxr

    Scaling factor (a float) used to define the threshold for acceptable peak widths of voxel intensity curves. It is a percentage of the total number of dynamics, which represents the maximum width allowed for peaks within the time series data of each voxel. Increasing wmaxr will relax the criterion for peak width, allowing wider peaks to be included in the region of interest (ROI). This might allow more voxels with broader peaks but could also include noisier or less precise signals. Decreasing wmaxr will make the criterion stricter, permitting only narrower peaks and potentially excluding more noisy voxels. This could increase precision but might also exclude valid data.

    ex. 0.5
  • nb_vox_cand

    Number of candidate voxels to be evaluated for computing the AIF (an integer).

    ex. 50
  • nb_vox_best_scor

    Number of best-scoring voxels to be used for computing the AIF after evaluating the initial set of candidate voxels (an integer).

    ex. 5

Outputs parameters:

  • aif_file

    The output data from the DSC-MRI AIF computation (a file with .json format). It includes the computed Arterial Input Function (aif key) and associated scoring details of selected voxels (scores key).

    In scores, the elements are, in this order:

    • Score value: The more intense and finer the peak, the higher the score.

    • Row index: Row index of the selected voxel.

    • Column index: Column index of the selected voxel.

    • Slice index: slice index of the selected voxel.

    • Number of warnings : The number of warnings when calculating AIF.

    • Pre-bolus baseline test: Indicates whether the pre-bolus baseline is too noisy. None value indicates no problem detected.

    • Post-bolus baseline test: Indicates whether the post-bolus baseline is too noisy. None value indicates no problem detected.

    • t0 point test: Indicates whether the voxel value at the time of bolus arrival (t0) is greater than 11/10th of the baseline mean. None value indicates no problem detected.

    • Pre-bolus baseline length test: Indicates whether the pre-bolus baseline length is too short (< 8 dynamics). None value indicates no problem detected.

    ex. '/home/username/data/derived_data/swrfunc_aif.json'