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gen_dataset.py
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gen_dataset.py
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import numpy as np
import SimpleITK as sitk
import pydicom
import pydicom.dataset
import os
import struct
import utils
import scipy.interpolate
import matplotlib.pyplot as plt
def normalize(images, mAs_array, kV_array, percent_gain):
print("normalize images")
kVs = {}
kVs[40] = (20.4125, 677.964)
kVs[50] = (61.6163, 686.4824)
kVs[60] = (138.4021, 684.1844)
kVs[70] = (250.8008, 691.9573)
kVs[80] = (398.963, 701.1038)
kVs[90] = (586.5949, 711.416)
kVs[100] = (794.5124, 729.8813)
kVs[109] = (1006.1, 750.0054)
kVs[120] = (1252.2, 791.9865)
kVs[125] = (1404.2202, 796.101)
#f, gamma = i0.get_i0(prefix + r"\70kVp")
#kVs[70] = f
fs = []
gain = 3
#gain = 1
for mAs, kV in zip(mAs_array, kV_array):
if kV in kVs:
f = np.polyval(kVs[kV], mAs)
else:
kVs_keys = np.array(list(sorted(kVs.keys())))
if kV < kVs_keys[0]:
f = np.polyval(kVs[kVs_keys[0]], mAs)
elif kV > kVs_keys[-1]:
f = np.polyval(kVs[kVs_keys[-1]], mAs)
else:
i1, i2 = np.argsort(np.abs(kVs_keys-kV))[:2]
f1 = np.polyval(kVs[kVs_keys[i1]], mAs)
f2 = np.polyval(kVs[kVs_keys[i2]], mAs)
d1 = np.abs(kVs_keys[i1]-kV)*1.0
d2 = np.abs(kVs_keys[i2]-kV)*1.0
f = f1*(1.0-(d1/(d1+d2))) + f2*(1.0-(d2/(d1+d2)))
fs.append(f)
fs = np.array(fs).flatten()
skip = 4
if images.shape[1] < 1000:
skip = 2
#if images.shape[1] < 600:
skip = 1
edges = 1
sel = np.zeros(images.shape, dtype=bool)
#sel[:,20*skip:-20*skip:skip,20*skip:-20*skip:skip] = True
#norm_images_gained = np.zeros((images.shape[0],images[0,20*skip:-20*skip:skip].shape[0], int(np.count_nonzero(sel)/images.shape[0]/(images[0,20*skip:-20*skip:skip].shape[0]))), dtype=np.float32)
#norm_images_ungained = np.zeros((images.shape[0],images[0,20*skip:-20*skip:skip].shape[0], int(np.count_nonzero(sel)/images.shape[0]/(images[0,20*skip:-20*skip:skip].shape[0]))), dtype=np.float32)
sel[:,edges:-edges:skip,edges:-edges:skip] = True
sel_shape = (images.shape[0],images[0,edges:-edges:skip].shape[0], int(np.count_nonzero(sel)/images.shape[0]/(images[0,edges:-edges:skip].shape[0])))
norm_images_gained = np.zeros(sel_shape, dtype=np.float32)
norm_images_ungained = np.zeros(sel_shape, dtype=np.float32)
gained_images = np.array([image*(1+gain/100) for image,gain in zip(images[sel].reshape(sel_shape), percent_gain)])
for i in range(len(fs)):
norm_img = gained_images[i].reshape(norm_images_gained[0].shape)
norm_images_gained[i] = norm_img
norm_img = images[i][sel[i]].reshape(norm_images_gained[0].shape)
norm_images_ungained[i] = norm_img
#gain = 2.30
gain = 1
offset = 400
use = fs>1
while (np.max(norm_images_gained, axis=(1,2))[use] > (offset+gain*fs[use])).all():
gain += 1
#gain = np.ones_like(fs)
#offset = 0
for i in range(len(fs)):
norm_img = norm_images_gained[i] / (offset + (gain*fs)[i])
#norm_img = norm_images_gained[i] / (1.1*np.max(norm_images_gained[i]))
if (norm_img==0).any():
norm_img[norm_img==0] = np.min(norm_img[norm_img!=0])
norm_images_gained[i] = -np.log(norm_img)
return norm_images_gained, norm_images_ungained, offset+gain*fs, fs
def read_dicoms(indir, max_ims=np.inf):
print("read dicoms")
kvs = []
mas = []
μas = []
ts = []
thetas = []
phis = []
prims = []
secs = []
ims = []
percent_gain = []
coord_systems = []
sids = []
sods = []
#sid = []
for root, _dirs, files in os.walk(indir):
for entry in files:
path = os.path.abspath(os.path.join(root, entry))
#read DICOM files
ds = pydicom.dcmread(path)
if "PositionerPrimaryAngleIncrement" in dir(ds):
ims = ds.pixel_array
ts = list(range(len(ims)))
thetas = np.array(ds.PositionerPrimaryAngleIncrement)
phis = np.array(ds.PositionerSecondaryAngleIncrement)
if ds[0x0021,0x1059].VR == "FL":
cs = np.array(ds[0x0021,0x1059].value).reshape((len(ts), 3, 4))
coord_systems = np.array(ds[0x0021,0x1059].value).reshape((len(ts), 3, 4))
else:
cs = np.array(list(struct.iter_unpack("<f", ds[0x0021,0x1059].value))).reshape((len(ts), 3, 4))
coord_systems = np.array(list(struct.iter_unpack("<f", ds[0x0021,0x1059].value))).reshape((len(ts), 3, 4))
if ds[0x0021,0x1031].VR == "SS":
sids = np.array(ds[0x0021,0x1031].value)*0.1
else:
sids = np.array(list(struct.iter_unpack("<h", ds[0x0021,0x1031].value))).flatten()*0.1
#if ds[0x0021,0x1017].VR == "SL":
# sods = [np.array(ds[0x0021,0x1017].value)]*len(ts)
#else:
# sods = [np.array(int.from_bytes(ds[0x0021,0x1017].value, "little", signed=True))]*len(ts)
stparmdata = utils.unpack_sh_stparm(ds[0x0021,0x1012].value)
sods = [stparmdata["SOD_A"]] *len(ts)
if ds[0x0019,0x1008].VR == "US":
percent_gain = [ds[0x0019,0x1008].value] * len(ts)
else:
percent_gain = [float(int.from_bytes(ds[0x0019,0x1008].value, "little", signed=False))] * len(ts)
#sid = ds[0x0021,0x1031].value
if ds[0x0021,0x100f].VR =="SL":
xray_info = np.array(ds[0x0021,0x100F].value)
else:
xray_info = np.array(list(struct.iter_unpack("<l",ds[0x0021,0x100f].value))).flatten()
kvs = xray_info[0::4]
mas = xray_info[1::4]*xray_info[2::4]*0.001
μas = xray_info[2::4]*0.001
elif "NumberOfFrames" in dir(ds):
ims = ds.pixel_array
for i in range(int(ds.NumberOfFrames)):
ts.append(len(ts))
kvs.append(float(ds.KVP))
mas.append(float(ds.XRayTubeCurrent)*float(ds.ExposureTime)*0.001)
if ds[0x0021,0x1004].VR == "SL":
μas.append(float(ds[0x0021,0x1004].value)*0.001)
else:
μas.append(struct.unpack("<l", ds[0x0021,0x1004].value)[0]*0.001)
thetas.append(float(ds.PositionerPrimaryAngle))
phis.append(float(ds.PositionerSecondaryAngle))
stparmdata = utils.unpack_sh_stparm(ds[0x0021,0x1012].value)
cs = np.array(stparmdata["COORD_SYS_C_ARM"]).reshape((3, 4))
cs[:,2] = utils.rotMat(i*360/ims.shape[0], cs[:,0]).dot(cs[:,2])
cs[:,1] = utils.rotMat(i*360/ims.shape[0], cs[:,0]).dot(cs[:,1])
coord_systems.append(cs)
sids.append(np.array(stparmdata["SID_A"]))
sods.append(np.array(stparmdata["SOD_A"]))
rv = cs[:,2]
rv /= np.sum(rv**2, axis=-1)
prim = np.arctan2(np.sqrt(rv[0]**2+rv[1]**2), rv[2])
prim = np.arccos(rv[2] / np.sqrt(rv[0]**2+rv[1]**2+rv[2]**2) )
prim = prim*180 / np.pi
if cs[1,2]<0:
prim *= -1
if (prim > 50 and prim < 135) or (prim <-45 and prim > -135):
sec = np.arctan2(rv[1], rv[0])
sec = sec * 180 / np.pi
if cs[1,2]<0:
sec *= -1
sec -= 90
else:
sec = np.arctan2(rv[2], rv[0])
sec = sec * 180 / np.pi - 90
prims.append(prim)
secs.append(sec)
if ds[0x0019,0x1008].VR == "US":
percent_gain.append(ds[0x0019,0x1008].value)
else:
percent_gain.append(float(int.from_bytes(ds[0x0019,0x1008].value, "little", signed=False)))
elif "PositionerPrimaryAngle" in dir(ds):
ts.append(len(ts))
kvs.append(float(ds.KVP))
mas.append(float(ds.XRayTubeCurrent)*float(ds.ExposureTime)*0.001)
if ds[0x0021,0x1004].VR == "SL":
μas.append(float(ds[0x0021,0x1004].value)*0.001)
else:
μas.append(struct.unpack("<l", ds[0x0021,0x1004].value)[0]*0.001)
thetas.append(float(ds.PositionerPrimaryAngle))
phis.append(float(ds.PositionerSecondaryAngle))
ims.append(ds.pixel_array)
stparmdata = utils.unpack_sh_stparm(ds[0x0021,0x1012].value)
cs = np.array(stparmdata["COORD_SYS_C_ARM"]).reshape((3, 4))
coord_systems.append(cs)
sids.append(np.array(stparmdata["SID_A"]))
sods.append(np.array(stparmdata["SOD_A"]))
rv = cs[:,2]
rv /= np.sum(rv**2, axis=-1)
prim = np.arctan2(np.sqrt(rv[0]**2+rv[1]**2), rv[2])
prim = np.arccos(rv[2] / np.sqrt(rv[0]**2+rv[1]**2+rv[2]**2) )
prim = prim*180 / np.pi
if cs[1,2]<0:
prim *= -1
if (prim > 50 and prim < 135) or (prim <-45 and prim > -135):
sec = np.arctan2(rv[1], rv[0])
sec = sec * 180 / np.pi
if cs[1,2]<0:
sec *= -1
sec -= 90
else:
sec = np.arctan2(rv[2], rv[0])
sec = sec * 180 / np.pi - 90
prims.append(prim)
secs.append(sec)
if ds[0x0019,0x1008].VR == "US":
percent_gain.append(ds[0x0019,0x1008].value)
else:
percent_gain.append(float(int.from_bytes(ds[0x0019,0x1008].value, "little", signed=False)))
del ds
if len(ts)>=max_ims:
break
if len(ts)>=max_ims:
break
print("create numpy arrays")
kvs = np.array(kvs)
mas = np.array(mas)
μas = np.array(μas)
percent_gain = np.array(percent_gain)
thetas = np.array(thetas)
phis = np.array(phis)
ims = np.array(ims)
coord_systems = np.array(coord_systems)
sids = np.array(sids)
sods = np.array(sods)
cs = coord_systems
rv = cs[:,:,2]
rv /= np.vstack((np.sum(rv, axis=-1),np.sum(rv, axis=-1),np.sum(rv, axis=-1))).T
prim = np.arctan2(np.sqrt(rv[:,0]**2+rv[:,1]**2), rv[:,2])
prim = np.arccos(rv[:,2] / np.sqrt(rv[:,0]**2+rv[:,1]**2+rv[:,2]**2) )
sec = 0.5*np.pi-np.arctan2(rv[:,1], rv[:,0])
prim = prim*180 / np.pi
sec = sec * 180 / np.pi
prim[cs[:,1,2]<0] *= -1
sec[cs[:,1,2]<0] -= 180
prim[np.bitwise_and(cs[:,2,2]<0, cs[:,1,1]>0) ] -= 180
prim[cs[:,1,0]>0] -= 180
#thetas = prim
#phis = sec
angles = np.vstack((thetas, phis, np.zeros_like(thetas))).T
ims_gained, ims_ungained, i0s_gained, i0s_ungained = normalize(ims, μas, kvs, percent_gain)
return ims_gained, ims_ungained, i0s_gained, i0s_ungained, angles, coord_systems, sids, sods
def add_noise(ims, angles):
random = np.random.default_rng(23)
#angles_noise = random.normal(loc=0, scale=0.5, size=(len(ims), 3))#*np.pi/180
angles_noise = random.uniform(low=-1, high=1, size=(len(ims),3))
#angles_noise = np.zeros_like(angles_noise)
#trans_noise = random.normal(loc=0, scale=20, size=(len(ims), 3))
min_trans, max_trans = -10, 10
trans_noise = np.array(np.round(random.uniform(low=min_trans, high=max_trans, size=(len(ims),2))), dtype=int)
zoom_noise = random.uniform(low=0.9, high=1, size=len(ims))
moved_ims = np.zeros((ims.shape[0], ims.shape[1]+min_trans-max_trans-2, ims.shape[2]-max_trans+min_trans-2), dtype=ims.dtype)
for i in range(ims.shape[0]):
dx, dy = trans_noise[i]
sx, ex = max_trans+1+dx, min_trans-1+dx
sy, ey = max_trans+1+dy, min_trans-1+dy
iy, ix = np.arange(0, ims.shape[2], 1), np.arange(0, ims.shape[1], 1)
f = scipy.interpolate.interp2d(iy, ix, ims[i], fill_value=np.max(ims[i]))
mid_x, mid_y = ims.shape[1]/2, ims.shape[2]/2
mid_x -= dx
mid_y -= dy
sx = mid_x-(ims.shape[1]/2) * zoom_noise[i]
ex = mid_x+(ims.shape[1]/2) * zoom_noise[i]
sy = mid_y-(ims.shape[2]/2) * zoom_noise[i]
ey = mid_y+(ims.shape[2]/2) * zoom_noise[i]
#print(i, (ex-sx) / (ey-sy), (len(ix)) / (len(iy)))
x = np.linspace(sx, ex, moved_ims.shape[1])
y = np.linspace(sy, ey, moved_ims.shape[2])
moved_ims[i] = f(y,x)
return moved_ims, angles + angles_noise
def get_path():
if os.path.exists("E:\\output"):
prefix = r"E:\output"
else:
prefix = r"D:\lumbal_spine_13.10.2020\output"
filepath = prefix + '\\CKM_LumbalSpine\\20201020-093446.875000\\20sDCT Head 70kV'
return filepath
def save_dcm(ims, angles, sids, sods, orig_path, out_path):
if not os.path.isdir(os.path.dirname(out_path)):
os.makedirs(os.path.dirname(out_path))
ds = pydicom.dcmread(os.path.join(orig_path, os.listdir(orig_path)[0]))
file_meta = ds.file_meta
# Create the FileDataset instance (initially no data elements, but file_meta
# supplied)
#ds = pydicom.dataset.FileDataset(out_path, {}, file_meta=file_meta, preamble=ds.preamble)
# Add the data elements -- not trying to set all required here. Check DICOM
# standard
#ds.PatientName = "Test^Firstname"
#ds.PatientID = "123456"
ds.PositionerPrimaryAngleIncrement = angles[:,0].tolist()
ds.PositionerSecondaryAngleIncrement = angles[:,1].tolist()
ds.Rows = ims.shape[1]
ds.Columns = ims.shape[2]
ds.add_new(0x00211017, 'SL', sods[0])
#ds.add_new(0x00211031, 'SS', np.array(sids*10, dtype=int))
#ds.DistanceSourceToDetector = sids[0]
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = False
# Set creation date/time
#dt = datetime.datetime.now()
#ds.ContentDate = dt.strftime('%Y%m%d')
#timeStr = dt.strftime('%H%M%S.%f') # long format with micro seconds
#ds.ContentTime = timeStr
pydicom.dataset.validate_file_meta(ds.file_meta, enforce_standard=True)
ims = ims.astype(np.uint16)
ds.PixelData = ims.flatten().tobytes()
#ds.add_new(0x7fe00009, "OD", ims.flatten())
ds.save_as(out_path, False)
def create():
ims_gained, ims_ungained, i0s_gained, i0s_ungained, angles, coord_systems, sids, sods = read_dicoms(get_path())
ims = ims_ungained[:, 10:-10, 10:-10]
noise_ims, noise_angles = add_noise(ims, angles)
if os.path.exists("E:\\output"):
prefix = r"E:\output"
else:
prefix = r"D:\lumbal_spine_13.10.2020\output"
save_dcm(noise_ims, np.round(noise_angles, 2), sids, sods, get_path(), prefix+'/gen_dataset/noisy/sino.dcm')
save_dcm(noise_ims, np.round(angles, 2), sids, sods, get_path(), prefix+'/gen_dataset/only_trans/sino.dcm')
save_dcm(ims, np.round(noise_angles, 2), sids, sods, get_path(), prefix+'/gen_dataset/only_angle/sino.dcm')
if __name__ == "__main__":
create()