.. include:: links.rst =========================== Processing pipeline details =========================== *PETPrep* adapts its pipeline depending on what data and metadata are available and are used as the input. Certain processing steps will run only when the required metadata is available in the input dataset. Before any subject-level workflow is initialized, *PETPrep* checks whether each selected subject has the required PET and T1w inputs. Subjects missing PET and/or T1w data are skipped, and a warning is issued at the start of the run. Processing continues for the remaining valid subjects. A (very) high-level view of the simplest pipeline (for a single dataset with only a single tracer and single baseline) is presented below: .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.tests import mock_config from petprep.workflows.base import init_single_subject_wf with mock_config(): wf = init_single_subject_wf('01') .. note:: Each node in this workflow is either a processing node or a sub-workflow. Several conventions appear in this workflow that will be apparent throughout PETPrep. * ``inputnode``\s are special nodes that provide the runtime-generated inputs to a workflow. These are like function "arguments". There are corresponding ``outputnode``\s in most other workflows, which are like function return values. * Workflows end with ``_wf``, and are generated by a function of the form ``init_{workflow}_wf``. For example, ``anat_preproc_wf`` is a sub-workflow that is generated by the :func:`~smriprep.workflows.anatomical.init_anat_preproc_wf` (see below). Because each tracer/session/task/run of PET data is processed separately, :func:`~petprep.workflows.pet.base.init_pet_wf` names the resulting workflows using input parameters, resulting in names such as ``pet_task_{task}_run_{run}_wf``. * Datasinks begin with ``ds_``, and save files to the output directory. This is in contrast to most nodes, which save their outputs to the working directory. ``ds_report_`` nodes indicate that the node is saving text and figures for generating reports, rather than processed data. * When a name appears in parentheses, such as ``(reports)`` in ``about (reports)`` it is the module where the interface is defined. In this case, ``about`` is an :class:`~petprep.interfaces.reports.AboutSummary`, found in the :mod:`petprep.interfaces.reports` module. Preprocessing of structural MRI ------------------------------- The anatomical sub-workflow begins by constructing an average image by conforming all found T1w images to RAS orientation and a common voxel size, and, in the case of multiple images, averages them into a single reference template (see `Longitudinal processing`_). .. workflow:: :graph2use: orig :simple_form: yes from niworkflows.utils.spaces import Reference, SpatialReferences from smriprep.workflows.anatomical import init_anat_preproc_wf spaces=SpatialReferences([ ('MNI152Lin', {}), ('fsaverage', {'density': '10k'}), ('T1w', {}), ('fsnative', {}) ]) spaces.checkpoint() wf = init_anat_preproc_wf( bids_root='.', freesurfer=True, hires=False, longitudinal=False, omp_nthreads=1, output_dir='.', skull_strip_mode='force', skull_strip_template=Reference('MNI152NLin2009cAsym'), spaces=spaces, skull_strip_fixed_seed=False, t1w=['sub-01/anat/sub-01_T1w.nii.gz'], t2w=[], msm_sulc=True, precomputed={}, ) .. important:: Occasionally, openly shared datasets may contain preprocessed anatomical images as if they are unprocessed. In the case of brain-extracted (skull-stripped) T1w images, attempting to perform brain extraction again will often have poor results and may cause *PETPrep* to crash. *PETPrep* can attempt to detect these cases using a heuristic to check if the T1w image is already masked. This must be explicitly requested with ``--skull-strip-t1w auto``. If this heuristic fails, and you know your images are skull-stripped, you can skip brain extraction with ``--skull-strip-t1w skip``. Likewise, if you know your images are not skull-stripped and the heuristic incorrectly determines that they are, you can force skull stripping with ``--skull-strip-t1w force``, which is the current default behavior. See also *sMRIPrep*'s :py:func:`~smriprep.workflows.anatomical.init_anat_preproc_wf`. .. _t1preproc_steps: Brain extraction, brain tissue segmentation and spatial normalization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Then, the T1w reference is skull-stripped using a Nipype implementation of the ``antsBrainExtraction.sh`` tool (ANTs), which is an atlas-based brain extraction workflow: .. workflow:: :graph2use: orig :simple_form: yes from niworkflows.anat.ants import init_brain_extraction_wf wf = init_brain_extraction_wf() Once the brain mask is computed, FSL ``fast`` is utilized for brain tissue segmentation. PETPrep includes a single figure overlaying the brain mask (red), and tissue boundaries (blue = gray/white; magenta = tissue/CSF): .. figure:: _static/sub-01_dseg.svg Brain extraction and segmentation report Finally, spatial normalization to standard spaces is performed using ANTs' ``antsRegistration`` in a multiscale, mutual-information based, nonlinear registration scheme. See :ref:`output-spaces` for information about how standard and nonstandard spaces can be set to resample the preprocessed data onto the final output spaces. .. figure:: _static/T1MNINormalization.svg Animation showing spatial normalization of T1w onto the ``MNI152NLin2009cAsym`` template. Cost function masking during spatial normalization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When processing images from patients with focal brain lesions (e.g., stroke, tumor resection), it is possible to provide a lesion mask to be used during spatial normalization to standard space [Brett2001]_. ANTs will use this mask to minimize warping of healthy tissue into damaged areas (or vice-versa). Lesion masks should be binary NIfTI images (damaged areas = 1, everywhere else = 0) in the same space and resolution as the T1w image, and use the ``_roi`` suffix, for example, ``sub-001_label-lesion_roi.nii.gz``. This file should be placed in the ``sub-*/anat`` directory of the BIDS dataset to be run through *PETPrep*. Because lesion masks are not currently part of the BIDS specification, it is also necessary to include a ``.bidsignore`` file in the root of your dataset directory. This will prevent `bids-validator `_ from complaining that your dataset is not valid BIDS, which prevents *PETPrep* from running. Your ``.bidsignore`` file should include the following line:: *lesion_roi.nii.gz .. note:: The lesion masking instructions in this section predate the release of BIDS Derivatives. As of BIDS 1.4.0, the recommended naming convention is:: manual_masks/ └─ sub-001/ └─ anat/ ├─ sub-001_desc-tumor_mask.nii.gz └─ sub-001_desc-tumor_mask.json In an upcoming version of PETPrep, we will search for lesion masks as pre-computed derivatives. Until this is supported, we will continue to look for the ``_roi`` suffix. Longitudinal processing ~~~~~~~~~~~~~~~~~~~~~~~ In the case of multiple T1w images (across sessions and/or runs), *PETPrep* provides a few choices on how to generate the reference anatomical space. If ``--subject-anatomical-reference first-lex`` is used, all T1w images are merged into a single template image using FreeSurfer's `mri_robust_template`_, aligned to the first image (determined lexicographically by session label). This is the default behavior. If ``--subject-anatomical-reference unbiased`` is used, all T1w images are merged into an *unbiased* template, equidistant from all source images. For two images, the additional cost of estimating an unbiased template is trivial, but aligning three or more images is too expensive to justify being the default behavior. If ``--subject-anatomical-reference sessionwise`` is used, a reference template will be generated for each session independently. If multiple T1w images are found within a session, the images will be aligned to the first image, sorted lexicographically, from that session. The deprecated ``--longitudinal`` flag remains available as an alias for ``--subject-anatomical-reference unbiased``. .. note:: The preprocessed T1w image defines the ``T1w`` space. In the case of multiple T1w images, this space may not be precisely aligned with any of the original images. Reconstructed surfaces and PET datasets will be registered to the ``T1w`` space, and not to the input images. .. _workflows_surface: Surface preprocessing ~~~~~~~~~~~~~~~~~~~~~ *PETPrep* uses FreeSurfer_ to reconstruct surfaces from T1w/T2w structural images. If enabled, several steps in the *PETPrep* pipeline are added or replaced. All surface preprocessing may be disabled with the ``--fs-no-reconall`` flag. .. note:: Surface processing will be skipped if the outputs already exist. In order to bypass reconstruction in *PETPrep*, place existing reconstructed subjects in ``/sourcedata/freesurfer`` prior to the run, or specify an external subjects directory with the ``--fs-subjects-dir`` flag. *PETPrep* will perform any missing ``recon-all`` steps, but will not perform any steps whose outputs already exist. If FreeSurfer reconstruction is performed, the reconstructed subject is placed in ``/sourcedata/freesurfer/sub-/`` (see :ref:`fsderivs`). Surface reconstruction is performed in three phases. The first phase initializes the subject with T1w and T2w (if available) structural images and performs basic reconstruction (``autorecon1``) with the exception of skull-stripping. Skull-stripping is skipped since the brain mask :ref:`calculated previously ` is injected into the appropriate location for FreeSurfer. For example, a subject with only one session with T1w and T2w images would be processed by the following command:: $ recon-all -sd /freesurfer -subjid sub- \ -i /sub-/anat/sub-_T1w.nii.gz \ -T2 /sub-/anat/sub-_T2w.nii.gz \ -autorecon1 \ -noskullstrip The second phase imports the brainmask calculated in the `Preprocessing of structural MRI`_ sub-workflow. The final phase resumes reconstruction, using the T2w image to assist in finding the pial surface, if available. See :py:func:`~smriprep.workflows.surfaces.init_autorecon_resume_wf` for details. Reconstructed white and pial surfaces are included in the report. .. figure:: _static/reconall.svg Surface reconstruction (FreeSurfer) FreeSurfer `submillimeter reconstruction`_ is disabled by default in *PETPrep* because PET resampling and GTM time-activity-curve extraction can become very memory intensive on submillimeter anatomical grids. Use ``--submm-recon`` to opt in for T1w images with voxel sizes less than 1mm in all dimensions (rounding to nearest .1mm). The ``--no-submm-recon`` flag may be used to explicitly keep this default behavior. If T2w or FLAIR images are available, and you do not want them included in FreeSurfer reconstruction, use ``--ignore t2w`` or ``--ignore flair``, respectively. ``lh.midthickness`` and ``rh.midthickness`` surfaces are created in the subject ``surf/`` directory, corresponding to the surface half-way between the gray/white boundary and the pial surface. The ``smoothwm``, ``midthickness``, ``pial`` and ``inflated`` surfaces are also converted to GIFTI_ format and adjusted to be compatible with multiple software packages, including FreeSurfer and the `Connectome Workbench`_. .. note:: GIFTI surface outputs are aligned to the FreeSurfer ``T1.mgz`` image, which may differ from the T1w space in some cases, to maintain compatibility with the FreeSurfer directory. Any measures sampled to the surface take into account any difference in these images. .. workflow:: :graph2use: orig :simple_form: yes from smriprep.workflows.surfaces import init_surface_recon_wf wf = init_surface_recon_wf( omp_nthreads=1, hires=False, precomputed={}, fs_no_resume=False, ) See also *sMRIPrep*'s :py:func:`~smriprep.workflows.surfaces.init_surface_recon_wf` Refinement of the brain mask ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Typically, the original brain mask calculated with ``antsBrainExtraction.sh`` will contain some inaccuracies including small amounts of MR signal from outside the brain. Based on the tissue segmentation of FreeSurfer (located in ``mri/aseg.mgz``) and only when the :ref:`Surface Processing ` step has been executed, *PETPrep* replaces the brain mask with a refined one that derives from the ``aseg.mgz`` file as described in :py:class:`~niworkflows.interfaces.freesurfer.RefineBrainMask`. PET preprocessing ------------------ *PETPrep* performs a series of steps to preprocess :abbr:`PET (positron emission tomography)` data. Broadly, these are split into fit and transform stages. Stage 1 simultaneously estimates head motion and the reference image. The following figures show the overall workflow graph and the ``pet_fit_wf`` subgraph: :py:func:`~petprep.workflows.pet.base.init_pet_wf` .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.tests import mock_config from petprep import config from petprep.workflows.pet.base import init_pet_wf with mock_config(): pet_file = config.execution.bids_dir / 'sub-01' / 'pet' \ / 'sub-01_task-mixedgamblestask_run-01_pet.nii.gz' wf = init_pet_wf(pet_series=[str(pet_file)]) .. _pet_fit: :py:func:`~petprep.workflows.pet.fit.init_pet_fit_wf` .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.tests import mock_config from petprep import config from petprep.workflows.pet.fit import init_pet_fit_wf with mock_config(): pet_file = config.execution.bids_dir / 'sub-01' / 'pet' \ / 'sub-01_task-mixedgamblestask_pet.nii.gz' wf = init_pet_fit_wf(pet_series=[str(pet_file)]) Preprocessing of :abbr:`PET (positron emission tomography)` files is split into multiple sub-workflows described below. .. _pet_ref: PET reference image estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.reference.init_raw_petref_wf` .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.pet.reference import init_raw_petref_wf wf = init_raw_petref_wf() This workflow estimates a reference image for a :abbr:`PET (positron emission tomography)` series according to the strategy requested with :option:`--petref`: * ``auto`` (default) evaluates all candidate reference strategies and selects the option that yields the strongest PET-to-T1w registration score. * ``template`` uses the motion-correction template built during head-motion estimation. When head-motion correction is disabled, ``template`` automatically falls back to ``twa`` while emitting a warning. * ``twa`` computes a time-weighted average of the motion-corrected series, preserving dynamic information when later frames carry more counts. * ``sum`` produces a summed image of the motion-corrected series. * ``first5min`` weights only the first five minutes of the acquisition, which can be helpful for tracers whose early dynamics resemble perfusion. During automatic reference selection, the workflow falls back to the first frame if no frames overlap the initial 5-minute window. Head-motion estimation ~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.hmc.init_pet_hmc_wf` .. workflow:: :graph2use: colored :simple_form: yes from petprep.workflows.pet import init_pet_hmc_wf wf = init_pet_hmc_wf( mem_gb=1, omp_nthreads=1) Using the previously :ref:`estimated reference scan `, a robust-template approach estimates head motion. All frames are aligned to one another with FreeSurfer's ``mri_robust_template`` and ``mri_robust_register`` to create a within-run template and compute rigid-body transforms for every :abbr:`PET (positron emission tomography)` volume. The resulting transforms and the six rotation and translation parameters for each time-step are passed on to the :ref:`confounds workflow `. The smoothing kernel width and onset of motion estimation can be customized via the :option:`--hmc-fwhm` and :option:`--hmc-start-time` command line options. By default, PETPrep initializes registration with the frame showing the highest tracer uptake after this start time. An explicit zero-based frame index can be provided with :option:`--hmc-init-frame`. Adding :option:`--hmc-init-frame-fix` keeps the chosen frame fixed during robust template estimation and disables iterations to reduce runtime. A 10 mm FWHM Gaussian is applied and estimation begins at 120 s unless otherwise specified. Pre-processed PET in native space ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.fit.init_pet_native_wf` .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.tests import mock_config from petprep import config from petprep.workflows.pet.fit import init_pet_native_wf with mock_config(): pet_file = config.execution.bids_dir / 'sub-01' / 'pet' \ / 'sub-01_task-mixedgamblestask_run-01_pet.nii.gz' wf = init_pet_native_wf(pet_series=[str(pet_file)]) A new *preproc* :abbr:`PET (positron emission tomography)` series is generated from the original data in the original space. All volumes in the :abbr:`PET (positron emission tomography)` series are resampled in their native space by concatenating the mappings found in previous correction workflows (:abbr:`HMC (head-motion correction)`) for a one-shot interpolation process. Interpolation uses cubic B-spline interpolation. .. _pet_reg: PET to T1w registration ~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.registration.init_pet_reg_wf` .. workflow:: :graph2use: hierarchical :simple_form: yes from petprep.workflows.pet.registration import init_pet_reg_wf wf = init_pet_reg_wf( omp_nthreads=1, pet2anat_dof=6, mem_gb=3, pet2anat_method='auto', ) The PET reference volume is aligned to the skull-stripped anatomical image using the method selected via :option:`--pet2anat-method`. The anatomical image is first cropped with FSL's ``robustfov`` and masked to focus the alignment on brain tissue (ANTs receives the unmasked cropped image together with its mask). Use :option:`--pet2anat-no-anat-crop` to bypass anatomical ``robustfov`` and register against the full anatomical field of view. In ``auto`` mode, when cropping is enabled and all cropped registration scores are weak for a PET reference, PETPrep evaluates an uncropped anatomical fallback and keeps it if the score improves; disabling anatomical cropping also disables this crop-triggered fallback. By default, the workflow runs ``auto`` mode (``--pet2anat-method auto``), which executes both FreeSurfer and ANTs registrations in parallel, applies the resulting transforms to the PET reference, computes a similarity score within the T1w brain mask, and selects the best-performing transform. Alternative manual modes include FreeSurfer's ``mri_coreg`` (``--pet2anat-method mri_coreg``), FreeSurfer's ``mri_robust_register`` (``--pet2anat-method robust``), and ANTs rigid registration (``--pet2anat-method ants``). The ``robust`` and ``ants`` options are limited to rigid-body alignment (6 DoF). The resulting affine is converted to ITK format for downstream application, along with its inverse. Resampling PET runs onto standard spaces ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.apply.init_pet_volumetric_resample_wf` .. workflow:: :graph2use: colored :simple_form: yes from petprep.workflows.pet.apply import init_pet_volumetric_resample_wf wf = init_pet_volumetric_resample_wf( mem_gb={'resampled': 1}, ) This sub-workflow concatenates the transforms calculated upstream (see `Head-motion estimation`_, `PET to T1w registration`_, and an anatomical-to-standard transform from `Preprocessing of structural MRI`_) to map the :abbr:`PET (positron emission tomography)` series from native space to the standard spaces given by the ``--output-spaces`` argument (see :ref:`output-spaces`). It also maps the T1w-based mask to each of those standard spaces. Transforms are concatenated and applied all at once, with one cubic B-spline interpolation step, so as little information is lost as possible. The output space grid can be specified using modifiers to the ``--output-spaces`` argument. PET sampled to FreeSurfer surfaces ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.resampling.init_pet_surf_wf` .. workflow:: :graph2use: colored :simple_form: yes from petprep.workflows.pet import init_pet_surf_wf wf = init_pet_surf_wf( mem_gb=1, surface_spaces=['fsnative', 'fsaverage5'], medial_surface_nan=False, metadata={}, output_dir='.', ) If FreeSurfer processing is enabled, the motion-corrected PET series (after single shot resampling to T1w space) is sampled to the surface by averaging across the cortical ribbon. Specifically, at each vertex, the segment normal to the white-matter surface, extending to the pial surface, is sampled at 6 intervals and averaged. Surfaces are generated for the "subject native" surface, as well as transformed to the ``fsaverage`` template space. All surface outputs are in GIFTI format. HCP Grayordinates ~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.resampling.init_pet_fsLR_resampling_wf` .. workflow:: :graph2use: orig :simple_form: yes from petprep.workflows.pet.resampling import init_pet_fsLR_resampling_wf wf = init_pet_fsLR_resampling_wf( grayord_density='92k', omp_nthreads=1, mem_gb=1, ) If CIFTI output is enabled, the motion-corrected PET timeseries (in T1w space) is resampled onto the subject-native surface, optionally using the `HCP Pipelines`_'s "goodvoxels" masking method to exclude voxels with local peaks of temporal variation. After dilating the surface-sampled time series to fill sampling holes, the result is resampled to the ``fsLR`` mesh (with the left and right hemisphere aligned). These workflows make use of various `Connectome Workbench`_ functions. These surfaces are then combined with corresponding volumetric timeseries to create a CIFTI-2 file. Segmentation workflows ~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.segmentation.init_segmentation_wf` *PETPrep* ships with optional segmentation routines that can be selected via the ``--seg`` command-line argument. Supported values include ``gtm`` (the default), ``brainstem``, ``thalamicNuclei``, ``hippocampusAmygdala``, ``wm``, ``raphe`` and ``limbic``. Atlas-based segmentations may also be selected with ``--seg``; these include ``HOCPA``, ``MASSP20`` and the ``Schaefer2018Parcels7Networks`` and ``Schaefer2018Parcels17Networks`` variants described in :doc:`usage`. Tool-based segmentations rely on pretrained models distributed with ``petprep.data.segmentation``. The first time a particular model is requested it will be automatically downloaded to the *PETPrep* cache directory, so ensure sufficient disk space is available. Atlas-based segmentations are warped from their template space into the subject's anatomical space; when an atlas is selected, *PETPrep* automatically adds the atlas template to ``--output-spaces``. Each segmentation produces a labeled NIfTI image ``seg-_dseg.nii.gz`` and a TSV table of region volumes ``seg-_morph.tsv`` saved under the ``anat/`` derivatives folder. Reference masks generated via ``--ref-mask-name`` create a mask image and a similar ``label-_desc-ref_morph.tsv`` file. These TSVs share the same columns as the segmentation morph tables: ``index``, ``name`` and ``volume-mm3``. For example, the raphe segmentation can be enabled with:: petprep run /data/bids /data/out --seg raphe Resulting outputs will appear in ``sub-*/anat/`` alongside the anatomical derivatives and can be used in downstream analyses. .. _pet_confounds: Confounds estimation ~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.confounds.init_pet_confs_wf` .. workflow:: :graph2use: colored :simple_form: yes from petprep.workflows.pet.confounds import init_pet_confs_wf wf = init_pet_confs_wf( name="discover_wf", mem_gb=1, regressors_dvars_th=1.5, regressors_fd_th=0.5, ) Given a motion-corrected PET, a brain mask, estimated motion parameters and a segmentation, the `discover_wf` sub-workflow calculates potential confounds per volume. Calculated confounds include the mean global signal, mean tissue class signal, tCompCor, aCompCor, Frame-wise Displacement, 6 motion parameters, DVARS, and spike regressors. Partial volume correction ~~~~~~~~~~~~~~~~~~~~~~~~ :py:func:`~petprep.workflows.pet.pvc.init_pet_pvc_wf` .. workflow:: :graph2use: colored :simple_form: yes from petprep.workflows.tests import mock_config from petprep.workflows.pet.pvc import init_pet_pvc_wf with mock_config(): wf = init_pet_pvc_wf( name="pvc_wf", tool="PETPVC", method="GTM" ) Partial volume correction (PVC) is a process that attempts to correct for the partial volume effects that occur when a single voxel contains multiple tissue types. This is particularly important in PET imaging, where the signal from different tissues (e.g., gray matter, white matter, and cerebrospinal fluid) can overlap within a voxel, leading to inaccurate quantification of tracer uptake. *PETPrep* provides a PVC workflow using established tools such as PETPVC_ [Thomas2016]_ or PETSurfer_ [Greve2014]_ [Greve2016]_. PVC is enabled by providing ``--pvc-tool``, ``--pvc-method`` and ``--pvc-psf`` together. PETPVC_ provides methods such as ``GTM``, ``LABBE``, ``RL``, ``VC``, ``RBV``, ``LABBE+RBV``, ``RBV+VC``, ``RBV+RL``, ``LABBE+RBV+VC``, ``LABBE+RBV+RL``, ``STC``, ``MTC``, ``LABBE+MTC``, ``MTC+VC``, ``MTC+RL``, ``LABBE+MTC+VC``, ``LABBE+MTC+RL``, ``IY``, ``IY+VC``, ``IY+RL``, ``MG``, ``MG+VC`` and ``MG+RL``. PETSurfer_ provides ``GTM``, ``MG``, ``RBV`` and ``AGTM``. The point spread function is supplied with ``--pvc-psf`` as either one FWHM value or three values. PETSurfer_ PVC requires the ``gtm`` segmentation, whereas ``PETPVC`` can use any *PETPrep* segmentation. When PVC is enabled, the corrected PET series feeds the downstream resampling and output workflows, and the filenames include the ``pvc-`` entity. References ---------- .. [Brett2001] Brett M, Leff AP, Rorden C, Ashburner J (2001) Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking. NeuroImage 14(2) doi:`10.006/nimg.2001.0845 `_. .. [Thomas2016] Thomas, B. A., Cuplov, V., Bousse, A., Mendes, A., Thielemans, K., Hutton, B. F., & Erlandsson, K. (2016). PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. Physics in Medicine & Biology, 61(22), 7975. .. [Greve2014] Greve, D. N., Svarer, C., Fisher, P. M., Feng, L., Hansen, A. E., Baare, W., ... & Knudsen, G. M. (2014). Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. Neuroimage, 92, 225-236. .. [Greve2016] Greve, D. N., Salat, D. H., Bowen, S. L., Izquierdo-Garcia, D., Schultz, A. P., Catana, C., ... & Johnson, K. A. (2016). Different partial volume correction methods lead to different conclusions: An 18 F-FDG-PET study of aging. NeuroImage, 132, 334-343.