使用pydicom从轴位视图提取矢状和冠状切面

Question

我正在尝试读取一系列.dcm文件，它们在默认情况下显示轴向视图。代码如下：

import os
import numpy as np
import pydicom as dicom
from matplotlib import pyplot as plt

root_dir = 'mydcomDir'


def sortDcm():
        print('Given Path to the .dcm directory is: {}'.format(root_dir))
        slices = [dicom.read_file(root_dir + '/' + s) for s in os.listdir(root_dir)]
        slices.sort(key = lambda x: float(x.ImagePositionPatient[2]))
        pos1 = slices[int(len(slices)/2)].ImagePositionPatient[2]
        pos2 = slices[(int(len(slices)/2)) + 1].ImagePositionPatient[2]
        diff = pos2 - pos1
#        if diff > 0:
#            slices = np.flipud(slices)
        try:
            slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])
        except:
            slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)

        for s in slices:
            s.SliceThickness = slice_thickness
#        print("from sorted dicom",len(slices))         
        return slices 


dcms = sortDcm()
ref_dicom = dcms[0]

d_array = np.zeros((ref_dicom.Columns,ref_dicom.Rows, len(dcms)), dtype=ref_dicom.pixel_array.dtype)

for dcm in dcms:
    d_array[:, :, dcms.index(dcm)] = dcm.pixel_array

#    fig = plt.figure(figsize=(12,12))
#    plt.subplot(1, 3, 1)
#    plt.title("Coronal")
#    plt.imshow(np.flipud(d_array[idx , :, :].T))
#    plt.subplot(1, 3, 2)
#    plt.title("Sagital")
#    plt.imshow(np.flipud(d_array[:, idy, :].T))
#    plt.subplot(1, 3, 3)
    plt.title("axial")
    plt.imshow(d_array[:, :, dcms.index(dcm)])
    plt.pause(0.001)

正如您从代码中看到的，我无法为特定的dcm文件计算出相关的idx和idy。 所以我的问题是，如何得到矢状和冠状切面，并在轴向切割的情况下绘制它们？在

提前谢谢。在

编辑： 正如@ColonelFazackerley完美的回答。我在下面的行添加只是为了显示我如何使用它。在

^{pr2}$

Answer 1

"""usage: reslice.py <glob>
where <glob> refers to a set of DICOM image files.

Example: python reslice.py "*.dcm". The quotes are needed to protect the glob
from your system and leave it for the script."""

import pydicom
import numpy as np
import matplotlib.pyplot as plt
import sys
import glob

# load the DICOM files
files=[]
print('glob: {}'.format(sys.argv[1]))
for fname in glob.glob(sys.argv[1], recursive=False):
    print("loading: {}".format(fname))
    files.append(pydicom.read_file(fname))

print("file count: {}".format(len(files)))

# skip files with no SliceLocation (eg scout views)
slices=[]
skipcount=0
for f in files:
    if hasattr(f, 'SliceLocation'):
        slices.append(f)
    else:
        skipcount = skipcount + 1

print("skipped, no SliceLocation: {}".format(skipcount))

# ensure they are in the correct order
slices = sorted(slices, key=lambda s: s.SliceLocation)

# pixel aspects, assuming all slices are the same
ps = slices[0].PixelSpacing
ss = slices[0].SliceThickness
ax_aspect = ps[1]/ps[0]
sag_aspect = ps[1]/ss
cor_aspect = ss/ps[0]

# create 3D array
img_shape = list(slices[0].pixel_array.shape)
img_shape.append(len(slices))
img3d=np.zeros(img_shape)

# fill 3D array with the images from the files
for i, s in enumerate(slices):
    img2d = s.pixel_array
    img3d[:,:,i] = img2d

# plot 3 orthogonal slices
a1 = plt.subplot(2,2,1)
plt.imshow(img3d[:,:,img_shape[2]//2])
a1.set_aspect(ax_aspect)

a2 = plt.subplot(2,2,2)
plt.imshow(img3d[:,img_shape[1]//2,:])
a2.set_aspect(sag_aspect)

a3 = plt.subplot(2,2,3)
plt.imshow(img3d[img_shape[0]//2,:,:].T)
a3.set_aspect(cor_aspect)

plt.show()

根据本例3D CT数据对序列2进行测试 http://www.pcir.org/researchers/54879843_20060101.html

编辑注释：作为pydicom项目的一个例子 https://github.com/pydicom/pydicom/blob/master/examples/image_processing/reslice.py