如何在 python 中获得 dicom 切片的 3D 重建？

Question

我有一个包含 .dcm 个单次扫描文件的目录。我能够获得 2D 视图（我与其他 dicom 查看器进行了交叉检查）。但我无法让 3D 视图正常工作。我尝试使用 vtkDICOMImageReader class 但它无法读取文件。所以我尝试从 3D numpy 数组中获取一个 Volume 对象以使用 vtkplotter 显示它。出来的观点显然是错误的。我认为 3D 数组需要一些处理。

import time
import glob
import pydicom
import numpy as np
from vtkplotter import Volume
import sys, os

def main(folderPath):
    st = time.time()
    my_glob = glob.glob1(folderPath, "*")
    numFiles = 0
    rejected = 0

    # return if empty directory
    if len(my_glob) == 0:
        return False

    # get all readable dicom files in  array
    tem = []
    for file in list(my_glob):
        try:
            data_item = pydicom.dcmread(os.path.join(folderPath, file))
            if hasattr(data_item, 'SliceLocation'):
                tem.append(data_item)
                numFiles += 1
            else:
                rejected += 1
                print(file)
        except Exception as e:
            pass
    print("read done %s | %d files | %d rejected" % (time.time() - st, numFiles, rejected))
    if len(tem) <= 0:
        return False

    tem.sort(key=lambda x: x.InstanceNumber)

    # make 3d np array from all slices
    unset = True
    for i in range(len(tem)):
        arr = tem[i].pixel_array.astype(np.float32)
        if unset:
            imShape = (arr.shape[0], arr.shape[1], len(tem))
            scaledIm = np.zeros(imShape)
            pix_spacing = tem[i].PixelSpacing
            dist = 0
            for j in range(2):
                cs = [float(q) for q in tem[j].ImageOrientationPatient]
                ipp = [float(q) for q in tem[j].ImagePositionPatient]
                parity = pow(-1, j)
                dist += parity*(cs[1]*cs[5] - cs[2]*cs[4])*ipp[0]
                dist += parity*(cs[2]*cs[3] - cs[0]*cs[5])*ipp[1]
                dist += parity*(cs[0]*cs[4] - cs[1]*cs[3])*ipp[2]
            z_spacing = abs(dist)
            slope = tem[i].RescaleSlope
            intercept = tem[i].RescaleIntercept
            unset = False
        scaledIm[:, :, i] = arr

    # convert to hounsfield units
    scaledIm = slope*scaledIm + intercept
    pix_spacing.append(z_spacing)

    wl = 300    # window parameters for Angio
    ww = 600

    windowed = np.zeros(imShape, dtype=np.uint8)
    # allImages[scaledIm <= (wl-0.5-(ww-1)/2.0)] = 0
    k = np.logical_and(scaledIm > (wl-0.5-(ww-1)/2.0), scaledIm <= (wl-0.5+(ww-1)/2.0))
    windowed[k] = ((scaledIm[k] - (wl-0.5))/(ww-1)+0.5)*255
    windowed[scaledIm > (wl-0.5+(ww-1)/2.0)] = 255
    # windowed image (in 2D) is correct i checked visually in other DICOM viewers
    print("arrays made %s" % (time.time() - st))


    # Volume(scaledIm, spacing=pix_spacing).show(bg="black")
    Volume(windowed, spacing=pix_spacing).show(bg="black")

    # X, Y, Z = np.mgrid[:30, :30, :30]
    # scalar_field = ((X-15)**2 + (Y-15)**2 + (Z-15)**2)/225
    # Volume(scalar_field, spacing=pix_spacing).show(bg="black")      # looks good on this example


if __name__ == '__main__':
    folder = sys.argv[1]
    main(folder)

需要做什么才能获得在其他 dicom 查看器中显示的正确 3D 视图？

Answer 1

我无法重现问题，因为我从 pydicom 收到一条错误消息：

AttributeError: 'FileDataset' object has no attribute 'RescaleSlope'

无论如何你可以尝试以下方法：

更新到最新提交 pip install git+https://github.com/marcomusy/vtkplotter.git
将您的 Volume 实例化修改为：

    # NOTE that pix_spacing[0] and pix_spacing[2] might be inverted
    vol = Volume(windowed, spacing=pix_spacing)
    vol.permuteAxes(2,1,0).mirror("y")
    vol.show(bg="black")

您还可以查看 this example。

另一种可能性是直接加载带有load(mydicomdir, threshold=False)的目录，这个returns一个Volume.
欢迎在 https://github.com/marcomusy/vtkplotter/issues

如何在 python 中获得 dicom 切片的 3D 重建？

How to get 3D reconstruction of dicom slices in python?

3d

vtk

dicom

pydicom