From 38efcc1a2e4b1ae5747eb2f7f9dbf0dbdfd80d31 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Tue, 26 Oct 2021 19:34:56 +0200 Subject: [PATCH 1/9] doc: bring implementation details to the foreground of documentation --- docs/index.rst | 1 + docs/methods.rst | 237 +++++++++++++++++++++++++++++ sdcflows/workflows/fit/fieldmap.py | 96 +----------- sdcflows/workflows/fit/pepolar.py | 12 +- sdcflows/workflows/fit/syn.py | 71 --------- 5 files changed, 240 insertions(+), 177 deletions(-) create mode 100644 docs/methods.rst diff --git a/docs/index.rst b/docs/index.rst index c6b5f1a1cf..466e0d4dab 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,5 +7,6 @@ Contents :maxdepth: 3 installation + methods api changes diff --git a/docs/methods.rst b/docs/methods.rst new file mode 100644 index 0000000000..2d5ccba088 --- /dev/null +++ b/docs/methods.rst @@ -0,0 +1,237 @@ +Methods and implementation +========================== +*SDCFlows* defines a clear :abbr:`API (application programming interface)` that divides +"*the problem of susceptibility distortions (SD)*" into two stages: + +#. **Estimation**: + the MRI acquisitions in the protocol for :abbr:`SD (susceptibility distortions)` are + discovered and preprocessed to estimate a map of B\ :sub:`0` non-uniformity in Hz (:math:`\Delta B_0`). + The theory behind these distortions is well described in the literature [Jezzard1995]_ [Hutton2002]_ (Fig. 1). + *SDCFlows* builds on freely-available software to implement three major strategies for estimating + :math:`\Delta B_0` (Eq. 1). + These strategies are described below, and implemented within :py:mod:`sdcflows.fit`\ . + +#. **Application**: + the B-Spline basis coefficients used to represent the estimated :math:`\Delta B_0` map mapped into the + target EPI scan to be corrected, and a displacement field in NIfTI format that is compatible with ANTs + is interpolated from the B-Spline basis. + The voxel location error along the PE will be proportional to :math:`\Delta B_0 \cdot T_\text{ro}`, + where :math:`T_\text{ro}` is the *total readout time* of the target EPI (Fig. 1). + +Fieldmap estimation: theory and methods +--------------------------------------- +The displacement suffered by every voxel along the :abbr:`PE (phase-encoding)` direction +can be derived from Eq. (2) of [Hutton2002]_: + +.. math:: + + \Delta_\text{PE} (i, j, k) = \gamma \cdot \Delta B_0 (i, j, k) \cdot T_\text{ro}, + \label{eq:fieldmap-1}\tag{1} + +where +:math:`\Delta_\text{PE} (i, j, k)` is the *voxel-shift map* (VSM) along the *PE* direction, +:math:`\gamma` is the gyromagnetic ratio of the H proton in Hz/T +(:math:`\gamma = 42.576 \cdot 10^6 \, \text{Hz} \cdot \text{T}^\text{-1}`), +:math:`\Delta B_0 (i, j, k)` is the *fieldmap variation* in T (Tesla), and +:math:`T_\text{ro}` is the readout time of one slice of the EPI dataset +we want to correct for distortions. + +Let :math:`V` represent the «*fieldmap in Hz*» (or equivalently, +«*voxel-shift-velocity map*» as Hz are equivalent to voxels/s), with +:math:`V(i,j,k) = \gamma \cdot \Delta B_0 (i, j, k)`, then, introducing +the voxel zoom along the phase-encoding direction, :math:`s_\text{PE}`, +we obtain the nonzero component of the associated displacements field +:math:`\Delta D_\text{PE} (i, j, k)` that unwarps the target EPI dataset: + +.. math:: + + \Delta D_\text{PE} (i, j, k) = V(i, j, k) \cdot T_\text{ro} \cdot s_\text{PE}. + \label{eq:fieldmap-2}\tag{2} + +.. _sdc_direct_b0 : + +Direct B0 mapping sequences +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Some MR schemes such as :abbr:`SEI (spiral-echo imaging)` can directly +reconstruct an estimate of *the fieldmap in Hz*, :math:`V(i,j,k)`. +These *fieldmaps* are described with more detail `here +`__. + +This corresponds to `this section of the BIDS specification +`__. + +.. _sdc_phasediff : + +Phase-difference B0 estimation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain +:math:`\Delta_\text{PE} (i, j, k)` in Eq. :math:`\eqref{eq:fieldmap-1}` can be +calculated from two subsequient :abbr:`GRE (Gradient-Recalled Echo)` echoes, +via eq. (1) of [Hutton2002]_: + +.. math:: + + \Delta B_0 (i, j, k) = \frac{\Delta \Theta (i, j, k)}{2\pi \cdot \gamma \, \Delta\text{TE}}, + \label{eq:fieldmap-3}\tag{3} + +where +:math:`\Delta \Theta (i, j, k)` is the phase-difference map in radians, +and :math:`\Delta\text{TE}` is the elapsed time between the two GRE echoes. + +For simplicity, the «*voxel-shift-velocity map*» :math:`V(i,j,k)`, which we +can introduce in Eq. :math:`\eqref{eq:fieldmap-2}` to directly obtain +the displacements field, can be obtained as: + +.. math:: + + V(i, j, k) = \frac{\Delta \Theta (i, j, k)}{2\pi \cdot \Delta\text{TE}}. + \label{eq:fieldmap-4}\tag{4} + +This calculation is further complicated by the fact that :math:`\Theta_i` +(and therefore, :math:`\Delta \Theta`) are clipped (or *wrapped*) within +the range :math:`[0 \dotsb 2\pi )`. +It is necessary to find the integer number of offsets that make a region +continuously smooth with its neighbors (*phase-unwrapping*, [Jenkinson2003]_). + +This corresponds to `this section of the BIDS specification +`__. +Some scanners produce one ``phasediff`` map, where the drift between the two echos has +already been calculated (see `the corresponding section of BIDS +`__). + +.. _sdc_pepolar : + +:abbr:`PEPOLAR (Phase Encoding POLARity)` techniques +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +This corresponds to `this section of the BIDS specification +`__. + +.. _sdc_fieldmapless : + +Fieldmap-less approaches +~~~~~~~~~~~~~~~~~~~~~~~~ +Many studies acquired (especially with legacy MRI protocols) do not have any +information to estimate susceptibility-derived distortions. +In the absence of data with the specific purpose of estimating the :math:`B_0` +inhomogeneity map, researchers resort to nonlinear registration to an +«*anatomically correct*» map of the same individual (normally acquired with +:abbr:`T1w (T1-weighted)`, or :abbr:`T2w (T2-weighted)` sequences). +One of the most prominent proposals of this approach is found in [Studholme2000]_. + +*SDCFlows* includes an (experimental) procedure, based on nonlinear image registration +with ANTs' symmetric normalization (SyN) technique. +This workflow takes a skull-stripped :abbr:`T1w (T1-weighted)` image and +a reference :abbr:`EPI (Echo-Planar Imaging)` image, and estimates a field of nonlinear +displacements that accounts for susceptibility-derived distortions. +To more accurately estimate the warping on typically distorted regions, this +implementation uses an average :math:`B_0` mapping described in [Treiber2016]_. +The implementation is a variation on those developed in [Huntenburg2014]_ and +[Wang2017]_. + +The process is divided in two steps. +First, the two images to be aligned (anatomical and one or more EPI sources) are prepared for +registration, including a linear pre-alignment of both, calculation of a 3D EPI *reference* map, +intensity/histogram enhancement, and *deobliquing* (meaning, for images were the physical +coordinates axes and the data array axes are not aligned, the physical coordinates are +rotated to align with the data array). +Such a preprocessing is implemented in :py:func:`init_syn_preprocessing_wf`. +Second, the outputs of the preprocessing workflow are fed into :py:func:`init_syn_sdc_wf`, +which executes the nonlinear, SyN registration. +To aid the *Mattes* mutual information cost function, the registration scheme is set up +in *multi-channel* mode, and laplacian-filtered derivatives of both anatomical and EPI +reference are introduced as a second registration channel. +The optimization gradients of the registration process are weighted, so that deformations +effectively possible only along the :abbr:`PE (phase-encoding)` axis. +Given that ANTs' registration framework performs on physical coordinates, it is necessary +that input images are not *oblique*. +The anatomical image is used as *fixed image*, and therefore, the registration process +estimates the transformation function from *unwarped* (anatomically *correct*) coordinates +to *distorted* coordinates. +If fed into ``antsApplyTransforms``, the resulting transform will effectively *unwarp* a distorted +EPI given as input into its *unwarped* mapping. +The estimated transform is then converted into a :math:`B_0` fieldmap in Hz, which can be +stored within the derivatives folder. + +.. danger :: Experimental feature + + This procedure is experimental, and the outcomes should be scrutinized one-by-one + and used with caution. + Feedback will be enthusiastically received. + +Estimation tooling +~~~~~~~~~~~~~~~~~~ +The workflows provided by :py:mod:`sdcflows.fit` make use of several utilities. +Perhaps, the centerpiece of these tools is the fieldmap representation with B-Splines +(:py:mod:`sdcflows.interfaces.bspline`). +B-Splines are very adequate to plausibly smooth the fieldmap and provide a compact +representation of the field with fewer parameters. +This representation is also more accurate in the case the images that were used for estimation +are not aligned with the target images to be corrected because the fieldmap is not directly +interpolated in the projection, but rather, the position of the coefficients in space is +updated and then the fieldmap reconstructed. + +Unwarping the distorted data +---------------------------- +:py:mod:`sdcflows.apply` contains workflows to project fieldmaps represented by B-Spline +basis into the space of the target :abbr:`EPI (echo-planar imaging)` data. + +Discovering fieldmaps on a BIDS dataset +--------------------------------------- +To ease the implementation of higher-level pipelines integrating :abbr:`SDC (susceptibility distortion correction)`, +*SDCFlows* provides :py:func:`sdcflows.utils.wrangler.find_estimators`. + +Explicit specification with ``B0FieldIdentifier`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If one or more ``B0FieldIdentifier``\ (s) are set within the input metadata (following BIDS' specifications), +then corresponding estimators will be built from the available input data. + +Implicit specification with ``IntendedFor`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +In the case no ``B0FieldIdentifier``\ (s) are defined, then *SDCFlows* will try to identify as many fieldmap +estimators as possible within the dataset following a set of heuristics. +Then, those estimators may be linked to target :abbr:`EPI (echo-planar imaging)` data by interpreting the +``IntendedFor`` field if available. + +Fieldmap-less by registration to a T1-weighted image +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If none of the two previous options yielded any workable estimation strategy, and provided that +the argument ``fmapless`` is set to ``True``, then :py:func:`sdcflows.utils.wrangler.find_estimators` +will attempt to find :abbr:`BOLD (blood-oxygen level-dependent)` or :abbr:`DWI (diffusion-weighted imaging)` +instances within single sessions that are consistent in :abbr:`PE (phase-encoding)` direction and +*total readout time*, assumming they have been acquired with the same shimming settings. + +If one or more anatomical images are found, and if the search for candidate +:abbr:`BOLD (blood-oxygen level-dependent)` or :abbr:`DWI (diffusion-weighted imaging)` data +yields results, then corresponding fieldmap-less estimators are set up. + +It is possible to force the fieldmap-less estimation by passing ``force_fmapless=True`` to the +:py:func:`sdcflows.utils.wrangler.find_estimators` utility. + +References +---------- +.. [Jezzard1995] Jezzard, P. & Balaban, R. S. Correction for geometric distortion in + echo planar images from B0 field variations. Magn. Reson. Med. 34:65–73, 1995 + doi:`10.1002/mrm.1910340111 `__. +.. [Hutton2002] Hutton et al., Image Distortion Correction in fMRI: A Quantitative + Evaluation, NeuroImage 16(1):217-240, 2002. doi:`10.1006/nimg.2001.1054 + `__. +.. [Jenkinson2003] Jenkinson, M. (2003) Fast, automated, N-dimensional phase-unwrapping + algorithm. MRM 49(1):193-197. doi:`10.1002/mrm.10354 + `__. +.. [Studholme2000] Studholme et al. (2000) Accurate alignment of functional EPI data to + anatomical MRI using a physics-based distortion model, + IEEE Trans Med Imag 19(11):1115-1127, 2000, doi: `10.1109/42.896788 + `__. +.. [Treiber2016] Treiber, J. M. et al. (2016) Characterization and Correction + of Geometric Distortions in 814 Diffusion Weighted Images, + PLoS ONE 11(3): e0152472. doi:`10.1371/journal.pone.0152472 + `_. +.. [Wang2017] Wang S, et al. (2017) Evaluation of Field Map and Nonlinear + Registration Methods for Correction of Susceptibility Artifacts + in Diffusion MRI. Front. Neuroinform. 11:17. + doi:`10.3389/fninf.2017.00017 + `_. +.. [Huntenburg2014] Huntenburg, J. M. (2014) `Evaluating Nonlinear + Coregistration of BOLD EPI and T1w Images + `__, + Berlin: Master Thesis, Freie Universität. diff --git a/sdcflows/workflows/fit/fieldmap.py b/sdcflows/workflows/fit/fieldmap.py index 202ce3c168..accfdb9dae 100644 --- a/sdcflows/workflows/fit/fieldmap.py +++ b/sdcflows/workflows/fit/fieldmap.py @@ -20,101 +20,7 @@ # # https://www.nipreps.org/community/licensing/ # -r""" -Processing phase-difference and *directly measured* :math:`B_0` maps. - -Theory -~~~~~~ -The displacement suffered by every voxel along the phase-encoding (PE) direction -can be derived from Eq. (2) of [Hutton2002]_: - -.. math:: - - \Delta_\text{PE} (i, j, k) = \gamma \cdot \Delta B_0 (i, j, k) \cdot T_\text{ro}, - \label{eq:fieldmap-1}\tag{1} - -where -:math:`\Delta_\text{PE} (i, j, k)` is the *voxel-shift map* (VSM) along the *PE* direction, -:math:`\gamma` is the gyromagnetic ratio of the H proton in Hz/T -(:math:`\gamma = 42.576 \cdot 10^6 \, \text{Hz} \cdot \text{T}^\text{-1}`), -:math:`\Delta B_0 (i, j, k)` is the *fieldmap variation* in T (Tesla), and -:math:`T_\text{ro}` is the readout time of one slice of the EPI dataset -we want to correct for distortions. - -Let :math:`V` represent the «*fieldmap in Hz*» (or equivalently, -«*voxel-shift-velocity map*» as Hz are equivalent to voxels/s), with -:math:`V(i,j,k) = \gamma \cdot \Delta B_0 (i, j, k)`, then, introducing -the voxel zoom along the phase-encoding direction, :math:`s_\text{PE}`, -we obtain the nonzero component of the associated displacements field -:math:`\Delta D_\text{PE} (i, j, k)` that unwarps the target EPI dataset: - -.. math:: - - \Delta D_\text{PE} (i, j, k) = V(i, j, k) \cdot T_\text{ro} \cdot s_\text{PE}. - \label{eq:fieldmap-2}\tag{2} - - -.. _sdc_direct_b0 : - -Direct B0 mapping sequences -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Some MR schemes such as :abbr:`SEI (spiral-echo imaging)` can directly -reconstruct an estimate of *the fieldmap in Hz*, :math:`V(i,j,k)`. -These *fieldmaps* are described with more detail `here -`__. - -This corresponds to `this section of the BIDS specification -`__. - -.. _sdc_phasediff : - -Phase-difference B0 estimation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain -:math:`\Delta_\text{PE} (i, j, k)` in Eq. :math:`\eqref{eq:fieldmap-1}` can be -calculated from two subsequient :abbr:`GRE (Gradient-Recalled Echo)` echoes, -via eq. (1) of [Hutton2002]_: - -.. math:: - - \Delta B_0 (i, j, k) = \frac{\Delta \Theta (i, j, k)}{2\pi \cdot \gamma \, \Delta\text{TE}}, - \label{eq:fieldmap-3}\tag{3} - -where -:math:`\Delta \Theta (i, j, k)` is the phase-difference map in radians, -and :math:`\Delta\text{TE}` is the elapsed time between the two GRE echoes. - -For simplicity, the «*voxel-shift-velocity map*» :math:`V(i,j,k)`, which we -can introduce in Eq. :math:`\eqref{eq:fieldmap-2}` to directly obtain -the displacements field, can be obtained as: - -.. math:: - - V(i, j, k) = \frac{\Delta \Theta (i, j, k)}{2\pi \cdot \Delta\text{TE}}. - \label{eq:fieldmap-4}\tag{4} - -This calculation is further complicated by the fact that :math:`\Theta_i` -(and therefore, :math:`\Delta \Theta`) are clipped (or *wrapped*) within -the range :math:`[0 \dotsb 2\pi )`. -It is necessary to find the integer number of offsets that make a region -continuously smooth with its neighbors (*phase-unwrapping*, [Jenkinson2003]_). - -This corresponds to `this section of the BIDS specification -`__. -Some scanners produce one ``phasediff`` map, where the drift between the two echos has -already been calculated (see `the corresponding section of BIDS -`__). - -References ----------- -.. [Hutton2002] Hutton et al., Image Distortion Correction in fMRI: A Quantitative - Evaluation, NeuroImage 16(1):217-240, 2002. doi:`10.1006/nimg.2001.1054 - `__. -.. [Jenkinson2003] Jenkinson, M. (2003) Fast, automated, N-dimensional phase-unwrapping - algorithm. MRM 49(1):193-197. doi:`10.1002/mrm.10354 - `__. - -""" +r"""Processing phase-difference and *directly measured* :math:`B_0` maps.""" from nipype.pipeline import engine as pe from nipype.interfaces import utility as niu diff --git a/sdcflows/workflows/fit/pepolar.py b/sdcflows/workflows/fit/pepolar.py index 7bf8f14415..386dee0ed9 100644 --- a/sdcflows/workflows/fit/pepolar.py +++ b/sdcflows/workflows/fit/pepolar.py @@ -20,17 +20,7 @@ # # https://www.nipreps.org/community/licensing/ # -""" -Datasets with multiple phase encoded directions. - -.. _sdc_pepolar : - -:abbr:`PEPOLAR (Phase Encoding POLARity)` techniques -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This corresponds to `this section of the BIDS specification -`__. - -""" +"""Datasets with multiple phase encoded directions.""" from pkg_resources import resource_filename as _pkg_fname from nipype.pipeline import engine as pe from nipype.interfaces import utility as niu diff --git a/sdcflows/workflows/fit/syn.py b/sdcflows/workflows/fit/syn.py index 7b64b4f6cf..34a7304098 100644 --- a/sdcflows/workflows/fit/syn.py +++ b/sdcflows/workflows/fit/syn.py @@ -32,77 +32,6 @@ >>> nb.Nifti1Image(data, None, None).to_filename( ... tmpdir.join('field.nii.gz').strpath) -.. _sdc_fieldmapless : - -Fieldmap-less approaches -~~~~~~~~~~~~~~~~~~~~~~~~ -Many studies acquired (especially with legacy MRI protocols) do not have any -information to estimate susceptibility-derived distortions. -In the absence of data with the specific purpose of estimating the :math:`B_0` -inhomogeneity map, researchers resort to nonlinear registration to an -«*anatomically correct*» map of the same individual (normally acquired with -:abbr:`T1w (T1-weighted)`, or :abbr:`T2w (T2-weighted)` sequences). -One of the most prominent proposals of this approach is found in [Studholme2000]_. - -*SDCFlows* includes an (experimental) procedure, based on nonlinear image registration -with ANTs' symmetric normalization (SyN) technique. -This workflow takes a skull-stripped :abbr:`T1w (T1-weighted)` image and -a reference :abbr:`EPI (Echo-Planar Imaging)` image, and estimates a field of nonlinear -displacements that accounts for susceptibility-derived distortions. -To more accurately estimate the warping on typically distorted regions, this -implementation uses an average :math:`B_0` mapping described in [Treiber2016]_. -The implementation is a variation on those developed in [Huntenburg2014]_ and -[Wang2017]_. - -The process is divided in two steps. -First, the two images to be aligned (anatomical and one or more EPI sources) are prepared for -registration, including a linear pre-alignment of both, calculation of a 3D EPI *reference* map, -intensity/histogram enhancement, and *deobliquing* (meaning, for images were the physical -coordinates axes and the data array axes are not aligned, the physical coordinates are -rotated to align with the data array). -Such a preprocessing is implemented in :py:func:`init_syn_preprocessing_wf`. -Second, the outputs of the preprocessing workflow are fed into :py:func:`init_syn_sdc_wf`, -which executes the nonlinear, SyN registration. -To aid the *Mattes* mutual information cost function, the registration scheme is set up -in *multi-channel* mode, and laplacian-filtered derivatives of both anatomical and EPI -reference are introduced as a second registration channel. -The optimization gradients of the registration process are weighted, so that deformations -effectively possible only along the :abbr:`PE (phase-encoding)` axis. -Given that ANTs' registration framework performs on physical coordinates, it is necessary -that input images are not *oblique*. -The anatomical image is used as *fixed image*, and therefore, the registration process -estimates the transformation function from *unwarped* (anatomically *correct*) coordinates -to *distorted* coordinates. -If fed into ``antsApplyTransforms``, the resulting transform will effectively *unwarp* a distorted -EPI given as input into its *unwarped* mapping. -The estimated transform is then converted into a :math:`B_0` fieldmap in Hz, which can be -stored within the derivatives folder. - -.. danger :: Experimental feature - - This procedure is experimental, and the outcomes should be scrutinized one-by-one - and used with caution. - Feedback will be enthusiastically received. - -References ----------- -.. [Studholme2000] Studholme et al. (2000) Accurate alignment of functional EPI data to - anatomical MRI using a physics-based distortion model, - IEEE Trans Med Imag 19(11):1115-1127, 2000, doi: `10.1109/42.896788 - `__. -.. [Treiber2016] Treiber, J. M. et al. (2016) Characterization and Correction - of Geometric Distortions in 814 Diffusion Weighted Images, - PLoS ONE 11(3): e0152472. doi:`10.1371/journal.pone.0152472 - `_. -.. [Wang2017] Wang S, et al. (2017) Evaluation of Field Map and Nonlinear - Registration Methods for Correction of Susceptibility Artifacts - in Diffusion MRI. Front. Neuroinform. 11:17. - doi:`10.3389/fninf.2017.00017 - `_. -.. [Huntenburg2014] Huntenburg, J. M. (2014) `Evaluating Nonlinear - Coregistration of BOLD EPI and T1w Images - `__, - Berlin: Master Thesis, Freie Universität. """ from pkg_resources import resource_filename From 4eaf84a1a538edd14db6101321e5e0c87a577e30 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Tue, 26 Oct 2021 21:37:10 +0200 Subject: [PATCH 2/9] enh: expand pepolar and alternatives --- docs/methods.rst | 33 ++++++++++++++++++++++++++++----- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/docs/methods.rst b/docs/methods.rst index 2d5ccba088..e0e59f64f1 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -103,6 +103,11 @@ already been calculated (see `the corresponding section of BIDS :abbr:`PEPOLAR (Phase Encoding POLARity)` techniques ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Alternatively, it is possible to estimate the field by exploiting the symmetry of the +distortion when the PE polarity is reversed. +*SDCFlows* integrates two implementations based on FSL ``topup`` [Andersson2003]_, +and AFNI ``3dQwarp`` [Cox1997]_. + This corresponds to `this section of the BIDS specification `__. @@ -152,12 +157,18 @@ EPI given as input into its *unwarped* mapping. The estimated transform is then converted into a :math:`B_0` fieldmap in Hz, which can be stored within the derivatives folder. -.. danger :: Experimental feature +.. danger :: This procedure is experimental, and the outcomes should be scrutinized one-by-one and used with caution. Feedback will be enthusiastically received. +Other (unsupported) approaches +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +There exist some alternative options to estimate the fieldmap, such as mapping the +point-spread-function [Zaitsev2004]_, or by means of nonlinear registration of brain +surfaces onto the distorted :abbr:`EPI (echo-planar imaging)` data [Esteban2016]_. + Estimation tooling ~~~~~~~~~~~~~~~~~~ The workflows provided by :py:mod:`sdcflows.fit` make use of several utilities. @@ -209,15 +220,21 @@ It is possible to force the fieldmap-less estimation by passing ``force_fmapless References ---------- -.. [Jezzard1995] Jezzard, P. & Balaban, R. S. Correction for geometric distortion in - echo planar images from B0 field variations. Magn. Reson. Med. 34:65–73, 1995 +.. [Jezzard1995] Jezzard, P. & Balaban, R. S. (1995) Correction for geometric distortion in + echo planar images from B0 field variations. Magn. Reson. Med. 34:65–73. doi:`10.1002/mrm.1910340111 `__. -.. [Hutton2002] Hutton et al., Image Distortion Correction in fMRI: A Quantitative - Evaluation, NeuroImage 16(1):217-240, 2002. doi:`10.1006/nimg.2001.1054 +.. [Hutton2002] Hutton et al., (2002) Image Distortion Correction in fMRI: A Quantitative + Evaluation, NeuroImage 16(1):217-240. doi:`10.1006/nimg.2001.1054 `__. .. [Jenkinson2003] Jenkinson, M. (2003) Fast, automated, N-dimensional phase-unwrapping algorithm. MRM 49(1):193-197. doi:`10.1002/mrm.10354 `__. +.. [Andersson2003] Andersson, J. (2003) How to correct susceptibility distortions in spin-echo + echo-planar images: application to diffusion tensor imaging. NeuroImage 20:870–888. + doi:`10.1016/s1053-8119(03)00336-7 `__. +.. [Cox1997] Cox, R. (1997) Software tools for analysis and visualization of fMRI data. NMR Biomed. + 10:171–178, doi:`10.1002/(sici)1099-1492(199706/08)10:4/5%3C171::aid-nbm453%3E3.0.co;2-l + `__. .. [Studholme2000] Studholme et al. (2000) Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model, IEEE Trans Med Imag 19(11):1115-1127, 2000, doi: `10.1109/42.896788 @@ -235,3 +252,9 @@ References Coregistration of BOLD EPI and T1w Images `__, Berlin: Master Thesis, Freie Universität. +.. [Zaitsev2004] Zaitsev, M. (2004) Point spread function mapping with parallel imaging techniques and + high acceleration factors: Fast, robust, and flexible method for echo-planar imaging distortion correction, + MRM 52(5):1156-1166. doi:`10.1002/mrm.20261 `__. +.. [Esteban2016] Esteban, O. (2016) Surface-driven registration method for the structure-informed segmentation + of diffusion MR images. NeuroImage 139:450-461. + doi:`10.1016/j.neuroimage.2016.05.011 `__. 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_static/sdcflows-OHBM21-fig1.png + :width: 500 + .. _sdc_direct_b0 : Direct B0 mapping sequences From e0288c676f8cab5bd8863411f6733ef61586a6ac Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 16:36:01 +0200 Subject: [PATCH 4/9] Apply suggestions from code review Co-authored-by: Eilidh MacNicol <54267723+eilidhmacnicol@users.noreply.github.com> --- docs/methods.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/methods.rst b/docs/methods.rst index b167b47148..a12e50ac88 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -97,7 +97,7 @@ It is necessary to find the integer number of offsets that make a region continuously smooth with its neighbors (*phase-unwrapping*, [Jenkinson2003]_). This corresponds to `this section of the BIDS specification -`__. +`__. Some scanners produce one ``phasediff`` map, where the drift between the two echos has already been calculated (see `the corresponding section of BIDS `__). @@ -189,7 +189,7 @@ Unwarping the distorted data :py:mod:`sdcflows.apply` contains workflows to project fieldmaps represented by B-Spline basis into the space of the target :abbr:`EPI (echo-planar imaging)` data. -Discovering fieldmaps on a BIDS dataset +Discovering fieldmaps in a BIDS dataset --------------------------------------- To ease the implementation of higher-level pipelines integrating :abbr:`SDC (susceptibility distortion correction)`, *SDCFlows* provides :py:func:`sdcflows.utils.wrangler.find_estimators`. @@ -212,7 +212,7 @@ If none of the two previous options yielded any workable estimation strategy, an the argument ``fmapless`` is set to ``True``, then :py:func:`sdcflows.utils.wrangler.find_estimators` will attempt to find :abbr:`BOLD (blood-oxygen level-dependent)` or :abbr:`DWI (diffusion-weighted imaging)` instances within single sessions that are consistent in :abbr:`PE (phase-encoding)` direction and -*total readout time*, assumming they have been acquired with the same shimming settings. +*total readout time*, assuming they have been acquired with the same shimming settings. If one or more anatomical images are found, and if the search for candidate :abbr:`BOLD (blood-oxygen level-dependent)` or :abbr:`DWI (diffusion-weighted imaging)` data From bc7ccda387a6b207f67d73d35cc3f3a529555903 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 18:34:18 +0200 Subject: [PATCH 5/9] Apply suggestions from code review Co-authored-by: Mathias Goncalves --- docs/methods.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/methods.rst b/docs/methods.rst index a12e50ac88..a149806c8a 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -49,7 +49,8 @@ we obtain the nonzero component of the associated displacements field \label{eq:fieldmap-2}\tag{2} .. image:: _static/sdcflows-OHBM21-fig1.png - :width: 500 + :width: 100% + :align: center .. _sdc_direct_b0 : From 8726e6ecf9571434140ed46d42b968a15f886f81 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 19:17:03 +0200 Subject: [PATCH 6/9] enh: move BIDS' spec references below the section's title Co-authored-by: Mathias Goncalves --- docs/methods.rst | 32 ++++++++++++++++++++------------ 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/docs/methods.rst b/docs/methods.rst index a149806c8a..04cf2b491d 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -56,18 +56,29 @@ we obtain the nonzero component of the associated displacements field Direct B0 mapping sequences ~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. admonition:: BIDS Specification + + See `this section of the BIDS specification + `__. + Some MR schemes such as :abbr:`SEI (spiral-echo imaging)` can directly reconstruct an estimate of *the fieldmap in Hz*, :math:`V(i,j,k)`. These *fieldmaps* are described with more detail `here `__. -This corresponds to `this section of the BIDS specification -`__. - .. _sdc_phasediff : Phase-difference B0 estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. admonition:: BIDS Specification + + See `this section of the BIDS specification + `__. + + Some scanners produce one ``phasediff`` map, where the drift between the two echos has + already been calculated, see `the corresponding section of BIDS + `__. + The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain :math:`\Delta_\text{PE} (i, j, k)` in Eq. :math:`\eqref{eq:fieldmap-1}` can be calculated from two subsequient :abbr:`GRE (Gradient-Recalled Echo)` echoes, @@ -97,23 +108,20 @@ the range :math:`[0 \dotsb 2\pi )`. It is necessary to find the integer number of offsets that make a region continuously smooth with its neighbors (*phase-unwrapping*, [Jenkinson2003]_). -This corresponds to `this section of the BIDS specification -`__. -Some scanners produce one ``phasediff`` map, where the drift between the two echos has -already been calculated (see `the corresponding section of BIDS -`__). - .. _sdc_pepolar : :abbr:`PEPOLAR (Phase Encoding POLARity)` techniques ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. admonition:: BIDS Specification + + See `this section of the BIDS specification + `__. + Alternatively, it is possible to estimate the field by exploiting the symmetry of the distortion when the PE polarity is reversed. *SDCFlows* integrates two implementations based on FSL ``topup`` [Andersson2003]_, and AFNI ``3dQwarp`` [Cox1997]_. - -This corresponds to `this section of the BIDS specification -`__. +By default, FSL ``topup`` will be used. .. _sdc_fieldmapless : From dc9cdb991adb1f7c024c880bce4e856f37a5a1e0 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 21:30:26 +0200 Subject: [PATCH 7/9] Apply suggestions from code review Co-authored-by: Chris Markiewicz --- docs/methods.rst | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/methods.rst b/docs/methods.rst index 04cf2b491d..c2974746c9 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -1,7 +1,7 @@ Methods and implementation ========================== *SDCFlows* defines a clear :abbr:`API (application programming interface)` that divides -"*the problem of susceptibility distortions (SD)*" into two stages: +the problem of susceptibility distortions (SD) into two stages: #. **Estimation**: the MRI acquisitions in the protocol for :abbr:`SD (susceptibility distortions)` are @@ -36,8 +36,8 @@ where :math:`T_\text{ro}` is the readout time of one slice of the EPI dataset we want to correct for distortions. -Let :math:`V` represent the «*fieldmap in Hz*» (or equivalently, -«*voxel-shift-velocity map*» as Hz are equivalent to voxels/s), with +Let :math:`V` represent the *fieldmap* in Hz (or equivalently, +*voxel-shift-velocity map*, as Hz are equivalent to voxels/s), with :math:`V(i,j,k) = \gamma \cdot \Delta B_0 (i, j, k)`, then, introducing the voxel zoom along the phase-encoding direction, :math:`s_\text{PE}`, we obtain the nonzero component of the associated displacements field @@ -58,8 +58,9 @@ Direct B0 mapping sequences ~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. admonition:: BIDS Specification - See `this section of the BIDS specification - `__. + See the section `Types of fieldmaps - Case 3: Direct field mapping + `__ + in the BIDS specification. Some MR schemes such as :abbr:`SEI (spiral-echo imaging)` can directly reconstruct an estimate of *the fieldmap in Hz*, :math:`V(i,j,k)`. @@ -72,11 +73,13 @@ Phase-difference B0 estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. admonition:: BIDS Specification - See `this section of the BIDS specification - `__. + See the section `Types of fieldmaps - Case 2: Two phase maps and two magnitude images + `__ + in the BIDS specification. Some scanners produce one ``phasediff`` map, where the drift between the two echos has - already been calculated, see `the corresponding section of BIDS + already been calculated, see the section + `Types of fieldmaps - Case 1: Phase-difference map and at least one magnitude image `__. The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain @@ -148,7 +151,7 @@ The implementation is a variation on those developed in [Huntenburg2014]_ and The process is divided in two steps. First, the two images to be aligned (anatomical and one or more EPI sources) are prepared for registration, including a linear pre-alignment of both, calculation of a 3D EPI *reference* map, -intensity/histogram enhancement, and *deobliquing* (meaning, for images were the physical +intensity/histogram enhancement, and *deobliquing* (meaning, for images where the physical coordinates axes and the data array axes are not aligned, the physical coordinates are rotated to align with the data array). Such a preprocessing is implemented in :py:func:`init_syn_preprocessing_wf`. @@ -184,9 +187,9 @@ surfaces onto the distorted :abbr:`EPI (echo-planar imaging)` data [Esteban2016] Estimation tooling ~~~~~~~~~~~~~~~~~~ The workflows provided by :py:mod:`sdcflows.fit` make use of several utilities. -Perhaps, the centerpiece of these tools is the fieldmap representation with B-Splines +The cornerstone of these tools is the fieldmap representation with B-Splines (:py:mod:`sdcflows.interfaces.bspline`). -B-Splines are very adequate to plausibly smooth the fieldmap and provide a compact +B-Splines are well-suited to plausibly smooth the fieldmap and provide a compact representation of the field with fewer parameters. This representation is also more accurate in the case the images that were used for estimation are not aligned with the target images to be corrected because the fieldmap is not directly From 56de2530ba41540a85441c578c410a9451fe8c08 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 22:22:38 +0200 Subject: [PATCH 8/9] fix: typos, more refs to BIDS specs, acronyms, etc. --- docs/methods.rst | 74 ++++++++++++++++++++++++++++++------------------ 1 file changed, 47 insertions(+), 27 deletions(-) diff --git a/docs/methods.rst b/docs/methods.rst index c2974746c9..6537acdf52 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -8,15 +8,27 @@ the problem of susceptibility distortions (SD) into two stages: discovered and preprocessed to estimate a map of B\ :sub:`0` non-uniformity in Hz (:math:`\Delta B_0`). The theory behind these distortions is well described in the literature [Jezzard1995]_ [Hutton2002]_ (Fig. 1). *SDCFlows* builds on freely-available software to implement three major strategies for estimating - :math:`\Delta B_0` (Eq. 1). - These strategies are described below, and implemented within :py:mod:`sdcflows.fit`\ . + :math:`\Delta B_0` (Eq. :math:`\eqref{eq:fieldmap-1}`). + These strategies are described below, and implemented within :py:mod:`sdcflows.workflows.fit`\ . #. **Application**: the B-Spline basis coefficients used to represent the estimated :math:`\Delta B_0` map mapped into the - target EPI scan to be corrected, and a displacement field in NIfTI format that is compatible with ANTs - is interpolated from the B-Spline basis. - The voxel location error along the PE will be proportional to :math:`\Delta B_0 \cdot T_\text{ro}`, - where :math:`T_\text{ro}` is the *total readout time* of the target EPI (Fig. 1). + target :abbr:`EPI (echo-planar imaging)` scan to be corrected, and a displacement field in NIfTI + format that is compatible with ANTs is interpolated from the B-Spline basis. + The voxel location error along the :abbr:`PE (phase-encoding)` will be proportional to :math:`\Delta B_0 \cdot T_\text{ro}`, + where :math:`T_\text{ro}` is the *total readout time* of the target :abbr:`EPI (echo-planar imaging)` (Fig. 1). + The implementation of these workflows is found in the submodule :py:mod:`sdcflows.workflows.apply`\ . + +.. figure:: _static/sdcflows-OHBM21-fig1.png + :width: 100% + :align: center + + **Figure 1**. Susceptibility distortions in a nutshell + +.. admonition:: BIDS Specification + + See the section `Echo-planar imaging and *B0* mapping + `__. Fieldmap estimation: theory and methods --------------------------------------- @@ -29,11 +41,11 @@ can be derived from Eq. (2) of [Hutton2002]_: \label{eq:fieldmap-1}\tag{1} where -:math:`\Delta_\text{PE} (i, j, k)` is the *voxel-shift map* (VSM) along the *PE* direction, +:math:`\Delta_\text{PE} (i, j, k)` is the *voxel-shift map* (VSM) along the :abbr:`PE (phase-encoding)` direction, :math:`\gamma` is the gyromagnetic ratio of the H proton in Hz/T (:math:`\gamma = 42.576 \cdot 10^6 \, \text{Hz} \cdot \text{T}^\text{-1}`), :math:`\Delta B_0 (i, j, k)` is the *fieldmap variation* in T (Tesla), and -:math:`T_\text{ro}` is the readout time of one slice of the EPI dataset +:math:`T_\text{ro}` is the readout time of one slice of the :abbr:`EPI (echo-planar imaging)` dataset we want to correct for distortions. Let :math:`V` represent the *fieldmap* in Hz (or equivalently, @@ -41,17 +53,13 @@ Let :math:`V` represent the *fieldmap* in Hz (or equivalently, :math:`V(i,j,k) = \gamma \cdot \Delta B_0 (i, j, k)`, then, introducing the voxel zoom along the phase-encoding direction, :math:`s_\text{PE}`, we obtain the nonzero component of the associated displacements field -:math:`\Delta D_\text{PE} (i, j, k)` that unwarps the target EPI dataset: +:math:`\Delta D_\text{PE} (i, j, k)` that unwarps the target :abbr:`EPI (echo-planar imaging)` dataset: .. math:: \Delta D_\text{PE} (i, j, k) = V(i, j, k) \cdot T_\text{ro} \cdot s_\text{PE}. \label{eq:fieldmap-2}\tag{2} -.. image:: _static/sdcflows-OHBM21-fig1.png - :width: 100% - :align: center - .. _sdc_direct_b0 : Direct B0 mapping sequences @@ -84,7 +92,7 @@ Phase-difference B0 estimation The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain :math:`\Delta_\text{PE} (i, j, k)` in Eq. :math:`\eqref{eq:fieldmap-1}` can be -calculated from two subsequient :abbr:`GRE (Gradient-Recalled Echo)` echoes, +calculated from two subsequent :abbr:`GRE (Gradient-Recalled Echo)` echoes, via eq. (1) of [Hutton2002]_: .. math:: @@ -117,11 +125,11 @@ continuously smooth with its neighbors (*phase-unwrapping*, [Jenkinson2003]_). ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. admonition:: BIDS Specification - See `this section of the BIDS specification - `__. + See the section `Types of fieldmaps - Case 4: Multiple phase encoded directions ("pepolar") + `__. Alternatively, it is possible to estimate the field by exploiting the symmetry of the -distortion when the PE polarity is reversed. +distortion when the :abbr:`PE (phase-encoding)` polarity is reversed. *SDCFlows* integrates two implementations based on FSL ``topup`` [Andersson2003]_, and AFNI ``3dQwarp`` [Cox1997]_. By default, FSL ``topup`` will be used. @@ -149,16 +157,18 @@ The implementation is a variation on those developed in [Huntenburg2014]_ and [Wang2017]_. The process is divided in two steps. -First, the two images to be aligned (anatomical and one or more EPI sources) are prepared for -registration, including a linear pre-alignment of both, calculation of a 3D EPI *reference* map, +First, the two images to be aligned (anatomical and one or more :abbr:`EPI (echo-planar imaging)` sources) are prepared for +registration, including a linear pre-alignment of both, calculation of a 3D :abbr:`EPI (echo-planar imaging)` *reference* map, intensity/histogram enhancement, and *deobliquing* (meaning, for images where the physical coordinates axes and the data array axes are not aligned, the physical coordinates are rotated to align with the data array). -Such a preprocessing is implemented in :py:func:`init_syn_preprocessing_wf`. -Second, the outputs of the preprocessing workflow are fed into :py:func:`init_syn_sdc_wf`, +Such a preprocessing is implemented in +:py:func:`~sdcflows.workflows.fit.syn.init_syn_preprocessing_wf`. +Second, the outputs of the preprocessing workflow are fed into +:py:func:`~sdcflows.workflows.fit.syn.init_syn_sdc_wf`, which executes the nonlinear, SyN registration. To aid the *Mattes* mutual information cost function, the registration scheme is set up -in *multi-channel* mode, and laplacian-filtered derivatives of both anatomical and EPI +in *multi-channel* mode, and laplacian-filtered derivatives of both anatomical and :abbr:`EPI (echo-planar imaging)` reference are introduced as a second registration channel. The optimization gradients of the registration process are weighted, so that deformations effectively possible only along the :abbr:`PE (phase-encoding)` axis. @@ -168,7 +178,7 @@ The anatomical image is used as *fixed image*, and therefore, the registration p estimates the transformation function from *unwarped* (anatomically *correct*) coordinates to *distorted* coordinates. If fed into ``antsApplyTransforms``, the resulting transform will effectively *unwarp* a distorted -EPI given as input into its *unwarped* mapping. +:abbr:`EPI (echo-planar imaging)` given as input into its *unwarped* mapping. The estimated transform is then converted into a :math:`B_0` fieldmap in Hz, which can be stored within the derivatives folder. @@ -186,7 +196,7 @@ surfaces onto the distorted :abbr:`EPI (echo-planar imaging)` data [Esteban2016] Estimation tooling ~~~~~~~~~~~~~~~~~~ -The workflows provided by :py:mod:`sdcflows.fit` make use of several utilities. +The workflows provided by :py:mod:`sdcflows.workflows.fit` make use of several utilities. The cornerstone of these tools is the fieldmap representation with B-Splines (:py:mod:`sdcflows.interfaces.bspline`). B-Splines are well-suited to plausibly smooth the fieldmap and provide a compact @@ -198,7 +208,7 @@ updated and then the fieldmap reconstructed. Unwarping the distorted data ---------------------------- -:py:mod:`sdcflows.apply` contains workflows to project fieldmaps represented by B-Spline +:py:mod:`sdcflows.workflows.apply` contains workflows to project fieldmaps represented by B-Spline basis into the space of the target :abbr:`EPI (echo-planar imaging)` data. Discovering fieldmaps in a BIDS dataset @@ -208,11 +218,21 @@ To ease the implementation of higher-level pipelines integrating :abbr:`SDC (sus Explicit specification with ``B0FieldIdentifier`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. admonition:: BIDS Specification + + See the section `Expressing the MR protocol intent for fieldmaps - Using *B0FieldIdentifier* metadata + `__. + If one or more ``B0FieldIdentifier``\ (s) are set within the input metadata (following BIDS' specifications), then corresponding estimators will be built from the available input data. Implicit specification with ``IntendedFor`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. admonition:: BIDS Specification + + See the section `Expressing the MR protocol intent for fieldmaps - Using *IntendedFor* metadata + `__. + In the case no ``B0FieldIdentifier``\ (s) are defined, then *SDCFlows* will try to identify as many fieldmap estimators as possible within the dataset following a set of heuristics. Then, those estimators may be linked to target :abbr:`EPI (echo-planar imaging)` data by interpreting the @@ -221,7 +241,7 @@ Then, those estimators may be linked to target :abbr:`EPI (echo-planar imaging)` Fieldmap-less by registration to a T1-weighted image ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If none of the two previous options yielded any workable estimation strategy, and provided that -the argument ``fmapless`` is set to ``True``, then :py:func:`sdcflows.utils.wrangler.find_estimators` +the argument ``fmapless`` is set to ``True``, then :py:func:`~sdcflows.utils.wrangler.find_estimators` will attempt to find :abbr:`BOLD (blood-oxygen level-dependent)` or :abbr:`DWI (diffusion-weighted imaging)` instances within single sessions that are consistent in :abbr:`PE (phase-encoding)` direction and *total readout time*, assuming they have been acquired with the same shimming settings. @@ -231,7 +251,7 @@ If one or more anatomical images are found, and if the search for candidate yields results, then corresponding fieldmap-less estimators are set up. It is possible to force the fieldmap-less estimation by passing ``force_fmapless=True`` to the -:py:func:`sdcflows.utils.wrangler.find_estimators` utility. +:py:func:`~sdcflows.utils.wrangler.find_estimators` utility. References ---------- From f211350ab1ec22609cafa6a4d12350c636a7a156 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 27 Oct 2021 22:34:21 +0200 Subject: [PATCH 9/9] fix: final fixes, figure reference --- docs/conf.py | 2 ++ docs/methods.rst | 11 +++++++---- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index c20beef2eb..e03e8d5ca6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -76,6 +76,8 @@ # The master toctree document. master_doc = "index" +numfig = True + # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # diff --git a/docs/methods.rst b/docs/methods.rst index 6537acdf52..2badecefa5 100644 --- a/docs/methods.rst +++ b/docs/methods.rst @@ -6,7 +6,8 @@ the problem of susceptibility distortions (SD) into two stages: #. **Estimation**: the MRI acquisitions in the protocol for :abbr:`SD (susceptibility distortions)` are discovered and preprocessed to estimate a map of B\ :sub:`0` non-uniformity in Hz (:math:`\Delta B_0`). - The theory behind these distortions is well described in the literature [Jezzard1995]_ [Hutton2002]_ (Fig. 1). + The theory behind these distortions is well described in the literature ([Jezzard1995]_, [Hutton2002]_), + and further discussed below (see a summary in :numref:`fig-1`). *SDCFlows* builds on freely-available software to implement three major strategies for estimating :math:`\Delta B_0` (Eq. :math:`\eqref{eq:fieldmap-1}`). These strategies are described below, and implemented within :py:mod:`sdcflows.workflows.fit`\ . @@ -16,14 +17,16 @@ the problem of susceptibility distortions (SD) into two stages: target :abbr:`EPI (echo-planar imaging)` scan to be corrected, and a displacement field in NIfTI format that is compatible with ANTs is interpolated from the B-Spline basis. The voxel location error along the :abbr:`PE (phase-encoding)` will be proportional to :math:`\Delta B_0 \cdot T_\text{ro}`, - where :math:`T_\text{ro}` is the *total readout time* of the target :abbr:`EPI (echo-planar imaging)` (Fig. 1). + where :math:`T_\text{ro}` is the *total readout time* of the target :abbr:`EPI (echo-planar imaging)` (:numref:`fig-1`). The implementation of these workflows is found in the submodule :py:mod:`sdcflows.workflows.apply`\ . +.. _fig-1: + .. figure:: _static/sdcflows-OHBM21-fig1.png :width: 100% :align: center - **Figure 1**. Susceptibility distortions in a nutshell + Susceptibility distortions in a nutshell .. admonition:: BIDS Specification @@ -93,7 +96,7 @@ Phase-difference B0 estimation The fieldmap variation in T, :math:`\Delta B_0 (i, j, k)`, that is necessary to obtain :math:`\Delta_\text{PE} (i, j, k)` in Eq. :math:`\eqref{eq:fieldmap-1}` can be calculated from two subsequent :abbr:`GRE (Gradient-Recalled Echo)` echoes, -via eq. (1) of [Hutton2002]_: +via Eq. (1) of [Hutton2002]_: .. math::