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dsc_roiSignal2concentration.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This program extracts temporal signal within a given ROI and applies the implemented signal processing
pipeline (effective TR correction, discard missed triggers data, breathing frequencies filtering, smoothing).
Created on Mon Oct 14 19:21:50 2019
@author: slevy
"""
import dsc_utils
import nibabel as nib
import numpy as np
from scipy.io import savemat
import argparse
import _pickle as pckl
import dsc_pipelines
import os
import matplotlib.pyplot as plt
def main(iFname, maskFname, physioLogFname, oFname, injRep, firstPassStartTime, firstPassEndTime, TE, r2GdInBlood, paramFilePath):
"""Main."""
# ----------------------------------------------------------------------------------------------------------------------
# load data
# ----------------------------------------------------------------------------------------------------------------------
img = nib.load(iFname).get_data() # MRI image
mask = nib.load(maskFname).get_data() # masks
# ----------------------------------------------------------------------------------------------------------------------
# extract mean in mask
# ----------------------------------------------------------------------------------------------------------------------
mriSignal_meanAllSlices, mriSignal_bySlice = dsc_utils.extract_signal_within_roi(img, mask)
allMRIsignals = np.vstack((mriSignal_meanAllSlices, mriSignal_bySlice.T))
# ----------------------------------------------------------------------------------------------------------------------
# Physio processing
# ----------------------------------------------------------------------------------------------------------------------
if not (injRep and physioLogFname and TE):
# get file name of physiolog and other parameters automatically
subjID = os.path.abspath(iFname).split('/')[-3]
baseDir = '/'.join(os.path.abspath(iFname).split('/')[0:-3])
physioLogFname, TE, injRep, gap, TR, acqTime_firstImg, firstPassStart, firstPassEnd, resolution = dsc_utils.get_physiologFname_TE_injRep(subjID, filename=paramFilePath, baseDir=baseDir)
# process phyiolog informations
# repsAcqTime: ((SC+all slices) x Nacq x (PulseOx, Resp)
# timePhysio: N_pulseOx_points x ((PulseOx, Resp)
# valuesPhysio: N_pulseOx_points x ((PulseOx, Resp)
repsAcqTime, timePhysio, valuesPhysio = dsc_utils.extract_acqtime_and_physio_by_slice(physioLogFname, img.shape[2], img.shape[3], acqTime_firstImg)
# reorder the acquisition times of each slice according to the acquisition scheme ("interleaved", "ascending",
# "descending")
acqScheme = "interleaved"
if acqScheme == "interleaved":
actualAcqTime_idx = [0, 2, 1]
elif acqScheme == "ascending":
actualAcqTime_idx = [0, 1, 2]
elif acqScheme == "descending":
actualAcqTime_idx = [2, 1, 0]
slicesAcqTime = repsAcqTime[1:, :, :]
repsAcqTime[1:, :, :] = slicesAcqTime[actualAcqTime_idx, :, :]
injTime = repsAcqTime[0, injRep, 0]
print('\n\t>> Contrast agent infected at repetition #%i <=> t=%.1fms.\n\n' % (injRep, injTime))
# ----------------------------------------------------------------------------------------------------------------------
# Normalize all signals by 1 - exp(-TReffective/T1)
# ----------------------------------------------------------------------------------------------------------------------
TReff = np.append(np.diff(repsAcqTime[0, :, 0])[0], np.diff(repsAcqTime[0, :, 0]))
allMRIsignals_TRnorm = np.divide(allMRIsignals, np.tile(1 - np.exp(-TReff/1251), (allMRIsignals.shape[0], 1)))
# ----------------------------------------------------------------------------------------------------------------------
# Discard acquisitions with an effective TR of two cardiac cycles (missed a trigger)
# ----------------------------------------------------------------------------------------------------------------------
allMRIsignals_TRfiltered, repsAcqTime_PulseOx_TRfiltered, repsAcqTime_Resp_TRfiltered, idxAcqToDiscard, cardiacPeriod = dsc_utils.discardWrongTRs(TReff, timePhysio[:, 0], valuesPhysio[:, 0], allMRIsignals_TRnorm, repsAcqTime[:, :, 0], repsAcqTime[:, :, 1], outPlotFname='')
repsAcqTime_TRfiltered = np.stack((repsAcqTime_PulseOx_TRfiltered, repsAcqTime_Resp_TRfiltered), axis=2)
# ----------------------------------------------------------------------------------------------------------------------
# Regrid physio signals with regular sampling (twice more sampling) except the MRI signal (will be done in the
# following for loop)
# ----------------------------------------------------------------------------------------------------------------------
interpFactor = 1
timePhysioRegrid = np.zeros((interpFactor*timePhysio.shape[0], timePhysio.shape[1]))
valuesPhysioRegrid = np.zeros((interpFactor*timePhysio.shape[0], timePhysio.shape[1]))
# PulseOx
timePhysioRegrid[:, 0] = np.linspace(np.min(timePhysio[:, 0]), np.max(timePhysio[:, 0]), interpFactor * timePhysio.shape[0])
valuesPhysioRegrid[:, 0] = np.interp(timePhysioRegrid[:, 0], timePhysio[:, 0], valuesPhysio[:, 0])
# Respiration
timePhysioRegrid[:, 1] = timePhysioRegrid[:, 0]
valuesPhysioRegrid[:, 1] = np.interp(timePhysioRegrid[:, 1], timePhysio[:, 1], valuesPhysio[:, 1])
# ----------------------------------------------------------------------------------------------------------------------
# filter each signal individually
# ----------------------------------------------------------------------------------------------------------------------
# signal in whole SC
acqTimeRegrid0, signal0_breathFilt, signal0Filtered, _ = dsc_pipelines.filterSignal(allMRIsignals_TRfiltered[0, :], repsAcqTime_TRfiltered[0, :, 0], timePhysioRegrid, valuesPhysioRegrid, cardiacPeriod)
signalsFiltered = np.zeros((img.shape[2]+1, signal0Filtered.size)) # (SC+all slices) x time
# signalsFilteredCrop = np.zeros((img.shape[2]+1, signal0FilteredCrop.size)) # (SC+all slices) x time
acqTimeRegrid = np.zeros((img.shape[2]+1, signal0Filtered.size)) # (SC+all slices) x time
# store first subject (already processed)
signalsFiltered[0, :] = signal0Filtered
# signalsFilteredCrop[0, :] = signal0FilteredCrop
acqTimeRegrid[0, :] = acqTimeRegrid0
# signal in individual slices
for i_slice in range(img.shape[2]):
acqTimeRegrid[i_slice+1, :], signal_i_slice_breathFilt, signalsFiltered[i_slice+1, :], _ = dsc_pipelines.filterSignal(allMRIsignals_TRfiltered[i_slice+1, :], repsAcqTime_TRfiltered[i_slice+1, :, 0], timePhysioRegrid, valuesPhysioRegrid, cardiacPeriod)
# injection time
injRepRegrid = np.abs(acqTimeRegrid - injTime).argmin(axis=-1)
# # ----------------------------------------------------------------------------------------------------------------------
# # Convert signal to concentration in (mmol/L): C(t) = -1/(r*TE)*log(S(t)/S0)
# # ----------------------------------------------------------------------------------------------------------------------
# S0AllSignals = np.zeros(img.shape[2]+1) # (SC+all slices)
# concAllSignals = np.zeros(signalsFiltered.shape) # (SC+all slices) x time
#
# # signal within whole SC
# S0AllSignals[0], concAllSignals[0, :] = convSignalToConc(signalsFiltered[0, :], acqTimeRegrid[0, :], injTime, TE, r2GdInBlood)
# # signal in individual slices
# for i_slice in range(img.shape[2]):
# S0AllSignals[i_slice+1], concAllSignals[i_slice+1, :] = convSignalToConc(signalsFiltered[i_slice+1, :], acqTimeRegrid[i_slice+1, :], injTime, TE, r2GdInBlood)
# ----------------------------------------------------------------------------------------------------------------------
# Convert signal to concentration in (mmol/L): C(t) = -1/(r*TE)*log(S(t)/S0)
# ----------------------------------------------------------------------------------------------------------------------
concAllSignals, _, S0AllSignals = dsc_utils.calculateDeltaR2(signalsFiltered, TE, injRepRegrid, r2GdInBlood)
# ----------------------------------------------------------------------------------------------------------------------
# save processed data for further application of DSC models
# ----------------------------------------------------------------------------------------------------------------------
savemat(oFname+'.mat', {"allMRIsignals_TRfiltered": allMRIsignals_TRfiltered,
"repsAcqTime_TRfiltered": repsAcqTime_TRfiltered,
"signalsFiltered": signalsFiltered,
# "signalsFilteredCrop": signalsFilteredCrop,
"acqTimeRegrid": acqTimeRegrid,
"S0AllSignals": S0AllSignals,
"concAllSignals": concAllSignals,
"injectionRep": injRep,
"injTime": injTime,
"firstPassStartTime": firstPassStartTime,
"firstPassEndTime": firstPassEndTime,
"TE": TE,
"r2GdInBlood": r2GdInBlood})
pckl.dump({"allMRIsignals_TRfiltered": allMRIsignals_TRfiltered,
"repsAcqTime_TRfiltered": repsAcqTime_TRfiltered,
"signalsFiltered": signalsFiltered,
# "signalsFilteredCrop": signalsFilteredCrop,
"acqTimeRegrid": acqTimeRegrid,
"S0AllSignals": S0AllSignals,
"concAllSignals": concAllSignals,
"injectionRep": injRep,
"injTime": injTime,
"firstPassStartTime": firstPassStartTime,
"firstPassEndTime": firstPassEndTime,
"TE": TE,
"r2GdInBlood": r2GdInBlood},
open(oFname + '.pickle', 'wb'))
# ----------------------------------------------------------------------------------------------------------------------
# plot results
# ----------------------------------------------------------------------------------------------------------------------
fig, axes = plt.subplots(2, 1, figsize=(17, 9.5))
plt.subplots_adjust(wspace=0.07, left=0.25, right=0.99, hspace=0.2, bottom=0.07, top=0.89)
# Signal by slice and all slices averaged
ylims = dsc_utils.plot_DeltaR2_perSlice(acqTimeRegrid/1000, signalsFiltered, TE, axes[0], xlabel='Time (s)', lateralTitle='Slice-wise\nprofile', injTime=injTime/1000, stats=True, cardiacPeriod=cardiacPeriod/1000, timeAcqToDiscard=repsAcqTime[0, idxAcqToDiscard, 0]/1000)
# Signal from all slices averaged
ylims = dsc_utils.plot_DeltaR2_perSlice(acqTimeRegrid[np.newaxis, 0, :]/1000, signalsFiltered[np.newaxis, 0, :], TE, axes[1], xlabel='Time (s)', lateralTitle='Average in ROI\n(all slices averaged)', injTime=injTime/1000, stats=False, signalLabels=[], cardiacPeriod=cardiacPeriod/1000, timeAcqToDiscard=repsAcqTime[0, idxAcqToDiscard, 0]/1000, ylims=ylims)
fig.savefig(oFname+'.pdf', transparent=True)
plt.show(fig)
# def convSignalToConc(mriSignal, acqTimeRegrid, injTime, TE, r2GdInBlood):
#
# # ----------------------------------------------------------------------------------------------------------------------
# # baseline last rep
# # ----------------------------------------------------------------------------------------------------------------------
# injRepRegrid = np.abs(acqTimeRegrid - injTime).argmin()
#
# # ----------------------------------------------------------------------------------------------------------------------
# # Compute baseline (S0)
# # ----------------------------------------------------------------------------------------------------------------------
# S0 = np.mean(mriSignal[0:injRepRegrid])
#
# # ----------------------------------------------------------------------------------------------------------------------
# # Convert signal to concentration in (mmol/L): C(t) = -1/(r*TE)*log(S(t)/S0)
# # ----------------------------------------------------------------------------------------------------------------------
# conc = - np.log(mriSignal / S0) / (r2GdInBlood * TE / 1000)
#
# return S0, conc
# ==========================================================================================
if __name__ == "__main__":
# parse arguments
parser = argparse.ArgumentParser(description='This program extracts temporal signal within a given ROI and applies '
'the implemented signal processing pipeline (effective TR correction, '
'discard missed triggers data, breathing frequencies filtering, '
'smoothing).')
optionalArgs = parser._action_groups.pop()
requiredArgs = parser.add_argument_group('required arguments')
requiredArgs.add_argument('-i', dest='iFname', help='Path to MRI data file.', type=str, required=True)
requiredArgs.add_argument('-m', dest='maskFname', help='NIFTI volume defining the region of interest.', type=str, required=True)
requiredArgs.add_argument('-o', dest='oFname', help='Filename for the output plots and data.', type=str, required=True)
optionalArgs.add_argument('-param', dest='paramFilePath', help='Path to file giving specific parameters (injection repetition, dicom path).', type=str, required=False, default='')
optionalArgs.add_argument('-physio', dest='physioLogFname', help='Basename of physio log for Pulse Ox and Respiration.', type=str, required=False, default='')
optionalArgs.add_argument('-inj', dest='injRep', help='Number of the repetition when contrast agent injection was launched.', type=int, required=False, default=0)
optionalArgs.add_argument('-s', dest='firstPassStartTime', help='Start time (on original time grid) of first pass (in seconds).', type=float, required=False, default=50.0)
optionalArgs.add_argument('-e', dest='firstPassEndTime', help='Time (on original time grid) of first pass end (in seconds).', type=float, required=False, default=71.0)
optionalArgs.add_argument('-te', dest='TE', help='Echo time in milliseconds.', type=float, required=False, default=0.0)
optionalArgs.add_argument('-r2', dest='r2GdInBlood', help='Transverve relaxivity (in s-1.mmol-1.L = s-1.mM-1) of Gadolinium in blood.'
' Default = 3.55 s-1.mmol-1.L [from Proc. Intl. Soc. Mag. Reson. Med. 16 (2008) 1457]', type=float, required=False, default=3.55)
parser._action_groups.append(optionalArgs)
args = parser.parse_args()
# FAparamsArgs = np.array(args.FAparams.strip().split(','), dtype=float)
# run main
main(iFname=args.iFname, maskFname=args.maskFname, oFname=args.oFname, physioLogFname=args.physioLogFname, injRep=args.injRep, firstPassStartTime=1000*args.firstPassStartTime,
firstPassEndTime=1000*args.firstPassEndTime, TE=args.TE, r2GdInBlood=args.r2GdInBlood, paramFilePath=args.paramFilePath)