2024_Ruthe_SND/code/GetQuantitativeValues.py

import os
import glob
import pandas as pd
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import argparse
from scipy.ndimage import binary_dilation

def create_output_dir(output_dir):
    """Create the output directory if it does not exist."""
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

def save_mask_as_nifti(mask_data, output_file):
    """Save mask data as NIfTI file."""
    # Convert boolean mask to integer (0s and 1s)
    mask_data_int = mask_data.astype(np.uint8)
    
    # Create NIfTI image object
    mask_img = nib.Nifti1Image(mask_data_int, affine=None)
    
    # Save NIfTI image
    nib.save(mask_img, output_file)


def process_files(input_path, output_path, create_figures, save_nifti_mask):
    """Process DWI files and generate figures."""
    # Define a dictionary to map Experiment values to the desired timepoints
    session_mapping = {
        0: 0,
        1: 3, 2: 3, 3: 3,
        4: 3, 5: 3, 6: 7, 7: 7,
        8: 7, 9: 7, 10: 7, 11: 14, 12: 14,
        13: 14, 14: 14, 15: 14, 16: 14, 17: 14, 18: 14, 19: 21,
        20: 21, 21: 21, 22: 21, 23: 21, 24: 21, 25: 21, 26: 28,
        27: 28, 28: 28, 29: 28, 30: 28 , 42:42, 43:42, 56:56, 57:56
    }

    # Initialize a list to store extracted information
    csvdata = []

    # Iterate over files with progress bar
    file_paths = glob.glob(os.path.join(input_path, "**", "dwi", "DSI_studio", "*_flipped.nii.gz"), recursive=True)
    for file_path in tqdm(file_paths, desc="Processing files"):
        # Extract information from the file path
        subject_id = file_path.split(os.sep)[-5]
        time_point = file_path.split(os.sep)[-4]
        int_time_point = 0 if time_point == "ses-Baseline" else int(time_point.split("-P")[1])
        merged_time_point = session_mapping[int_time_point]
        q_type = os.path.basename(file_path).split("_flipped")[0]
        try:
            searchStroke = os.path.join(os.path.dirname(file_path),"*StrokeMask_scaled.nii")
            stroke_path = glob.glob(searchStroke)[0]
        except IndexError:
            stroke_path = False
            
        # Create the temp path to search for dwi_masks
        temp_path = os.path.join(os.path.dirname(file_path), "RegisteredTractMasks_adjusted")
        dwi_masks = glob.glob(os.path.join(temp_path, "*dwi_flipped.nii.gz"))

        # Load DWI data using nibabel
        dwi_img = nib.load(file_path)
        dwi_data = dwi_img.get_fdata()

        # Loop through masks and calculate average values
        max_pixels = -1
        selected_slice = None
        pix_dialation = [0,1,2,3,4]
        for mask_path in tqdm(dwi_masks, desc="Processing masks", leave=False):
            mask_name = os.path.basename(mask_path).replace("registered", "").replace("flipped", "").replace(".nii.gz", "").replace("__dwi_","")
            mask_img = nib.load(mask_path)
            mask_data_pre_dilation = mask_img.get_fdata()

            # Find the slice with the highest number of pixels
            for i in range(mask_data_pre_dilation.shape[2]):
                num_pixels = np.count_nonzero(mask_data_pre_dilation[..., i])
                if num_pixels > max_pixels:
                    max_pixels = num_pixels
                    selected_slice = i

            # Assuming you want to calculate the average value within the mask region
            for pp in pix_dialation:
                if pp != 0:
                    mask_data = binary_dilation(mask_data_pre_dilation, iterations=pp)
                else:
                    mask_data = mask_data_pre_dilation > 0
                    
                if stroke_path:
                    stroke_image = nib.load(stroke_path)
                    stroke_data = stroke_image.get_fdata()    
                    # Subtracting stroke region from mask
                    if merged_time_point == 3 or merged_time_point == 28:
                        mask_data = mask_data & (stroke_data < 1)
                    else:
                        mask_data = mask_data
                    
                    masked_data = dwi_data[mask_data > 0]
                    non_zero_masked_data = masked_data[masked_data != 0]  # Exclude zero values
                    average_value = np.nanmean(non_zero_masked_data)  # Calculate mean only on non-zero elements

                    strokeFlag = "Stroke"
                else:
                    masked_data = dwi_data[mask_data > 0]
                    non_zero_masked_data = masked_data[masked_data != 0]  # Exclude zero values
                    average_value = np.nanmean(non_zero_masked_data)  # Calculate mean only on non-zero elements
                    strokeFlag = "Sham"
                # Create and save the figure if the flag is True
                if create_figures:
                    # Create a plot of the selected slice with overlay
                    fig, ax = plt.subplots(1, 1, figsize=(10, 10), dpi=50)
                    ax.imshow(dwi_data[..., selected_slice], cmap='gray')
                    ax.imshow(mask_data[..., selected_slice], cmap='jet', alpha=0.5)  # Overlay mask_data
                    ax.set_title(f"{subject_id}_tp_{merged_time_point}_{q_type}_{average_value:.2f}_{mask_name}_{pp}")
                    plt.axis('off')
    
                    # Save the plot to the output path
                    output_file = os.path.join(output_path,
                                               f"{subject_id}_tp_{merged_time_point}_{q_type}_{average_value:.2f}_{mask_name}_{pp}.png")
                    plt.savefig(output_file, bbox_inches='tight', pad_inches=0)
    
                    # Close the plot to release memory
                    plt.close(fig)

                if save_nifti_mask and pp > 0 and merged_time_point in [3, 28] and q_type == "fa":
                    # Save the mask as NIfTI file
                    output_nifti_file = os.path.join(output_path,
                                                      f"{subject_id}_tp_{merged_time_point}_{q_type}_{mask_name}_{pp}.nii.gz")
                    save_mask_as_nifti(mask_data, output_nifti_file)
    
                # Append the extracted information to the list
                csvdata.append([file_path, subject_id, time_point, int_time_point, merged_time_point, q_type, mask_name, pp ,average_value,strokeFlag])

    # Create a DataFrame
    df = pd.DataFrame(csvdata,
                      columns=["fullpath", "subjectID", "timePoint", "int_timepoint", "merged_timepoint", "Qtype", "mask_name",
                               "dialation_amount","Value","Group"])

    # Return DataFrame
    return df

def main():
    # Parse command-line arguments
    parser = argparse.ArgumentParser(description="Process DWI files and generate figures.")
    parser.add_argument("-i", "--input", type=str, required=True, help="Input directory containing DWI files. e.g: proc_data (flipped fa, md etc file must be available. registering of masks must happen before this step")
    parser.add_argument("-o", "--output", type=str, required=True, help="Output directory to save output results (csv and figures).")
    parser.add_argument("-f", "--figures", action="store_true", default=False, help="set if you want figures to be saved as pngs")
    parser.add_argument("-n", "--nifti-mask", action="store_true", default=False, help="set if you want masks to be saved as NIfTI files.")

    args = parser.parse_args()

    # Create the output directory if it does not exist
    create_output_dir(args.output)

    # Process files
    df = process_files(args.input, args.output, args.figures, args.nifti_mask)

    # Save the DataFrame as CSV
    csv_file = os.path.join(args.output, "Quantitative_results_from_dwi_processing.csv")
    df.to_csv(csv_file, index=False)
    print("CSV file created at:", csv_file)

    # Print the DataFrame
    print(df)

if __name__ == "__main__":
    main()