从numpy数组创建pydicom文件 [英] Create pydicom file from numpy array

问题描述

我正在尝试从标准大小(512 x 512或256 x 256)的numpy数组创建mew dicom图像.看来这应该很简单，我已经从 http://code.google.com/p/pydicom/source/browse/source/dicom/examples/write_new.py ，它似乎执行相同的过程，但是当我保存文件，我无法在RadiAnt或MicroDicom中查看它.

I'm trying to create a mew dicom image from a standard-sized (512 x 512 or 256 x 256) numpy array. It seems like this should be straightforward, and I've adapted my code from http://code.google.com/p/pydicom/source/browse/source/dicom/examples/write_new.py, which appears to execute the same process, but when I save the file, I can't view it either in RadiAnt or MicroDicom.

import dicom, dicom.UID
from dicom.dataset import Dataset, FileDataset

def write_dicom(pixel_array,filename):

    file_meta = Dataset()
    ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
    ds.PixelData = pixel_array.tostring()
    ds.save_as(filename)
    return

if __name__ == "__main__":
    import numpy as np
    pixel_array = np.tile(np.arange(256).reshape(16,16), (16,16)) * 4
    write_dicom(pixel_array,'pretty.dcm')

推荐答案

上面的示例可以工作，但是导致许多工具抱怨DICOM，甚至不能使用itk/SimpleITK作为堆栈来读取它们.我发现从numpy制作DICOM的最好方法是使用SimpleITK工具并逐片生成DICOM.一个基本示例( https://github.com/SimpleITK/blob/8e94451e4c0e90bcc6a1ffdd7bc3d56c81f58d80/Examples/DicomSeriesReadModifyWrite/DicomSeriesReadModifySeriesWrite.py )展示了如何在堆栈中加载，执行转换然后重新保存文件，但这可以通过使用

The above example works but causes many tools to complain about the DICOMs and they cannot even be read at all using itk/SimpleITK as a stack. The best way I have found for making DICOMs from numpy is by using the SimpleITK tools and generating the DICOMs slice-by-slice. A basic example (https://github.com/zivy/SimpleITK/blob/8e94451e4c0e90bcc6a1ffdd7bc3d56c81f58d80/Examples/DicomSeriesReadModifyWrite/DicomSeriesReadModifySeriesWrite.py) shows how to load in a stack, perform a transformation and then resave the files, but this can easily be modified by using the

import SimpleITK as sitk
filtered_image = sitk.GetImageFromArray(my_numpy_array)

最终在输出图像中使用的标签数量很大，因此手动创建所有标签都很乏味.另外，SimpleITK支持8、16、32位图像以及RGB，因此比在pydicom中制作图像要容易得多.

The number of tags ultimately in output image is quite large and so manually creating all of them is tedious. Additionally SimpleITK supports 8, 16, 32-bit images as well as RGB so it is much easier than making them in pydicom.

(0008, 0008) Image Type                          CS: ['DERIVED', 'SECONDARY']
(0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage
(0008, 0018) SOP Instance UID                    UI: 1.2.826.0.1.3680043.2.1125.1.35596048796922805578234000521866725
(0008, 0020) Study Date                          DA: '20170803'
(0008, 0021) Series Date                         DA: '20170803'
(0008, 0023) Content Date                        DA: 0
(0008, 0030) Study Time                          TM: '080429.171808'
(0008, 0031) Series Time                         TM: '080429'
(0008, 0033) Content Time                        TM: 0
(0008, 0050) Accession Number                    SH: ''
(0008, 0060) Modality                            CS: 'OT'
(0008, 0064) Conversion Type                     CS: 'WSD'
(0008, 0090) Referring Physician's Name          PN: ''
(0010, 0010) Patient's Name                      PN: ''
(0010, 0020) Patient ID                          LO: ''
(0010, 0030) Patient's Birth Date                DA: ''
(0010, 0040) Patient's Sex                       CS: ''
(0018, 2010) Nominal Scanned Pixel Spacing       DS: ['1', '3']
(0020, 000d) Study Instance UID                  UI: 1.2.826.0.1.3680043.2.1125.1.33389357207068897066210100430826006
(0020, 000e) Series Instance UID                 UI: 1.2.826.0.1.3680043.2.1125.1.51488923827429438625199681257282809
(0020, 0010) Study ID                            SH: ''
(0020, 0011) Series Number                       IS: ''
(0020, 0013) Instance Number                     IS: ''
(0020, 0020) Patient Orientation                 CS: ''
(0020, 0052) Frame of Reference UID              UI: 1.2.826.0.1.3680043.2.1125.1.35696880630664441938326682384062489
(0028, 0002) Samples per Pixel                   US: 1
(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'
(0028, 0010) Rows                                US: 40
(0028, 0011) Columns                             US: 50
(0028, 0100) Bits Allocated                      US: 32
(0028, 0101) Bits Stored                         US: 32
(0028, 0102) High Bit                            US: 31
(0028, 0103) Pixel Representation                US: 1
(0028, 1052) Rescale Intercept                   DS: "0"
(0028, 1053) Rescale Slope                       DS: "1"
(0028, 1054) Rescale Type                        LO: 'US'
(7fe0, 0010) Pixel Data                          OW: Array of 8000 bytes

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