def preproc(data_dir, sink_dir, subject, task, session, run, masks, motion_thresh, moco): from nipype.interfaces.fsl import MCFLIRT, FLIRT, FNIRT, ExtractROI, ApplyWarp, MotionOutliers, InvWarp, FAST #from nipype.interfaces.afni import AlignEpiAnatPy from nipype.interfaces.utility import Function from nilearn.plotting import plot_anat from nilearn import input_data #WRITE A DARA GRABBER def get_niftis(subject_id, data_dir, task, run, session): from os.path import join, exists t1 = join(data_dir, subject_id, 'session-{0}'.format(session), 'anatomical', 'anatomical-0', 'anatomical.nii.gz') #t1_brain_mask = join(data_dir, subject_id, 'session-1', 'anatomical', 'anatomical-0', 'fsl', 'anatomical-bet.nii.gz') epi = join(data_dir, subject_id, 'session-{0}'.format(session), task, '{0}-{1}'.format(task, run), '{0}.nii.gz'.format(task)) assert exists(t1), "t1 does not exist at {0}".format(t1) assert exists(epi), "epi does not exist at {0}".format(epi) standard = '/home/applications/fsl/5.0.8/data/standard/MNI152_T1_2mm.nii.gz' return t1, epi, standard data = Function( function=get_niftis, input_names=["subject_id", "data_dir", "task", "run", "session"], output_names=["t1", "epi", "standard"]) data.inputs.data_dir = data_dir data.inputs.subject_id = subject data.inputs.run = run data.inputs.session = session data.inputs.task = task grabber = data.run() if session == 0: sesh = 'pre' if session == 1: sesh = 'post' #reg_dir = '/home/data/nbc/physics-learning/data/first-level/{0}/session-1/retr/retr-{1}/retr-5mm.feat/reg'.format(subject, run) #set output paths for quality assurance pngs qa1 = join( sink_dir, 'qa', '{0}-session-{1}_{2}-{3}_t1_flirt.png'.format(subject, session, task, run)) qa2 = join( sink_dir, 'qa', '{0}-session-{1}_{2}-{3}_mni_flirt.png'.format(subject, session, task, run)) qa3 = join( sink_dir, 'qa', '{0}-session-{1}_{2}-{3}_mni_fnirt.png'.format(subject, session, task, run)) confound_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_confounds.txt'.format(subject, session, task, run)) #run motion correction if indicated if moco == True: mcflirt = MCFLIRT(ref_vol=144, save_plots=True, output_type='NIFTI_GZ') mcflirt.inputs.in_file = grabber.outputs.epi #mcflirt.inputs.in_file = join(data_dir, subject, 'session-1', 'retr', 'retr-{0}'.format(run), 'retr.nii.gz') mcflirt.inputs.out_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mcf.nii.gz'.format( subject, session, task, run)) flirty = mcflirt.run() motion = np.genfromtxt(flirty.outputs.par_file) else: print "no moco needed" motion = 0 #calculate motion outliers try: mout = MotionOutliers(metric='fd', threshold=motion_thresh) mout.inputs.in_file = grabber.outputs.epi mout.inputs.out_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_fd-gt-{3}mm'.format( subject, session, task, run, motion_thresh)) mout.inputs.out_metric_plot = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_metrics.png'.format( subject, session, task, run)) mout.inputs.out_metric_values = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_fd.txt'.format(subject, session, task, run)) moutliers = mout.run() outliers = np.genfromtxt(moutliers.outputs.out_file) e = 'no errors in motion outliers, yay' except Exception as e: print(e) outliers = np.genfromtxt(mout.inputs.out_metric_values) #set everything above the threshold to 1 and everything below to 0 outliers[outliers > motion_thresh] = 1 outliers[outliers < motion_thresh] = 0 #concatenate motion parameters and motion outliers to form confounds file #outliers = outliers.reshape((outliers.shape[0],1)) conf = outliers np.savetxt(confound_file, conf, delimiter=',') #extract an example volume for normalization ex_fun = ExtractROI(t_min=144, t_size=1) ex_fun.inputs.in_file = flirty.outputs.out_file ex_fun.inputs.roi_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}-example_func.nii.gz'.format( subject, session, task, run)) fun = ex_fun.run() warp = ApplyWarp(interp="nn", abswarp=True) if not exists( '/home/data/nbc/physics-learning/data/first-level/{0}/session-{1}/{2}/{2}-{3}/{2}-5mm.feat/reg/example_func2standard_warp.nii.gz' .format(subject, session, task, run)): #two-step normalization using flirt and fnirt, outputting qa pix flit = FLIRT(cost_func="corratio", dof=12) reg_func = flit.run( reference=fun.outputs.roi_file, in_file=grabber.outputs.t1, searchr_x=[-180, 180], searchr_y=[-180, 180], out_file=join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_t1-flirt.nii.gz'.format( subject, session, task, run)), out_matrix_file=join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_t1-flirt.mat'.format( subject, session, task, run))) reg_mni = flit.run( reference=grabber.outputs.t1, in_file=grabber.outputs.standard, searchr_y=[-180, 180], searchr_z=[-180, 180], out_file=join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-flirt-t1.nii.gz'.format( subject, session, task, run)), out_matrix_file=join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-flirt-t1.mat'.format( subject, session, task, run))) #plot_stat_map(aligner.outputs.out_file, bg_img=fun.outputs.roi_file, colorbar=True, draw_cross=False, threshold=1000, output_file=qa1a, dim=-2) display = plot_anat(fun.outputs.roi_file, dim=-1) display.add_edges(reg_func.outputs.out_file) display.savefig(qa1, dpi=300) display.close() display = plot_anat(grabber.outputs.t1, dim=-1) display.add_edges(reg_mni.outputs.out_file) display.savefig(qa2, dpi=300) display.close() perf = FNIRT(output_type='NIFTI_GZ') perf.inputs.warped_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-fnirt-t1.nii.gz'.format( subject, session, task, run)) perf.inputs.affine_file = reg_mni.outputs.out_matrix_file perf.inputs.in_file = grabber.outputs.standard perf.inputs.subsampling_scheme = [8, 4, 2, 2] perf.inputs.fieldcoeff_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warpcoeff.nii.gz'.format( subject, session, task, run)) perf.inputs.field_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warp.nii.gz'.format( subject, session, task, run)) perf.inputs.ref_file = grabber.outputs.t1 reg2 = perf.run() warp.inputs.field_file = reg2.outputs.field_file #plot fnirted MNI overlaid on example func display = plot_anat(grabber.outputs.t1, dim=-1) display.add_edges(reg2.outputs.warped_file) display.savefig(qa3, dpi=300) display.close() else: warpspeed = InvWarp(output_type='NIFTI_GZ') warpspeed.inputs.warp = '/home/data/nbc/physics-learning/data/first-level/{0}/session-{1}/{2}/{2}-{3}/{2}-5mm.feat/reg/example_func2standard_warp.nii.gz'.format( subject, session, task, run) warpspeed.inputs.reference = fun.outputs.roi_file warpspeed.inputs.inverse_warp = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warp.nii.gz'.format( subject, session, task, run)) mni2epiwarp = warpspeed.run() warp.inputs.field_file = mni2epiwarp.outputs.inverse_warp for key in masks.keys(): #warp takes us from mni to epi warp.inputs.in_file = masks[key] warp.inputs.ref_file = fun.outputs.roi_file warp.inputs.out_file = join( sink_dir, sesh, subject, '{0}-session-{1}_{2}-{3}_{4}.nii.gz'.format( subject, session, task, run, key)) net_warp = warp.run() qa_file = join( sink_dir, 'qa', '{0}-session-{1}_{2}-{3}_qa_{4}.png'.format( subject, session, task, run, key)) display = plotting.plot_roi(net_warp.outputs.out_file, bg_img=fun.outputs.roi_file, colorbar=True, vmin=0, vmax=18, draw_cross=False) display.savefig(qa_file, dpi=300) display.close() return flirty.outputs.out_file, confound_file, e
def fnirt_again(data_dir, sink_dir, subject, run, masks, mask_names): import numpy as np def get_niftis(subject, data_dir, sink_dir, run, masks): from os.path import join, exists t1 = join( data_dir, subject, "session-1", "anatomical", "anatomical-0", "anatomical.nii.gz", ) # t1_brain_mask = join(data_dir, subject, 'session-1', 'anatomical', 'anatomical-0', 'fsl', 'anatomical-bet.nii.gz') example_func = join( sink_dir, subject, "{0}-{1}_retr-example_func.nii.gz".format(subject, run)) assert exists(t1), "t1 does not exist" assert exists(example_func), "example_func does not exist" standard = "/home/applications/fsl/5.0.8/data/standard/MNI152_T1_2mm.nii.gz" mni2t1 = join(sink_dir, subject, "{0}-{1}_mni-flirt-t1.mat".format(subject, run)) t12epi = join(sink_dir, subject, "{0}-{1}_t1-flirt-retr.mat".format(subject, run)) masks = masks return t1, example_func, standard, mni2t1, t12epi, masks data = Function( function=get_niftis, input_names=["subject", "data_dir", "sink_dir", "run", "masks"], output_names=[ "t1", "example_func", "standard", "mni2t1", "t12epi", "masks" ], ) data.inputs.data_dir = data_dir data.inputs.sink_dir = sink_dir data.inputs.subject = subject data.inputs.masks = masks data.inputs.run = run grabber = data.run() perf = FNIRT(output_type="NIFTI_GZ") perf.inputs.warped_file = join( sink_dir, subject, "{0}-{1}_mni-fnirt-t1.nii.gz".format(subject, run)) perf.inputs.affine_file = grabber.outputs.mni2t1 perf.inputs.in_file = grabber.outputs.standard perf.inputs.subsampling_scheme = [8, 4, 2, 2] perf.inputs.fieldcoeff_file = join( sink_dir, subject, "{0}-{1}_mni-fnirt-t1-warpcoef.nii.gz".format(subject, run)) perf.inputs.field_file = join( sink_dir, subject, "{0}-{1}_mni-fnirt-t1-warp.nii.gz".format(subject, run)) perf.inputs.ref_file = grabber.outputs.t1 reg2 = perf.run() for i in np.arange(0, len(masks)): # warp takes us from mni to t1, postmat warp = ApplyWarp(interp="nn", abswarp=True) warp.inputs.in_file = grabber.outputs.masks[i] warp.inputs.ref_file = grabber.outputs.example_func warp.inputs.field_file = reg2.outputs.field_file warp.inputs.postmat = grabber.outputs.t12epi warp.inputs.out_file = join( sink_dir, subject, "{0}-{1}_{2}_retr.nii.gz".format(subject, run, mask_names[i]), ) net_warp = warp.run() return "yay"