def nii2dicom(ni, ds):
if len(ni.shape) == 3:
I, J, K = ni.shape
T = 1
elif len(ni.shape) == 4:
I, J, K, T = ni.shape
else:
raise (Exception(f"tried to convert nifti with shape {ni.shape}"))
orientation = ni.affine
orientation33 = orientation[:3, :3]
image_position0 = orientation[:3, 3]
pixel_spacing = list(
map(np.linalg.norm, [orientation[:3, 0], orientation[:3, 1]]))
slice_spacing = np.linalg.norm(orientation[:3, 2])
slice_thickness = ni.header["pixdim"][3]
orientation_rescaled = orientation33 @ np.diag(
[1 / pixel_spacing[0], 1 / pixel_spacing[1], 1 / slice_spacing])
orientation_dcm = np.reshape(orientation_rescaled[:2, :], 6)
slice_vector = np.cross(orientation_rescaled[0, :],
orientation_rescaled[1, :])
scl_slope = 1 if np.isnan(
ni.header["scl_slope"]) else ni.header["scl_slope"]
scl_inter = 0 if np.isnan(
ni.header["scl_inter"]) else ni.header["scl_inter"]
raw = ni.get_fdata()
minvol = np.min(raw)
maxvol = np.max(raw)
raw -= minvol
scl_inter += minvol * scl_slope
raw *= 65536 / (maxvol - minvol)
scl_slope *= (maxvol - minvol) / 65536
minval = scl_slope * np.min(raw) + scl_inter
maxval = scl_slope * np.max(raw) + scl_inter
uintVOL = np.uint16(np.round(raw))
out_dcms = []
for k in range(K):
for t in range(T):
d = Dataset()
for elem in ds:
if elem.tag.group in [0x8, 0x10, 0x18, 0x20, 0x28, 0x32, 0x40]:
d[elem.tag] = deepcopy(elem)
fix_meta_info(d)
d.SOPInstanceUID = generate_uid(entropy_srcs=[
d.SeriesInstanceUID, d.SeriesDescription,
str(k),
str(t),
str(uintVOL[:2, :2, k])
])
d.SeriesInstanceUID = generate_uid(
entropy_srcs=[d.SeriesInstanceUID, d.SeriesDescription])
d.PixelSpacing = pixel_spacing
d.SeriesNumber += 1000
d.SpacingBetweenSlices = slice_spacing
d.SliceThickness = slice_thickness
d.ImageOrientationPatient = list(orientation_dcm)
d.ImagePositionPatient = list(image_position0 + (k - 1) *
slice_vector * slice_spacing)
d.SliceLocation = np.dot(d.ImagePositionPatient, slice_vector)
d.Rows = J
d.Columns = I
d.InstanceNumber = k
d.AcquisitionNumber = t
if len(ni.shape) == 3:
d.PixelData = np.flip(uintVOL[:, :,
k]).T.copy(order='C').tobytes()
else:
d.PixelData = np.flip(uintVOL[:, :, k,
t]).T.copy(order='C').tobytes()
#d.Pixel Data = transpose(uintVOL[end:-1:1,end:-1:1,k,t]) # flip from RAS to LPS
d.BitsAllocated = 16
d.BitsStored = 16
d.HighBit = 15
d.PixelRepresentation = 0
d.WindowCenter = (maxval + minval) / 2
d.WindowWidth = (maxval - minval)
d.WindowCenterWidthExplanation = "LINEAR"
d.RescaleIntercept = scl_inter
d.RescaleSlope = scl_slope
fix_meta_info(d)
out_dcms.append(d)
return out_dcms
def make_dataset(query_level='series', **kwargs):
if query_level == 'study':
d = Dataset()
d.QueryRetrieveLevel = 'STUDY'
d.StudyDate = ''
#Required
d.StudyInstanceUID = ''
#Optional
d.PatientName = ''
d.PatientID = ''
d.AccessionNumber = ''
d.StudyDescription = ''
d.StudyID = ''
d.StudyTime = ''
d.StudyInstanceUID = ''
d.NumberOfStudyRelatedSeries = ''
d.StationName = ''
d.SpecificCharacterSet = ''
d.ModalitiesInStudy = ''
d.Manufacturer = ''
d.ManufacturerModelName = ''
d.InstitutionName = ''
if query_level == 'series':
d = Dataset()
d.QueryRetrieveLevel = 'SERIES'
d.SeriesDescription = ''
d.SeriesNumber = ''
d.SeriesInstanceUID = ''
d.Modality = ''
d.NumberOfSeriesRelatedInstances = ''
d.StationName = ''
d.SpecificCharacterSet = ''
d.SeriesDate = ''
d.AccessionNumber = ''
d.SeriesTime = ''
d.Manufacturer = ''
d.ManufacturerModelName = ''
d.SOPClassUID = ''
if query_level == 'image':
d = Dataset()
d.QueryRetrieveLevel = "IMAGE"
#Required
d.SeriesInstanceUID = ''
#Optional
d.StudyInstanceUID = ''
d.SOPInstanceUID = ''
d.AccessionNumber = ''
d.StationName = ''
d.SeriesDate = ''
d.SeriesTime = ''
d.SOPClassUID = ''
d.InstanceNumber = ''
d.SpecificCharacterSet = ''
d.StudyInstanceUID = ''
if query_level == 'patient': #todo find optional tags
d = Dataset()
d.QueryRetrieveLevel = 'PATIENT'
d.PatientName = ''
d.PatientSex = ''
d.PatientID = ''
d.AccessionNumber = ''
for k, v in kwargs.items():
try:
d = write_tag_by_keyword(d, k, v)
except:
print(f'could not write tag: "{k}" with value: {v}')
return d
