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SAR_calc_main.py
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Sat, Sep 7, 22:25

SAR_calc_main.py
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# Copyright of the Board of Trustees of Columbia University in the City of New York
"""
#. This script computes the global head and body Specific Absoprtion Rate (SAR) values based on the Visible HUman Male model
#. This assumes an eight channel multi-transmit system with a scaled B1+
#. IEC checks on SAR resulting from a given sequence file
Parameters
----------
fname : str
Requires coms_server_flask to be running before the unit test is run (i.e.: run coms_server_flask.py first)
Returns
-------
payload : dict
* Contains the Q-matrix, GSAR head and body for now
* Will include local SAR based on discussions related to ongoing project
"""
import time
import matplotlib.pyplot as plt
import numpy as np
import numpy.matlib
import scipy.io as sio
from pypulseq.Sequence.sequence import Sequence
from pypulseq.calc_duration import calc_duration
from scipy import interpolate
from virtualscanner.utils import constants
SAR_PATH = constants.RF_SAR_PATH
IMG_SAR_PATH = constants.RF_SAR_STATIC_IMG_PATH
def calc_SAR(Q, I):
if len(I.shape) == 1: # Just to fit the multi-transmit case for now, ToDo
I = np.matlib.repmat(I, Q.shape[0], 1) # Nc x Nt
Ifact = np.divide(np.matmul(I, np.matrix(I).getH()), I.shape[1])
SAR_temp = np.multiply(Q, Ifact)
SAR = np.abs(np.sum(SAR_temp[:]))
return SAR
def loadQ():
"""
This definition loads the Q matrix that is precomputed based on the VHM model for 8 channels
Returns
-------
Qtmf, Qhmf : numpy.ndarray
Contains the Q-matrix, GSAR head and body for now
"""
# Load relevant Q matrices computed from the model - this code will be integrated later - starting from E fields
Qpath = str(SAR_PATH) + '/assets/QGlobal.mat'
Qmat = sio.loadmat(
Qpath)
Q = Qmat['Q']
val = Q[0, 0]
Qtmf = val['Qtmf']
Qhmf = val['Qhmf']
return Qtmf, Qhmf
def SARfromseq(fname, Qtmf, Qhmf):
"""
This definition computes the global whole body and head only SAR values
Parameters
----------
fname : str
Qtmf : numpy.ndarray
Qhmf : numpy.ndarray
Returns
-------
SARwbg_vec : numpy.ndarray
SARhg_vec : numpy.ndarray
t_vec : numpy.ndarray
contains the Q-matrix, GSAR head and body for now
"""
obj = Sequence()
obj.read(str(SAR_PATH / 'assets' / fname)) # replaced by
# Identify rf blocks and compute SAR - 10 seconds must be less than twice and 6 minutes must be less than 4 (WB) and 3.2 (head-20)
blockEvents = obj.block_events
numEvents = len(blockEvents)
t_vec = np.zeros(numEvents)
SARwbg_vec = np.zeros(t_vec.shape)
SARhg_vec = np.zeros(t_vec.shape)
t_prev = 0
for iB in blockEvents:
block = obj.get_block(iB)
block_dur = calc_duration(block)
t_vec[iB - 1] = t_prev + block_dur
t_prev = t_vec[iB - 1]
if ('rf' in block): # has rf
rf = block['rf']
t = rf.t
signal = rf.signal
# This rf could be parallel transmit as well
SARwbg_vec[iB] = calc_SAR(Qtmf, signal)
SARhg_vec[iB] = calc_SAR(Qhmf, signal)
return SARwbg_vec, SARhg_vec, t_vec
def SARinterp(SAR, t):
"""
This definition interpolates the SAR values for one second resolution
Parameters
----------
SAR : numpy.ndarray
t : numpy.ndarray
Returns
-------
SARinterp : numpy.ndarray
tsec : numpy.ndarray
Interpolated values of SAR for a temporal resolution of 1 second
"""
tsec = np.arange(1, np.floor(t[-1]) + 1, 1)
f = interpolate.interp1d(t, SAR)
SARinterp = f(tsec)
return SARinterp, tsec
def SARlimscheck(SARwbg_lim_s, SARhg_lim_s, tsec):
"""
This definition checks for SAR violations as compared to IEC 10 second and 6 minute averages;
it returns SAR values that are interpolated for the fixed IEC time intervals.
Parameters
----------
SARwbg_lim_s : numpy.ndarray
SARhg_lim_s : numpy.ndarray
tsec : numpy.ndarray
Returns
-------
SAR_wbg_tensec : numpy.ndarray
SAR_wbg_sixmin : numpy.ndarray
SAR_hg_tensec : numpy.ndarray
SAR_hg_sixmin : numpy.ndarray
SAR_wbg_sixmin_peak : numpy.ndarray
SAR_hg_sixmin_peak : numpy.ndarray
SAR_wbg_tensec_peak : numpy.ndarray
SAR_hg_tensec_peak : numpy.ndarray
"""
if (tsec[-1] > 10):
SixMinThresh_wbg = 4
TenSecThresh_wbg = 8
SixMinThresh_hg = 3.2
TenSecThresh_hg = 6.4
SARwbg_lim_app = np.concatenate((np.zeros(5), SARwbg_lim_s, np.zeros(5)), axis=0)
SARhg_lim_app = np.concatenate((np.zeros(5), SARhg_lim_s, np.zeros(5)), axis=0)
SAR_wbg_tensec = do_sw_sar(SARwbg_lim_app, tsec, 10) # < 2 SARmax
SAR_hg_tensec = do_sw_sar(SARhg_lim_app, tsec, 10) # < 2 SARmax
SAR_wbg_tensec_peak = np.round(np.max(SAR_wbg_tensec), 2)
SAR_hg_tensec_peak = np.round(np.max(SAR_hg_tensec), 2)
if ((np.max(SAR_wbg_tensec) > TenSecThresh_wbg) or (np.max(SAR_hg_tensec) > TenSecThresh_hg)):
print('Pulse exceeding 10 second Global SAR limits, increase TR')
SAR_wbg_sixmin = 'NA'
SAR_hg_sixmin = 'NA'
SAR_wbg_sixmin_peak = 'NA'
SAR_hg_sixmin_peak = 'NA'
if (tsec[-1] > 600):
SARwbg_lim_app = np.concatenate((np.zeros(300), SARwbg_lim_s, np.zeros(300)), axis=0)
SARhg_lim_app = np.concatenate((np.zeros(300), SARhg_lim_s, np.zeros(300)), axis=0)
SAR_hg_sixmin = do_sw_sar(SARhg_lim_app, tsec, 600)
SAR_wbg_sixmin = do_sw_sar(SARwbg_lim_app, tsec, 600)
SAR_wbg_sixmin_peak = np.round(np.max(SAR_wbg_sixmin), 2)
SAR_hg_sixmin_peak = np.round(np.max(SAR_hg_sixmin), 2)
if ((np.max(SAR_hg_sixmin) > SixMinThresh_wbg) or (np.max(SAR_hg_sixmin) > SixMinThresh_hg)):
print('Pulse exceeding 10 second Global SAR limits, increase TR')
else:
print('Need at least 10 seconds worth of sequence to calculate SAR')
SAR_wbg_tensec = 'NA'
SAR_wbg_sixmin = 'NA'
SAR_hg_tensec = "NA"
SAR_hg_sixmin = "NA"
SAR_wbg_sixmin_peak = 'NA'
SAR_hg_sixmin_peak = 'NA'
SAR_wbg_tensec_peak = 'NA'
SAR_hg_tensec_peak = 'NA'
return SAR_wbg_tensec, SAR_wbg_sixmin, SAR_hg_tensec, SAR_hg_sixmin, SAR_wbg_sixmin_peak, SAR_hg_sixmin_peak, SAR_wbg_tensec_peak, SAR_hg_tensec_peak
def do_sw_sar(SAR, tsec, t):
"""
This definition computes a sliding window average of SAR values
Parameters
----------
SAR : numpy.ndarray
tsec : numpy.ndarray
t : numpy.ndarray
Returns
-------
SAR_timeavag : numpy.ndarray
Sliding window time average of SAR values
"""
SAR_timeavg = np.zeros(len(tsec) + int(t))
for instant in range(int(t / 2), int(t / 2) + (int(tsec[-1]))): # better to go from -sw / 2: sw / 2
SAR_timeavg[instant] = sum(SAR[range(instant - int(t / 2), instant + int(t / 2) - 1)]) / t
SAR_timeavg = SAR_timeavg[int(t / 2):int(t / 2) + (int(tsec[-1]))]
return SAR_timeavg
def payload_process(fname='rad2D.seq'):
"""
This definition processes the seq file to compute Global SAR values for head and whole body over the specified time averages
Parameters
----------
fname : str, optional
Default is rad2.seq
Returns
-------
payload : dict
SAR graph figure filename, peak SAR values
"""
Qtmf, Qhmf = loadQ()
SARwbg, SARhg, t_vec = SARfromseq(fname, Qtmf, Qhmf)
SARwbg_lim, tsec = SARinterp(SARwbg, t_vec)
SARhg_lim, tsec = SARinterp(SARhg, t_vec)
SAR_wbg_tensec, SAR_wbg_sixmin, SAR_hg_tensec, SAR_hg_sixmin, SAR_wbg_sixmin_peak, SAR_hg_sixmin_peak, SAR_wbg_tensec_peak, SAR_hg_tensec_peak = SARlimscheck(
SARwbg_lim, SARhg_lim, tsec)
imgpath = str(IMG_SAR_PATH)
timestamp = time.strftime("%Y%m%d%H%M%S")
fname_store = timestamp + "_SAR1.png"
payload = {
"filename": fname_store,
"SAR_wbg_tensec_peak": SAR_wbg_tensec_peak,
"SAR_wbg_sixmin_peak": SAR_wbg_sixmin_peak,
"SAR_hg_tensec_peak": SAR_hg_tensec_peak,
"SAR_hg_sixmin_peak": SAR_hg_sixmin_peak, # random.randint(4, 100),
}
#
# print(payload)
# Display and save figures in hardcoded paths for now
# Plot 10 sec average SAR
if (tsec[-1] > 10):
print('Display starts now..')
plt.figure
plt.plot(tsec, SAR_wbg_tensec, label='Whole Body:10sec')
plt.plot(tsec, SAR_hg_tensec, label='Head only:10sec')
# plt.plot(t_vec, SARwbg, label='Whole Body - instant')
# plt.plot(t_vec, SARhg, label='Whole Body - instant')
plt.xlabel('time (s)')
plt.ylabel('SAR (W/kg)')
plt.title('Global SAR - Mass Normalized - Whole body and head only')
ax = plt.subplot(111)
ax.legend()
plt.grid(True)
plt.savefig(str(IMG_SAR_PATH / fname_store), bbox_inches='tight', pad_inches=0)
# plt.show() # Uncomment for local display - will hinder return function is persistent
print('SAR computation performed')
return payload

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