def update_wad(filename, mode, axorder, sporder, **kwargs):
#sphereac = 999, ctimespac = '120000', bgac = 999, bgactime = '120000', spremac = 999, spremactime = '120000', bgremac = 999, bgremactime = '120000',scantime='120000'
output = {}
#Float values
fmt = '%H%M%S' #assumed format for all times
output['Sphere activity'] = kwargs['sphereac']
output['Calibration time sphere'] = kwargs['ctimespac']
output['Background activity'] = kwargs['bgac']
output['Background act time'] = kwargs['bgactime']
output['Sphere rem act'] = kwargs['spremac']
output['Sphere rem act time'] = kwargs['spremactime']
output['Bg rem act'] = kwargs['bgremac']
output['Bg rem act time'] = kwargs['bgremactime']
''' With all the activities known we can simply calculate
the activity concentration of the spheres @ scantime and the activity concentration of the background @ scantime
'''
th = niqlib.thf18
# convert all 'string' times to datetime format so we can calculate something
scantime = datetime.strptime(kwargs['scantime'],fmt)
ctimespac = datetime.strptime(kwargs['ctimespac'],fmt)
bgactime = datetime.strptime(kwargs['bgactime'],fmt)
spremactime = datetime.strptime(kwargs['spremactime'],fmt)
bgremactime = datetime.strptime(kwargs['bgremactime'],fmt)
actspsctime = niqlib.decay(float(kwargs['sphereac']),float((scantime - ctimespac).seconds/60.),th) - niqlib.decay(float(kwargs['spremac']),float((scantime - spremactime).seconds/60.),th)
actbgsctime = niqlib.decay(float(kwargs['bgac']),float((scantime - bgactime).seconds/60.),th) - niqlib.decay(float(kwargs['bgremac']),float((scantime - bgremactime).seconds/60.),th)
output['Act Spheres (scantime)'] = actspsctime
output['Act BG (scantime)'] = actbgsctime
output['Scantime'] = scantime.time()
print axorder
print "1 - loading data"
iqphantom = simple_nema_phantom(data=filename,mode=mode,order=axorder,orientation=sporder)
print "z_slice bollen: ",iqphantom.z
zslicefig = plt.figure()
ax1 = zslicefig.add_subplot(111)
plt.xtitle = "Z slice"
plt.ytitle = "Counts in slice"
#plt.plot(iqphantom.z_slice())
#plt.show()
# images
output['EARL z_slice'] = iqphantom.z
output['EARL zprofile'] = zslicefig
print "2 - registering data"
print "COR and angle before registration:", iqphantom.cor, iqphantom.angle
#regpars = iqphantom.register_phantom()
#print "registration pars:", regpars[0], regpars[1]
# output['regpars'] = regpars
print "COR and angle after registration:", iqphantom.cor, iqphantom.angle
output['cor'] = iqphantom.cor
output['angle'] = iqphantom.angle
print "3 - show result: "
print "zslice", iqphantom.z
output['EARL halfslice'] = iqphantom.plot_dataspheres(iqphantom.z)
print "4 - sphere statistics: "
results = iqphantom.get_stats()
for key in results.keys():
print "Sphere: ", key
tmpresult = results[key]
for subkey in tmpresult.keys():
print '\t', subkey, tmpresult[subkey]
bgresults = iqphantom.get_bg_stats()
for key in bgresults.keys():
print "BG Sphere: ", key
tmpresult = bgresults[key]
for subkey in tmpresult.keys():
print '\t', subkey, tmpresult[subkey]
bgaverage = np.average([bgresults[key]['avg'] for key in bgresults.keys()])
print bgaverage
meancontrastlist = {}
maxcontrastlist = {}
for key in results.keys():
meancontrastlist[key] = (results[key]['avg'] - bgaverage)/bgaverage
maxcontrastlist[key] = (results[key]['max'] - bgaverage)/bgaverage
output['maxcontrast'] = maxcontrastlist
output['meancontrast'] = meancontrastlist
print "5 - contrasts: "
for key in meancontrastlist.keys():
print "mean:", key, meancontrastlist[key]
print "max:", key, maxcontrastlist[key]
print "6 - rc list: "
inputcontrast = (actspsctime - actbgsctime)/ actbgsctime
meanrclist = {}
maxrclist = {}
for key in results.keys():
meanrclist[key] = meancontrastlist[key]/inputcontrast
maxrclist[key] = maxcontrastlist[key]/inputcontrast
for key in meanrclist.keys():
print "mean: ",key, meanrclist[key]
print "max: ", key, maxrclist[key]
output['maxrc'] = maxrclist
output['meanrc'] = meanrclist
output['RC-1'] = meanrclist[1]
output['RC-2'] = meanrclist[2]
output['RC-3'] = meanrclist[3]
output['RC-4'] = meanrclist[4]
output['RC-5'] = meanrclist[5]
output['RC-6'] = meanrclist[6]
output['EARL rc mean curve'] = plot_rclist(meanrclist,'za')
output['EARL rc max curve'] = plot_rclist(maxrclist,'za')
output['status'] = 'processed'
print "Writing results to wadserver"
return output
def update_wad(filename, mode, axorder, sporder, **kwargs):
output = {}
#Float results
volume = kwargs['volume']
output['volume'] = volume
output['length'] = kwargs['dimensions'][0]
output['radius'] = kwargs['dimensions'][1]
output['activity'] = kwargs['bgac']
output['caltime'] = kwargs['bgactime']
output['remaindoer'] = kwargs['bgremac']
output['remtime'] = kwargs['bgremactime']
output['deltat0'] = 999999
output['deltatr'] = 999999
output['scanto'] = 999999
output['maxsuv'] =999999
output['avgsuv'] =999999
output['minsuv'] =999999
th = niqlib.thf18
fmt = '%H%M%S' #assumed format for all times
# convert all 'string' times to datetime format so we can calculate something
scantime = datetime.strptime(kwargs['scantime'],fmt)
ctimespac = datetime.strptime(kwargs['ctimespac'],fmt)
bgactime = datetime.strptime(kwargs['bgactime'],fmt)
spremactime = datetime.strptime(kwargs['spremactime'],fmt)
bgremactime = datetime.strptime(kwargs['bgremactime'],fmt)
actbgsctime = niqlib.decay(float(kwargs['bgac']),float((scantime - bgactime).seconds/60.),th) - niqlib.decay(float(kwargs['bgremac']),float((scantime - bgremactime).seconds/60.),th)
output['Act BG (scantime)'] = actbgsctime
output['Scantime'] = scantime.time()
print "Writing results to wadserver"
print "1 - loading data"
phantom = uniform_phantom(data=filename, order=axorder,phantomdim=kwargs['dimensions'],phantomvolume=kwargs['volume'],dose=kwargs['bgac'],dosecaltime=kwargs['bgactime'],remdose=kwargs['bgremac'],remdosecaltime=kwargs['bgremactime'])
print phantom.z
output['z_slice'] = phantom.z
zslicefig = plt.figure()
ax1 = zslicefig.add_subplot(111)
plt.xtitle = "Z slice"
plt.ytitle = "Counts in slice"
plt.plot(phantom.z_slice())
#plt.show()
output['EARL zprofile'] = zslicefig
print "2 - info from header"
"""We need to calculate the activity concentration in Bq/ml from the
volume of the phantom and the administered activity.
"""
# suv = c(t) / (A(t) / bodyweight)
#
# In the case of suv phantom:
# suv = c(t) / (A(t) / 9.293), where A in MBq and c in kBq/cc
#
# dicom[7053,1000] converts to SUV
# For Philips specific:
# There are 2 ways to calculate the suv
# 1 - multiply pixel value with tag 7053,1000 and make sure the correct weight was used (or correct)
# 2 - multiply pix. val. with tag 7053, 1009 (rescale slope) which converts to bq/cc and correct for decay
suv = lambda x:1000*x*float(phantom.suvfactor)/phantom.currconc #option 2
print "3 - show result: "
sid = phantom.z
output['EARL histogram']=phantom.plot_img_hist(sid, bins=256,showfig=False)
for k in [sid - 6, sid - 4, sid, sid + 4, sid + 6]:
tmpfig, tmpunif,tmpstd = phantom.slice_uniformity(k,showfig=False)
print "uniformity: ", k, tmpunif, tmpstd, actbgsctime, suv(tmpunif)
output['SUV slice %s'%str(k - sid)] = suv(tmpunif)
output['COV slice %s'%str(k - sid)] = tmpstd/tmpunif
output['EARL halfslice'] = phantom.plot_dataspheres(sid) #phantom.visualize_slices(sid)
print "4 - sphere statistics: "
bgresults = phantom.get_bg_stats()
meansuvlist = []
maxsuvlist = []
for key in bgresults.keys():
print "BG Sphere: ", key
tmpresult = bgresults[key]
meansuvlist.append([key,tmpresult['avg'],tmpresult['avg']/phantom.currconc])
maxsuvlist.append([key,tmpresult['max'],tmpresult['max']/phantom.currconc])
for subkey in tmpresult.keys():
print '\t', subkey, tmpresult[subkey]
print "SUVmean: ",meansuvlist
print "SUVmax: ",maxsuvlist
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(zip(*meansuvlist)[2])
#plt.show()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(zip(*maxsuvlist)[2])
output['EARL suvcurve'] = fig
#plt.show()
output['procstat'] = 'processed'
output['status'] = 'processed'
return output
