从numpy数组创建pydicom文件

Question

我正在尝试从标准尺寸(512 x 512或256 x 256)的numpy阵列创建一个新的dicom图像.看起来这应该是直截了当的,我已经调整了我的代码来自http://code.google.com/p/pydicom/source/browse/source/dicom/examples/write_new.py,它似乎执行相同的进程,但是当我保存文件时,我无法在RadiAnt或MicroDicom中查看它.

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')

Answer 1

2020 年更新:)

这些答案都不适合我。这就是我最终保存有效的单色 16bpp MR 切片的结果，该切片至少在 Slicer、Radiant 和 MicroDicom 中正确显示：

import pydicom
from pydicom.dataset import Dataset, FileDataset
from pydicom.uid import ExplicitVRLittleEndian
import pydicom._storage_sopclass_uids

image2d = image2d.astype(np.uint16)

print("Setting file meta information...")
# Populate required values for file meta information

meta = pydicom.Dataset()
meta.MediaStorageSOPClassUID = pydicom._storage_sopclass_uids.MRImageStorage
meta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian  

ds = Dataset()
ds.file_meta = meta

ds.is_little_endian = True
ds.is_implicit_VR = False

ds.SOPClassUID = pydicom._storage_sopclass_uids.MRImageStorage
ds.PatientName = "Test^Firstname"
ds.PatientID = "123456"

ds.Modality = "MR"
ds.SeriesInstanceUID = pydicom.uid.generate_uid()
ds.StudyInstanceUID = pydicom.uid.generate_uid()
ds.FrameOfReferenceUID = pydicom.uid.generate_uid()

ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SamplesPerPixel = 1
ds.HighBit = 15

ds.ImagesInAcquisition = "1"

ds.Rows = image2d.shape[0]
ds.Columns = image2d.shape[1]
ds.InstanceNumber = 1

ds.ImagePositionPatient = r"0\0\1"
ds.ImageOrientationPatient = r"1\0\0\0\-1\0"
ds.ImageType = r"ORIGINAL\PRIMARY\AXIAL"

ds.RescaleIntercept = "0"
ds.RescaleSlope = "1"
ds.PixelSpacing = r"1\1"
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 1

pydicom.dataset.validate_file_meta(ds.file_meta, enforce_standard=True)

print("Setting pixel data...")
ds.PixelData = image2d.tobytes()

ds.save_as(r"out.dcm")

请注意以下事项：

• 按照 PyDicom 文档的建议检查 FileDataset 构造函数未能为我创建有效的标头
• validate_file_meta 将为您在标头中创建一些缺失的元素（版本）
• 您需要指定字节序和显式/隐式 VR 两次：/
• 此方法将允许您创建一个有效的体积，并且只要您相应地更新每个切片的 ImagePositionPatient 和 InstanceNumber
• 确保您的 numpy 数组转换为与 BitsStored 具有相同位数的数据格式

Answer 2

这是我需要编写的代码的功能版本。它将从给定的 2D 像素数组写入 16 位灰度 DICOM 图像。根据 DICOM 标准，每个图像和系列的 UID 应该是唯一的，此代码不担心这一点，因为我不知道 UID 实际做什么。如果其他人这样做，我很乐意将其添加进来。

import dicom, dicom.UID
from dicom.dataset import Dataset, FileDataset
import numpy as np
import datetime, time

def write_dicom(pixel_array,filename):
    """
    INPUTS:
    pixel_array: 2D numpy ndarray.  If pixel_array is larger than 2D, errors.
    filename: string name for the output file.
    """

    ## This code block was taken from the output of a MATLAB secondary
    ## capture.  I do not know what the long dotted UIDs mean, but
    ## this code works.
    file_meta = Dataset()
    file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
    file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
    file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
    ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
    ds.Modality = 'WSD'
    ds.ContentDate = str(datetime.date.today()).replace('-','')
    ds.ContentTime = str(time.time()) #milliseconds since the epoch
    ds.StudyInstanceUID =  '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
    ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
    ds.SOPInstanceUID =    '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
    ds.SOPClassUID = 'Secondary Capture Image Storage'
    ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'

    ## These are the necessary imaging components of the FileDataset object.
    ds.SamplesPerPixel = 1
    ds.PhotometricInterpretation = "MONOCHROME2"
    ds.PixelRepresentation = 0
    ds.HighBit = 15
    ds.BitsStored = 16
    ds.BitsAllocated = 16
    ds.SmallestImagePixelValue = '\\x00\\x00'
    ds.LargestImagePixelValue = '\\xff\\xff'
    ds.Columns = pixel_array.shape[0]
    ds.Rows = pixel_array.shape[1]
    if pixel_array.dtype != np.uint16:
        pixel_array = pixel_array.astype(np.uint16)
    ds.PixelData = pixel_array.tostring()

    ds.save_as(filename)
    return



if __name__ == "__main__":
#    pixel_array = np.arange(256*256).reshape(256,256)
#    pixel_array = np.tile(np.arange(256).reshape(16,16),(16,16))
    x = np.arange(16).reshape(16,1)
    pixel_array = (x + x.T) * 32
    pixel_array = np.tile(pixel_array,(16,16))
    write_dicom(pixel_array,'pretty.dcm')

Answer 3

我能够进一步减少@Corvin 的精彩答案。这是一个极简代码示例，允许将（虚拟）3D numpy 数组保存到可以使用Amide打开的有效 DICOM 图像：

#!/usr/bin/python3
import numpy
import pydicom
import pydicom._storage_sopclass_uids

# dummy image
image = numpy.random.randint(2**16, size=(512, 512, 512), dtype=numpy.uint16)

# metadata
fileMeta = pydicom.Dataset()
fileMeta.MediaStorageSOPClassUID = pydicom._storage_sopclass_uids.CTImageStorage
fileMeta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
fileMeta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian

# dataset
ds = pydicom.Dataset()
ds.file_meta = fileMeta

ds.Rows = image.shape[0]
ds.Columns = image.shape[1]
ds.NumberOfFrames = image.shape[2]

ds.PixelSpacing = [1, 1] # in mm
ds.SliceThickness = 1 # in mm

ds.BitsAllocated = 16
ds.PixelRepresentation = 1
ds.PixelData = image.tobytes()

# save
ds.save_as('image.dcm', write_like_original=False)

image.dcm正如人们可能观察到的那样，如果将输出文件传递给dciodvfy，则会丢失许多字段。这些字段的填写留给读者;)