Source code for nighres.laminar.laminar_iterative_smoothing

import os
import numpy as np
import nibabel as nb
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, _check_available_memory

[docs]def laminar_iterative_smoothing(profile_surface_image, intensity_image, fwhm_mm, roi_mask_image=None, save_data=False, overwrite=False, output_dir=None, file_name=None): '''Smoothing data on multiple intracortical layers Parameters ----------- data_image: niimg Image from which data should be sampled profile_surface_image: niimg 4D image containing levelset representations of different intracortical surfaces on which data should be sampled fwhm_mm: float Full width half maximum distance to use in smoothing (in mm) roi_mask_image: niimg, optional Mask image defining a region of interest to restrict the smoothing save_data: bool Save output data to file (default is False) overwrite: bool Overwrite existing results (default is False) output_dir: str, optional Path to desired output directory, will be created if it doesn't exist file_name: str, optional Desired base name for output files with file extension (suffixes will be added) Returns ----------- dict Dictionary collecting outputs under the following keys (suffix of output files in brackets) * result (niimg): smoothed intensity image (_lis-smooth) Notes ---------- Original Java module by Pierre-Louis Bazin Important: this method assumes isotropic voxels ''' print('\nLaminar iterative smoothing') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, intensity_image) smoothed_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lis-smooth')) if overwrite is False \ and os.path.isfile(smoothed_file) : print("skip computation (use existing results)") output = {"result": smoothed_file} return output # start VM if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # initate class smoother = nighresjava.LaminarIterativeSmoothing() # load the data surface_img = load_volume(profile_surface_image) surface_data = surface_img.get_data() layers = surface_data.shape[3]-1 intensity_img = load_volume(intensity_image) intensity_data = intensity_img.get_data() hdr = intensity_img.header aff = intensity_img.affine resolution = [x.item() for x in hdr.get_zooms()] dimensions = intensity_data.shape if (roi_mask_image!=None) : roi_mask_data = load_volume(data_image).get_data() else : roi_mask_data = None # pass inputs smoother.setIntensityImage(nighresjava.JArray('float')( (intensity_data.flatten('F')).astype(float))) smoother.setProfileSurfaceImage(nighresjava.JArray('float')( (surface_data.flatten('F')).astype(float))) smoother.setResolutions(resolution[0], resolution[1], resolution[2]) smoother.setDimensions(dimensions[0], dimensions[1], dimensions[2]) smoother.setLayers(layers) if (len(dimensions)>3) : smoother.set4thDimension(dimensions[3]) else : smoother.set4thDimension(1) if (roi_mask_data!=None): smoother.setROIMask(nighresjava.JArray('int')( (roi_mask_data.flatten('F')).astype(int).tolist())) smoother.setFWHMmm(float(fwhm_mm)) # execute class try: smoother.execute() except: # if the Java module fails, reraise the error it throws print("\n The underlying Java code did not execute cleanly: ") print(sys.exc_info()[0]) raise return # collecting outputs smoothed_data = np.reshape(np.array( smoother.getSmoothedIntensityImage(), dtype=np.float32), dimensions, 'F') hdr['cal_max'] = np.nanmax(smoothed_data) smoothed = nb.Nifti1Image(smoothed_data, aff, hdr) if save_data: save_volume(smoothed_file, smoothed) return {"result": smoothed_file} else: return {"result": smoothed}