% paths studydir = 'C:\Users\Katka\Desktop\sikorova\20200803_140853_celer_1_1\19\pdata\1\'; % root folder for data from all study subjects subject_list = {''}; % cell array of subject sub-folders within the root folder studydir % preprocessing parameters preprocess_options.format = 'Bruker'; % input image format ('dicom' or 'nifti') (default: 'dicom') preprocess_options.navg = 2; % number of DKI series (default: 2); each DKI series must contain a b = 0 image followed by diffusion-weighted images; all series must use identical acquisition protocols (i.e., same diffusion weightings and gradient directions) preprocess_options.extra_b0 = 1; % whether (1) or not (0) there is an additional b = 0 series (default: 1); used when preprocess_options.format = 'dicom' preprocess_options.coreg_flag = 1; % whether (1) or not (0) to perform 6-parameter rigid-body coregistration between diffusion-weighted images (default: 1); used when preprocess_options.format = 'dicom' preprocess_options.series_description = {'DKI1' 'DKI2' 'DKI_B0'}; % list of dicom series descriptions; used when preprocess_options.format = 'dicom' preprocess_options.fn_nii = '4d.nii'; % four-dimensional nifti image file name; no coregistration is performed between the diffusion-weighted images; used when preprocess_options.format = 'nifti'; the first image must be the b = 0 image % imaging diffusion weightings and directions bval = [0 300 600 900 1200 1500]; ndir = 30; % number of gradient directions; if a scalar value is specified, the number of gradient directions for all b values is set to the scalar; otherwise, a 1-by-(nbval-1) vector should be specified, with each element of the vector corresponding to the number of gradient directions for a nonzero b-value fn_gradients = 'fn_gradient.dat'; % text file containing the gradient vectors; each row corresponds to a gradient vector; if a different gradient set was used for each b-value, a 1-by-(nbval-1) cell array of file names should be specified, with each cell specifying the gradient file name for the corresponding nonzero b-value %fn_gradients = 'C:\Program Files\DKE\fn_gradient.dat'; idx_gradients{1} = {1:30}; % indices of gradient directions to be used for DKI map estimation; unacceptable diffusion-weighted images (e.g., those affected by motion- or patient table vibration-induced diffusion signal loss) can be excluded here; there must be as many cells as there are nonzero b-values, with each cell corresponding to a nonzero b-value idx_gradients{2} = {1:30}; idx_gradients{3} = {1:30}; idx_gradients{4} = {1:30}; idx_gradients{5} = {1:30}; idx_1st_img = 1; % index of the first diffusion-weighted nifti image for each b-value (typically 0 or 1) (default: 1); used when preprocess_options.format = 'dicom'; this is rarely changed % constraints on directional kurtoses Kmin = 0; % constraint on minimum directional kurtosis (cf. Tabesh et al. (2011)) (default: 0); this is rarely changed NKmax = 3; % parameter defining the constraint on maximum directional kurtosis (cf. Eq. 6 in Tabesh et al. (2011)); this is rarely changed % thresholds on output kurtosis maps Kmin_final = 0; % lower (Kmin_final) and upper (Kmax_final) thresholds applied to output (mean, axial, and radial) kurtosis maps (default: Kmin_final = 0, Kmax_final = 3) Kmax_final = 3; % tissue/background segmentation T = 50; % background threshold (default: 50); voxels with b = 0 intensity values above this threshold (after optional connected component analysis) are processed find_brain_mask_flag = 1; % whether (1) or not (0) to apply connected component analysis to refine the binary brain mask obtained by applying threshold T to b = 0 image (default: 1) % DKI fitting method and parameters dki_method.no_tensor = 0; % whether (1) or not (0) to estimate parametric maps using directional signal fits instead of the default tensor fit of Tabesh et al. (2011) (default: 0); directional fits only allow estimation of mean diffusivity and mean kurtosis; not recommended unless the number of acquired gradient directions is less than 15 dki_method.linear_weighting = 1; % unweighted (0) or weighted (1) linear least-squares (default: 1) dki_method.linear_constrained = 1; % unconstrained (0) or constrained (1) linear least-squares (cf. Tabesh et al. (2011)) (default: 1) dki_method.nonlinear = 0; % unconstrained nonlinear least-squares (default: 0) dki_method.linear_violations = 0; % whether (1) or not (0) to generate maps of constraint violations (default: 0); intensity of each voxel in the violation maps represents the proportion of constraints on directional diffusivities ('d_viol' map) and kurtoses ('kmin_viol' and 'kmax_viol' maps) violated by the unconstrained linear least-squares solution; the unconstrained solution used to obtain the violation maps will depend on dki_method.linear_weighting and dki_method.robust_option parameters dki_method.robust_option = 0; % robust fitting option (default: 0): (0) do not use robust fitting; (1) RESTORE-type algorithm (outlier detection and removal followed by tensor refitting) with a user-supplied 'noise tolerance level' (dki_method.noise_tolerance) expressed as a fraction of the diffusion signal magnitude dki_method.noise_tolerance = 0.09; % threshold for outlier detection (used when dki_method.robust_option = 1); the diffusion signal for a gradient direction is declared an outlier if abs(log(diffusion signal) - log(predicted diffusion signal)) > dki_method.noise_tolerance % DTI fitting method and parameters dti_method.dti_flag = 0; % whether (1) or not (0) to estimate DTI parametric maps based on the DTI (in addition to DKI) signal model (default: 0) dti_method.dti_only = 0; % whether (1) or not (0) to only estimate DTI parametric maps (no DKI map estimation) (default: 0) dti_method.no_tensor = 0; % whether (1) or not (0) to estimate parametric maps using directional signal fits instead of the default diffusion tensor fit (default: 0); directional fits only allow estimation of mean diffusivity; not recommended unless the number of acquired gradient directions is less than 6 dti_method.linear_weighting = 1; % unweighted (0) or weighted (1) linear least-squares (default: 1); weighting is based on the diffusion signal magnitude dti_method.b_value = [0 300 600 900 1200 1500]; % vector of nonzero b-values used for DTI map estimation; multiple b-values are supported, e.g., [1000 2000] dti_method.directions = {1:30 1:30 1:30 1:30 1:30}; % indices of gradient directions to be used for DTI map estimation; unacceptable diffusion-weighted images (e.g., those affected by with motion- or patient table vibration-induced diffusion signal loss) can be excluded here; there must be as many cells as there are nonzero b-values, with each cell corresponding to a nonzero b-value; indices are relative to the indices specified in idx_gradients dti_method.robust_option = 0; % robust fitting option (default: 0): (0) do not use robust fitting; (1) RESTORE-type algorithm (outlier detection and removal followed by tensor refitting) with a user-supplied 'noise tolerance level' (dti_method.noise_tolerance) expressed as a fraction of the diffusion signal level dti_method.noise_tolerance = 0.09; % threshold for outlier detection (used when dti_method.robust_option = 1); the diffusion signal for a gradient direction is declared an outlier if abs(log(diffusion signal) - log(predicted diffusion signal)) > dti_method.noise_tolerance % diffusion-weighted image filtering fwhm_img = 1.25 * [2.7 2.7 2.7]; % full width at half maximum (FWHM) (in mm) of Gaussian kernel for smoothing diffusion-weighted images (default: 1.25 * voxel size); a zero value indicates no smoothing % Rician noise subtraction fn_noise = ''; % user-supplied map of Rician noise level (default: '' (i.e., none)); noise subtraction is based on the method of McGibney and Smith (1993); estimated signal s_hat at each voxel is obtained as s_hat = (s^2 - n^2)^0.5, where s is the measured signal and n is the noise level at that voxel fwhm_noise = 0; % FWHM (in mm) of Gaussian kernel for smoothing noise image; a zero value indicates no smoothing (default: 0) % parametric map filtering median_filter_method = 2; % selective median filtering applied to voxels that violate the minimum directional kurtosis constraints (default: 2): (0) no filtering; (1) weak filtering (voxels with less than 15 unviolated constraints will be filtered); (2) strong filtering (voxels with any constraint violation will be filtered) % parametric map interpolation map_interpolation_method.flag = 1; % whether (1) or not (0) to interpolate the parametric maps (default: 1) map_interpolation_method.order = 1; % interpolation polynomial order: (0) nearest neighbor; (1) trilinear; (2 and up) higher order (default: 1) map_interpolation_method.resolution = 1; % target resolution (isotropic) in mm for the interpolated maps (default: 1 mm)