def getInputs(): import os from mrtrix3 import app, image, path, run if app.args.mask: run.command('mrconvert ' + path.fromUser(app.args.mask, True) + ' ' + path.toTemp('mask.mif', True) + ' -datatype bit -stride -1,+2,+3') if app.args.t2: if not image.match(app.args.input, app.args.t2): app.error('Provided T2 image does not match input T1 image') run.command('mrconvert ' + path.fromUser(app.args.t2, True) + ' ' + path.toTemp('T2.nii', True) + ' -stride -1,+2,+3')
def get_inputs(): #pylint: disable=unused-variable image.check_3d_nonunity(path.from_user(app.ARGS.input, False)) run.command('mrconvert ' + path.from_user(app.ARGS.input) + ' ' + path.to_scratch('input.mif')) if app.ARGS.mask: run.command('mrconvert ' + path.from_user(app.ARGS.mask) + ' ' + path.to_scratch('mask.mif') + ' -datatype bit -strides -1,+2,+3') if app.ARGS.t2: if not image.match(path.from_user(app.ARGS.input, False), path.from_user(app.ARGS.t2, False)): raise MRtrixError('Provided T2 image does not match input T1 image') run.command('mrconvert ' + path.from_user(app.ARGS.t2) + ' ' + path.to_scratch('T2.nii') + ' -strides -1,+2,+3')
def getInputs(): #pylint: disable=unused-variable from mrtrix3 import app, image, path, run image.check3DNonunity(path.fromUser(app.args.input, False)) run.command('mrconvert ' + path.fromUser(app.args.input, True) + ' ' + path.toTemp('input.mif', True)) if app.args.mask: run.command('mrconvert ' + path.fromUser(app.args.mask, True) + ' ' + path.toTemp('mask.mif', True) + ' -datatype bit -strides -1,+2,+3') if app.args.t2: if not image.match(app.args.input, app.args.t2): app.error('Provided T2 image does not match input T1 image') run.command('mrconvert ' + path.fromUser(app.args.t2, True) + ' ' + path.toTemp('T2.nii', True) + ' -strides -1,+2,+3')
def execute(): import os from distutils.spawn import find_executable from mrtrix3 import app, file, fsl, image, run if app.isWindows(): app.error( '\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows' ) fsl_path = os.environ.get('FSLDIR', '') if not fsl_path: app.error( 'Environment variable FSLDIR is not set; please run appropriate FSL configuration script' ) ssroi_cmd = 'standard_space_roi' if not find_executable(ssroi_cmd): ssroi_cmd = 'fsl5.0-standard_space_roi' if not find_executable(ssroi_cmd): app.error( 'Could not find FSL program standard_space_roi; please verify FSL install' ) bet_cmd = 'bet' if not find_executable(bet_cmd): bet_cmd = 'fsl5.0-bet' if not find_executable(bet_cmd): app.error( 'Could not find FSL program bet; please verify FSL install') fast_cmd = 'fast' if not find_executable(fast_cmd): fast_cmd = 'fsl5.0-fast' if not find_executable(fast_cmd): app.error( 'Could not find FSL program fast; please verify FSL install') first_cmd = 'run_first_all' if not find_executable(first_cmd): first_cmd = "fsl5.0-run_first_all" if not find_executable(first_cmd): app.error( 'Could not find FSL program run_first_all; please verify FSL install' ) first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin') if not os.path.isdir(first_atlas_path): app.error( 'Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager' ) fsl_suffix = fsl.suffix() sgm_structures = [ 'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal' ] if app.args.sgm_amyg_hipp: sgm_structures.extend(['L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp']) run.command('mrconvert input.mif T1.nii -stride -1,+2,+3') fast_t1_input = 'T1.nii' fast_t2_input = '' # Decide whether or not we're going to do any brain masking if os.path.exists('mask.mif'): fast_t1_input = 'T1_masked' + fsl_suffix # Check to see if the mask matches the T1 image if image.match('T1.nii', 'mask.mif'): run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input) mask_path = 'mask.mif' else: app.warn('Mask image does not match input image - re-gridding') run.command( 'mrtransform mask.mif mask_regrid.mif -template T1.nii') run.command('mrcalc T1.nii mask_regrid.mif ' + fast_t1_input) mask_path = 'mask_regrid.mif' if os.path.exists('T2.nii'): fast_t2_input = 'T2_masked' + fsl_suffix run.command('mrcalc T2.nii ' + mask_path + ' -mult ' + fast_t2_input) elif app.args.premasked: fast_t1_input = 'T1.nii' if os.path.exists('T2.nii'): fast_t2_input = 'T2.nii' else: # Use FSL command standard_space_roi to do an initial masking of the image before BET # Also reduce the FoV of the image # Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation mni_mask_path = os.path.join(fsl_path, 'data', 'standard', 'MNI152_T1_1mm_brain_mask_dil.nii.gz') mni_mask_dilation = 0 if os.path.exists(mni_mask_path): mni_mask_dilation = 4 else: mni_mask_path = os.path.join( fsl_path, 'data', 'standard', 'MNI152_T1_2mm_brain_mask_dil.nii.gz') if os.path.exists(mni_mask_path): mni_mask_dilation = 2 if mni_mask_dilation: run.command('maskfilter ' + mni_mask_path + ' dilate mni_mask.nii -npass ' + str(mni_mask_dilation)) if app.args.nocrop: ssroi_roi_option = ' -roiNONE' else: ssroi_roi_option = ' -roiFOV' run.command( ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -maskMASK mni_mask.nii' + ssroi_roi_option, False) else: run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b', False) # For whatever reason, the output file from standard_space_roi may not be # completed before BET is run file.waitFor('T1_preBET' + fsl_suffix) # BET fast_t1_input = 'T1_BET' + fsl_suffix run.command(bet_cmd + ' T1_preBET' + fsl_suffix + ' ' + fast_t1_input + ' -f 0.15 -R') if os.path.exists('T2.nii'): if app.args.nocrop: fast_t2_input = 'T2.nii' else: # Just a reduction of FoV, no sub-voxel interpolation going on run.command('mrtransform T2.nii T2_cropped.nii -template ' + fast_t1_input + ' -interp nearest') fast_t2_input = 'T2_cropped.nii' # Finish branching based on brain masking # FAST if fast_t2_input: run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input) else: run.command(fast_cmd + ' ' + fast_t1_input) fast_output_prefix = fast_t1_input.split('.')[0] # FIRST first_input_is_brain_extracted = '' if app.args.premasked: first_input_is_brain_extracted = ' -b' run.command(first_cmd + ' -s ' + ','.join(sgm_structures) + ' -i T1.nii -o first' + first_input_is_brain_extracted) # Test to see whether or not FIRST has succeeded # However if the expected image is absent, it may be due to FIRST being run # on SGE; in this case it is necessary to wait and see if the file appears. # But even in this case, FIRST may still fail, and the file will never appear... combined_image_path = 'first_all_none_firstseg' + fsl_suffix if not os.path.isfile(combined_image_path): if 'SGE_ROOT' in os.environ: app.console( 'FSL FIRST job has been submitted to SGE; awaiting completion') app.console( '(note however that FIRST may fail, and hence this script may hang indefinitely)' ) file.waitFor(combined_image_path) else: app.error( 'FSL FIRST has failed; not all structures were segmented successfully (check ' + path.toTemp('first.logs', False) + ')') # Convert FIRST meshes to partial volume images pve_image_list = [] for struct in sgm_structures: pve_image_path = 'mesh2pve_' + struct + '.mif' vtk_in_path = 'first-' + struct + '_first.vtk' vtk_temp_path = struct + '.vtk' run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path + ' -transform first2real T1.nii') run.command('mesh2pve ' + vtk_temp_path + ' ' + fast_t1_input + ' ' + pve_image_path) pve_image_list.append(pve_image_path) pve_cat = ' '.join(pve_image_list) run.command('mrmath ' + pve_cat + ' sum - | mrcalc - 1.0 -min all_sgms.mif') # Looks like FAST in 5.0 ignores FSLOUTPUTTYPE when writing the PVE images # Will have to wait and see whether this changes, and update the script accordingly if fast_cmd == 'fast': fast_suffix = fsl_suffix else: fast_suffix = '.nii.gz' # Combine the tissue images into the 5TT format within the script itself # Step 1: Run LCC on the WM image run.command( 'mrthreshold ' + fast_output_prefix + '_pve_2' + fast_suffix + ' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit' ) # Step 2: Generate the images in the same fashion as the 5ttgen command run.command('mrcalc ' + fast_output_prefix + '_pve_0' + fast_suffix + ' remove_unconnected_wm_mask.mif -mult csf.mif') run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif') run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_output_prefix + '_pve_1' + fast_suffix + ' ' + fast_output_prefix + '_pve_2' + fast_suffix + ' -add -div multiplier.mif') run.command( 'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif' ) run.command( 'mrcalc ' + fast_output_prefix + '_pve_1' + fast_suffix + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif' ) run.command( 'mrcalc ' + fast_output_prefix + '_pve_2' + fast_suffix + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif' ) run.command('mrcalc 0 wm.mif -min path.mif') run.command( 'mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -stride +2,+3,+4,+1' ) # Use mrcrop to reduce file size (improves caching of image data during tracking) if app.args.nocrop: run.command('mrconvert combined_precrop.mif result.mif') else: run.command( 'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -' )
def execute(): #pylint: disable=unused-variable import math, os from mrtrix3 import app, fsl, image, run if app.isWindows(): app.error('\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows') fsl_path = os.environ.get('FSLDIR', '') if not fsl_path: app.error('Environment variable FSLDIR is not set; please run appropriate FSL configuration script') bet_cmd = fsl.exeName('bet') fast_cmd = fsl.exeName('fast') first_cmd = fsl.exeName('run_first_all') ssroi_cmd = fsl.exeName('standard_space_roi') first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin') if not os.path.isdir(first_atlas_path): app.error('Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager') fsl_suffix = fsl.suffix() sgm_structures = [ 'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal' ] if app.args.sgm_amyg_hipp: sgm_structures.extend([ 'L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp' ]) t1_spacing = image.Header('input.mif').spacing() upsample_for_first = False # If voxel size is 1.25mm or larger, make a guess that the user has erroneously re-gridded their data if math.pow(t1_spacing[0] * t1_spacing[1] * t1_spacing[2], 1.0/3.0) > 1.225: app.warn('Voxel size larger than expected for T1-weighted images (' + str(t1_spacing) + '); ' 'note that ACT does not require re-gridding of T1 image to DWI space, and indeed ' 'retaining the original higher resolution of the T1 image is preferable') upsample_for_first = True run.command('mrconvert input.mif T1.nii -strides -1,+2,+3') fast_t1_input = 'T1.nii' fast_t2_input = '' # Decide whether or not we're going to do any brain masking if os.path.exists('mask.mif'): fast_t1_input = 'T1_masked' + fsl_suffix # Check to see if the mask matches the T1 image if image.match('T1.nii', 'mask.mif'): run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input) mask_path = 'mask.mif' else: app.warn('Mask image does not match input image - re-gridding') run.command('mrtransform mask.mif mask_regrid.mif -template T1.nii -datatype bit') run.command('mrcalc T1.nii mask_regrid.mif -mult ' + fast_t1_input) mask_path = 'mask_regrid.mif' if os.path.exists('T2.nii'): fast_t2_input = 'T2_masked' + fsl_suffix run.command('mrcalc T2.nii ' + mask_path + ' -mult ' + fast_t2_input) elif app.args.premasked: fast_t1_input = 'T1.nii' if os.path.exists('T2.nii'): fast_t2_input = 'T2.nii' else: # Use FSL command standard_space_roi to do an initial masking of the image before BET # Also reduce the FoV of the image # Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation mni_mask_path = os.path.join(fsl_path, 'data', 'standard', 'MNI152_T1_1mm_brain_mask_dil.nii.gz') mni_mask_dilation = 0 if os.path.exists (mni_mask_path): mni_mask_dilation = 4 else: mni_mask_path = os.path.join(fsl_path, 'data', 'standard', 'MNI152_T1_2mm_brain_mask_dil.nii.gz') if os.path.exists (mni_mask_path): mni_mask_dilation = 2 if mni_mask_dilation: run.command('maskfilter ' + mni_mask_path + ' dilate mni_mask.nii -npass ' + str(mni_mask_dilation)) if app.args.nocrop: ssroi_roi_option = ' -roiNONE' else: ssroi_roi_option = ' -roiFOV' run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -maskMASK mni_mask.nii' + ssroi_roi_option, False) else: run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b', False) pre_bet_image = fsl.findImage('T1_preBET') # BET run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix + ' -f 0.15 -R') fast_t1_input = fsl.findImage('T1_BET' + fsl_suffix) if os.path.exists('T2.nii'): if app.args.nocrop: fast_t2_input = 'T2.nii' else: # Just a reduction of FoV, no sub-voxel interpolation going on run.command('mrtransform T2.nii T2_cropped.nii -template ' + fast_t1_input + ' -interp nearest') fast_t2_input = 'T2_cropped.nii' # Finish branching based on brain masking # FAST if fast_t2_input: run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input) else: run.command(fast_cmd + ' ' + fast_t1_input) # FIRST first_input = 'T1.nii' if upsample_for_first: app.warn('Generating 1mm isotropic T1 image for FIRST in hope of preventing failure, since input image is of lower resolution') run.command('mrresize T1.nii T1_1mm.nii -voxel 1.0 -interp sinc') first_input = 'T1_1mm.nii' first_input_brain_extracted_option = '' if app.args.premasked: first_input_brain_extracted_option = ' -b' first_debug_option = '' if not app.cleanup: first_debug_option = ' -d' first_verbosity_option = '' if app.verbosity == 3: first_verbosity_option = ' -v' run.command(first_cmd + ' -m none -s ' + ','.join(sgm_structures) + ' -i ' + first_input + ' -o first' + first_input_brain_extracted_option + first_debug_option + first_verbosity_option) fsl.checkFirst('first', sgm_structures) # Convert FIRST meshes to partial volume images pve_image_list = [ ] progress = app.progressBar('Generating partial volume images for SGM structures', len(sgm_structures)) for struct in sgm_structures: pve_image_path = 'mesh2voxel_' + struct + '.mif' vtk_in_path = 'first-' + struct + '_first.vtk' vtk_temp_path = struct + '.vtk' run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path + ' -transform first2real ' + first_input) run.command('mesh2voxel ' + vtk_temp_path + ' ' + fast_t1_input + ' ' + pve_image_path) pve_image_list.append(pve_image_path) progress.increment() progress.done() run.command('mrmath ' + ' '.join(pve_image_list) + ' sum - | mrcalc - 1.0 -min all_sgms.mif') # Combine the tissue images into the 5TT format within the script itself fast_output_prefix = fast_t1_input.split('.')[0] fast_csf_output = fsl.findImage(fast_output_prefix + '_pve_0') fast_gm_output = fsl.findImage(fast_output_prefix + '_pve_1') fast_wm_output = fsl.findImage(fast_output_prefix + '_pve_2') # Step 1: Run LCC on the WM image run.command('mrthreshold ' + fast_wm_output + ' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit') # Step 2: Generate the images in the same fashion as the old 5ttgen binary used to: # - Preserve CSF as-is # - Preserve SGM, unless it results in a sum of volume fractions greater than 1, in which case clamp # - Multiply the FAST volume fractions of GM and CSF, so that the sum of CSF, SGM, CGM and WM is 1.0 run.command('mrcalc ' + fast_csf_output + ' remove_unconnected_wm_mask.mif -mult csf.mif') run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif') run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_gm_output + ' ' + fast_wm_output + ' -add -div multiplier.mif') run.command('mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif') run.command('mrcalc ' + fast_gm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif') run.command('mrcalc ' + fast_wm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif') run.command('mrcalc 0 wm.mif -min path.mif') run.command('mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -strides +2,+3,+4,+1') # Use mrcrop to reduce file size (improves caching of image data during tracking) if app.args.nocrop: run.command('mrconvert combined_precrop.mif result.mif') else: run.command('mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -')
def execute(): #pylint: disable=unused-variable import math, os from distutils.spawn import find_executable from mrtrix3 import app, fsl, image, MRtrixError, path, run, utils if utils.is_windows(): raise MRtrixError( '\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows' ) fsl_path = os.environ.get('FSLDIR', '') if not fsl_path: raise MRtrixError( 'Environment variable FSLDIR is not set; please run appropriate FSL configuration script' ) bet_cmd = fsl.exe_name('bet') fast_cmd = fsl.exe_name('fast') first_cmd = fsl.exe_name('run_first_all') ssroi_cmd = fsl.exe_name('standard_space_roi') first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin') if not os.path.isdir(first_atlas_path): raise MRtrixError( 'Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager' ) fsl_suffix = fsl.suffix() sgm_structures = [ 'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal' ] if app.ARGS.sgm_amyg_hipp: sgm_structures.extend(['L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp']) t1_spacing = image.Header('input.mif').spacing() upsample_for_first = False # If voxel size is 1.25mm or larger, make a guess that the user has erroneously re-gridded their data if math.pow(t1_spacing[0] * t1_spacing[1] * t1_spacing[2], 1.0 / 3.0) > 1.225: app.warn( 'Voxel size larger than expected for T1-weighted images (' + str(t1_spacing) + '); ' 'note that ACT does not require re-gridding of T1 image to DWI space, and indeed ' 'retaining the original higher resolution of the T1 image is preferable' ) upsample_for_first = True run.command('mrconvert input.mif T1.nii -strides -1,+2,+3') fast_t1_input = 'T1.nii' fast_t2_input = '' # Decide whether or not we're going to do any brain masking if os.path.exists('mask.mif'): fast_t1_input = 'T1_masked' + fsl_suffix # Check to see if the mask matches the T1 image if image.match('T1.nii', 'mask.mif'): run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input) mask_path = 'mask.mif' else: app.warn('Mask image does not match input image - re-gridding') run.command( 'mrtransform mask.mif mask_regrid.mif -template T1.nii -datatype bit' ) run.command('mrcalc T1.nii mask_regrid.mif -mult ' + fast_t1_input) mask_path = 'mask_regrid.mif' if os.path.exists('T2.nii'): fast_t2_input = 'T2_masked' + fsl_suffix run.command('mrcalc T2.nii ' + mask_path + ' -mult ' + fast_t2_input) elif app.ARGS.premasked: fast_t1_input = 'T1.nii' if os.path.exists('T2.nii'): fast_t2_input = 'T2.nii' else: # Use FSL command standard_space_roi to do an initial masking of the image before BET # Also reduce the FoV of the image # Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation mni_mask_path = os.path.join(fsl_path, 'data', 'standard', 'MNI152_T1_1mm_brain_mask_dil.nii.gz') mni_mask_dilation = 0 if os.path.exists(mni_mask_path): mni_mask_dilation = 4 else: mni_mask_path = os.path.join( fsl_path, 'data', 'standard', 'MNI152_T1_2mm_brain_mask_dil.nii.gz') if os.path.exists(mni_mask_path): mni_mask_dilation = 2 try: if mni_mask_dilation: run.command('maskfilter ' + mni_mask_path + ' dilate mni_mask.nii -npass ' + str(mni_mask_dilation)) if app.ARGS.nocrop: ssroi_roi_option = ' -roiNONE' else: ssroi_roi_option = ' -roiFOV' run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -maskMASK mni_mask.nii' + ssroi_roi_option) else: run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b') except run.MRtrixCmdError: pass try: pre_bet_image = fsl.find_image('T1_preBET') except MRtrixError: app.warn('FSL script \'standard_space_roi\' did not complete successfully' + \ ('' if find_executable('dc') else ' (possibly due to program \'dc\' not being installed') + '; ' + \ 'attempting to continue by providing un-cropped image to BET') pre_bet_image = 'T1.nii' # BET run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix + ' -f 0.15 -R') fast_t1_input = fsl.find_image('T1_BET' + fsl_suffix) if os.path.exists('T2.nii'): if app.ARGS.nocrop: fast_t2_input = 'T2.nii' else: # Just a reduction of FoV, no sub-voxel interpolation going on run.command('mrtransform T2.nii T2_cropped.nii -template ' + fast_t1_input + ' -interp nearest') fast_t2_input = 'T2_cropped.nii' # Finish branching based on brain masking # FAST if fast_t2_input: run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input) else: run.command(fast_cmd + ' ' + fast_t1_input) # FIRST first_input = 'T1.nii' if upsample_for_first: app.warn( 'Generating 1mm isotropic T1 image for FIRST in hope of preventing failure, since input image is of lower resolution' ) run.command('mrgrid T1.nii regrid T1_1mm.nii -voxel 1.0 -interp sinc') first_input = 'T1_1mm.nii' first_brain_extracted_option = '' if app.ARGS.premasked: first_brain_extracted_option = ' -b' first_debug_option = '' if not app.DO_CLEANUP: first_debug_option = ' -d' first_verbosity_option = '' if app.VERBOSITY == 3: first_verbosity_option = ' -v' run.command(first_cmd + ' -m none -s ' + ','.join(sgm_structures) + ' -i ' + first_input + ' -o first' + first_brain_extracted_option + first_debug_option + first_verbosity_option) fsl.check_first('first', sgm_structures) # Convert FIRST meshes to partial volume images pve_image_list = [] progress = app.ProgressBar( 'Generating partial volume images for SGM structures', len(sgm_structures)) for struct in sgm_structures: pve_image_path = 'mesh2voxel_' + struct + '.mif' vtk_in_path = 'first-' + struct + '_first.vtk' vtk_temp_path = struct + '.vtk' run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path + ' -transform first2real ' + first_input) run.command('mesh2voxel ' + vtk_temp_path + ' ' + fast_t1_input + ' ' + pve_image_path) pve_image_list.append(pve_image_path) progress.increment() progress.done() run.command(['mrmath', pve_image_list, 'sum', '-', '|', \ 'mrcalc', '-', '1.0', '-min', 'all_sgms.mif']) # Combine the tissue images into the 5TT format within the script itself fast_output_prefix = fast_t1_input.split('.')[0] fast_csf_output = fsl.find_image(fast_output_prefix + '_pve_0') fast_gm_output = fsl.find_image(fast_output_prefix + '_pve_1') fast_wm_output = fsl.find_image(fast_output_prefix + '_pve_2') # Step 1: Run LCC on the WM image run.command( 'mrthreshold ' + fast_wm_output + ' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit' ) # Step 2: Generate the images in the same fashion as the old 5ttgen binary used to: # - Preserve CSF as-is # - Preserve SGM, unless it results in a sum of volume fractions greater than 1, in which case clamp # - Multiply the FAST volume fractions of GM and CSF, so that the sum of CSF, SGM, CGM and WM is 1.0 run.command('mrcalc ' + fast_csf_output + ' remove_unconnected_wm_mask.mif -mult csf.mif') run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif') run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_gm_output + ' ' + fast_wm_output + ' -add -div multiplier.mif') run.command( 'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif' ) run.command( 'mrcalc ' + fast_gm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif' ) run.command( 'mrcalc ' + fast_wm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif' ) run.command('mrcalc 0 wm.mif -min path.mif') run.command( 'mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -strides +2,+3,+4,+1' ) # Crop to reduce file size (improves caching of image data during tracking) if app.ARGS.nocrop: run.function(os.rename, 'combined_precrop.mif', 'result.mif') else: run.command( 'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrgrid combined_precrop.mif crop result.mif -mask -' ) run.command('mrconvert result.mif ' + path.from_user(app.ARGS.output), mrconvert_keyval=path.from_user(app.ARGS.input), force=app.FORCE_OVERWRITE)
def execute(): import os from distutils.spawn import find_executable from mrtrix3 import app, file, fsl, image, path, run if app.isWindows(): app.error( '\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows' ) fsl_path = os.environ.get('FSLDIR', '') if not fsl_path: app.error( 'Environment variable FSLDIR is not set; please run appropriate FSL configuration script' ) ssroi_cmd = 'standard_space_roi' if not find_executable(ssroi_cmd): ssroi_cmd = 'fsl5.0-standard_space_roi' if not find_executable(ssroi_cmd): app.error( 'Could not find FSL program standard_space_roi; please verify FSL install' ) bet_cmd = 'bet' if not find_executable(bet_cmd): bet_cmd = 'fsl5.0-bet' if not find_executable(bet_cmd): app.error( 'Could not find FSL program bet; please verify FSL install') fast_cmd = 'fast' if not find_executable(fast_cmd): fast_cmd = 'fsl5.0-fast' if not find_executable(fast_cmd): app.error( 'Could not find FSL program fast; please verify FSL install') first_cmd = 'run_first_all' if not find_executable(first_cmd): first_cmd = "fsl5.0-run_first_all" if not find_executable(first_cmd): app.error( 'Could not find FSL program run_first_all; please verify FSL install' ) first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin') if not os.path.isdir(first_atlas_path): app.error( 'Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager' ) fsl_suffix = fsl.suffix() sgm_structures = [ 'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal' ] if app.args.sgm_amyg_hipp: sgm_structures.extend(['L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp']) run.command('mrconvert input.mif T1.nii -stride -1,+2,+3') fast_t1_input = 'T1.nii' fast_t2_input = '' # Decide whether or not we're going to do any brain masking if os.path.exists('mask.mif'): fast_t1_input = 'T1_masked' + fsl_suffix # Check to see if the mask matches the T1 image if image.match('T1.nii', 'mask.mif'): run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input) mask_path = 'mask.mif' else: app.warn('Mask image does not match input image - re-gridding') run.command( 'mrtransform mask.mif mask_regrid.mif -template T1.nii') run.command('mrcalc T1.nii mask_regrid.mif ' + fast_t1_input) mask_path = 'mask_regrid.mif' if os.path.exists('T2.nii'): fast_t2_input = 'T2_masked' + fsl_suffix run.command('mrcalc T2.nii ' + mask_path + ' -mult ' + fast_t2_input) elif app.args.premasked: fast_t1_input = 'T1.nii' if os.path.exists('T2.nii'): fast_t2_input = 'T2.nii' else: # Use FSL command standard_space_roi to do an initial masking of the image before BET # Also reduce the FoV of the image # Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation mni_mask_path = os.path.join(fsl_path, 'data', 'standard', 'MNI152_T1_1mm_brain_mask_dil.nii.gz') mni_mask_dilation = 0 if os.path.exists(mni_mask_path): mni_mask_dilation = 4 else: mni_mask_path = os.path.join( fsl_path, 'data', 'standard', 'MNI152_T1_2mm_brain_mask_dil.nii.gz') if os.path.exists(mni_mask_path): mni_mask_dilation = 2 if mni_mask_dilation: run.command('maskfilter ' + mni_mask_path + ' dilate mni_mask.nii -npass ' + str(mni_mask_dilation)) if app.args.nocrop: ssroi_roi_option = ' -roiNONE' else: ssroi_roi_option = ' -roiFOV' run.command( ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -maskMASK mni_mask.nii' + ssroi_roi_option, False) else: run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b', False) pre_bet_image = fsl.findImage('T1_preBET') # BET run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix + ' -f 0.15 -R') fast_t1_input = fsl.findImage('T1_BET' + fsl_suffix) if os.path.exists('T2.nii'): if app.args.nocrop: fast_t2_input = 'T2.nii' else: # Just a reduction of FoV, no sub-voxel interpolation going on run.command('mrtransform T2.nii T2_cropped.nii -template ' + fast_t1_input + ' -interp nearest') fast_t2_input = 'T2_cropped.nii' # Finish branching based on brain masking # FAST if fast_t2_input: run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input) else: run.command(fast_cmd + ' ' + fast_t1_input) # FIRST first_input_is_brain_extracted = '' if app.args.premasked: first_input_is_brain_extracted = ' -b' run.command(first_cmd + ' -m none -s ' + ','.join(sgm_structures) + ' -i T1.nii -o first' + first_input_is_brain_extracted) fsl.checkFirst('first', sgm_structures) # Convert FIRST meshes to partial volume images pve_image_list = [] for struct in sgm_structures: pve_image_path = 'mesh2pve_' + struct + '.mif' vtk_in_path = 'first-' + struct + '_first.vtk' vtk_temp_path = struct + '.vtk' run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path + ' -transform first2real T1.nii') run.command('mesh2pve ' + vtk_temp_path + ' ' + fast_t1_input + ' ' + pve_image_path) pve_image_list.append(pve_image_path) pve_cat = ' '.join(pve_image_list) run.command('mrmath ' + pve_cat + ' sum - | mrcalc - 1.0 -min all_sgms.mif') # Combine the tissue images into the 5TT format within the script itself fast_output_prefix = fast_t1_input.split('.')[0] fast_csf_output = fsl.findImage(fast_output_prefix + '_pve_0') fast_gm_output = fsl.findImage(fast_output_prefix + '_pve_1') fast_wm_output = fsl.findImage(fast_output_prefix + '_pve_2') # Step 1: Run LCC on the WM image run.command( 'mrthreshold ' + fast_wm_output + ' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit' ) # Step 2: Generate the images in the same fashion as the old 5ttgen binary used to: # - Preserve CSF as-is # - Preserve SGM, unless it results in a sum of volume fractions greater than 1, in which case clamp # - Multiply the FAST volume fractions of GM and CSF, so that the sum of CSF, SGM, CGM and WM is 1.0 run.command('mrcalc ' + fast_csf_output + ' remove_unconnected_wm_mask.mif -mult csf.mif') run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif') run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_gm_output + ' ' + fast_wm_output + ' -add -div multiplier.mif') run.command( 'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif' ) run.command( 'mrcalc ' + fast_gm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif' ) run.command( 'mrcalc ' + fast_wm_output + ' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif' ) run.command('mrcalc 0 wm.mif -min path.mif') run.command( 'mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -stride +2,+3,+4,+1' ) # Use mrcrop to reduce file size (improves caching of image data during tracking) if app.args.nocrop: run.command('mrconvert combined_precrop.mif result.mif') else: run.command( 'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -' )