本文档旨在指导初学者如何使用Python和Nilearn库加载和处理自定义的fMRI（功能性磁共振成像）数据，特别是NIfTI格式的文件。我们将介绍如何使用nilearn.image.load_img函数加载NIfTI图像，并使用get_fdata()方法获取图像数据，以及如何将这些技术整合到现有的多进程代码中。
使用Nilearn加载NIfTI文件
Nilearn是一个Python模块，专门用于神经影像数据的机器学习和统计分析。它提供了方便的函数来加载和处理各种神经影像格式，包括NIfTI。
要加载NIfTI文件，首先需要安装Nilearn：
pip install nilearn
安装完成后，可以使用以下代码加载NIfTI文件：
from nilearn.image import load_img
# 指定NIfTI文件的路径
file_path = "F:\New folder\cn_processed data\Sub1\S1.nii"
# 加载NIfTI图像
try:
nifti_image = load_img(file_path)
print(f"Successfully loaded image from: {file_path}")
except FileNotFoundError:
print(f"Error: File not found at {file_path}")
exit()
except Exception as e:
print(f"An error occurred: {e}")
exit()
# 获取图像数据
data = nifti_image.get_fdata()
# 打印数据的形状
print("Data shape:", data.shape)这段代码首先导入load_img函数，然后指定NIfTI文件的路径。load_img函数会读取NIfTI文件并返回一个Nifti1Image对象。可以使用get_fdata()方法从该对象中提取图像数据，它将返回一个NumPy数组，包含了图像的体素值。
将Nilearn集成到多进程代码中
现在，我们将展示如何将Nilearn集成到提供的多进程代码中，以加载和处理fMRI数据。主要修改集中在read_data函数中。
from monai.transforms import LoadImage # 假设仍然需要monai
import torch
import os
import time
from multiprocessing import Process, Queue
from nilearn.image import load_img
import numpy as np
def read_data(filename, load_root, save_root, subj_name, count, queue=None, scaling_method=None, fill_zeroback=False):
print("processing: " + filename, flush=True)
path = os.path.join(load_root, filename)
try:
# 使用 Nilearn 加载 NIfTI 文件
nifti_image = load_img(path)
data = nifti_image.get_fdata()
except Exception as e:
print(f"Error loading image with Nilearn: {e}")
return None
#change this line according to your file names
save_dir = os.path.join(save_root,subj_name)
isExist = os.path.exists(save_dir)
if not isExist:
os.makedirs(save_dir)
# change this line according to your dataset
data = data[:, 14:-7, :, :]
# width, height, depth, time
# Inspect the fMRI file first using your visualization tool.
# Limit the ranges of width, height, and depth to be under 96. Crop the background, not the brain regions.
# Each dimension of fMRI registered to MNI space (2mm) is expected to be around 100.
# You can do this when you load each volume at the Dataset class, including padding backgrounds to fill dimensions under 96.
background = data==0
if scaling_method == 'z-norm':
global_mean = data[~background].mean()
global_std = data[~background].std()
data_temp = (data - global_mean) / global_std
elif scaling_method == 'minmax':
data_temp = (data - data[~background].min()) / (data[~background].max() - data[~background].min())
data_global = torch.empty(data.shape) # 假设后续代码需要torch.empty
data_global[background] = data_temp[~background].min() if not fill_zeroback else 0
# data_temp[~background].min() is expected to be 0 for scaling_method == 'minmax', and minimum z-value for scaling_method == 'z-norm'
data_global[~background] = data_temp[~background]
# save volumes one-by-one in fp16 format.
data_global = data_global.type(torch.float16)
data_global_split = torch.split(data_global, 1, 3)
for i, TR in enumerate(data_global_split):
torch.save(TR.clone(), os.path.join(save_dir,"frame_"+str(i)+".pt"))
def main():
# change two lines below according to your dataset
dataset_name = 'ABCD'
load_root = 'F:\New folder\cn_processed data' # This folder should have fMRI files in nifti format with subject names. Ex) sub-01.nii.gz
save_root = f'/storage/7.{dataset_name}_MNI_to_TRs_minmax'
scaling_method = 'z-norm' # choose either 'z-norm'(default) or 'minmax'.
# make result folders
filenames = os.listdir(load_root)
os.makedirs(os.path.join(save_root,'img'), exist_ok = True)
os.makedirs(os.path.join(save_root,'metadata'), exist_ok = True) # locate your metadata file at this folder
save_root = os.path.join(save_root,'img')
finished_samples = os.listdir(save_root)
queue = Queue()
count = 0
for filename in sorted(filenames):
# 确保filename包含完整的文件路径
subj_name = filename # extract subject name from nifti file. [:-7] rules out '.nii.gz'
# we recommend you use subj_name that aligns with the subject key in a metadata file.
expected_seq_length = 1000 # Specify the expected sequence length of fMRI for the case your preprocessing stopped unexpectedly and you try to resume the preprocessing.
# change the line below according to your folder structure
if (subj_name not in finished_samples) or (len(os.listdir(os.path.join(save_root,subj_name))) < expected_seq_length): # preprocess if the subject folder does not exist, or the number of pth files is lower than expected sequence length.
try:
count+=1
p = Process(target=read_data, args=(filename,load_root,save_root,subj_name,count,queue,scaling_method))
p.start()
if count % 32 == 0: # requires more than 32 cpu cores for parallel processing
p.join()
except Exception:
print('encountered problem with'+filename)
print(Exception)
if __name__=='__main__':
start_time = time.time()
main()
end_time = time.time()
print('
Total', round((end_time - start_time) / 60), 'minutes elapsed.')关键修改：
- 导入Nilearn: 在read_data函数中导入nilearn.image.load_img。
- 替换加载方式: 使用nifti_image = load_img(path)加载图像，然后使用data = nifti_image.get_fdata()获取数据。
- 异常处理: 添加了更详细的异常处理，以便更好地诊断加载NIfTI文件时可能出现的问题。
- 修改main函数 修改load_root指向到包含子文件夹的根目录，并且修改filename的获取方式，确保传递给read_data的filename是包含子文件夹的完整路径。
注意事项：
- 确保Nilearn已正确安装。
- 检查文件路径是否正确，并且NIfTI文件存在。
- 根据实际情况调整数据处理步骤，例如数据裁剪和标准化。
- 根据硬件资源调整多进程的数量。
总结
本文档介绍了如何使用Nilearn库加载和处理NIfTI格式的fMRI数据，并将其集成到现有的多进程代码中。通过使用Nilearn，可以更方便地加载和处理神经影像数据，从而简化fMRI数据分析的流程。同时，合理的多进程处理可以加速数据处理过程，提高效率。
