diff --git a/cornucopia/qmri.py b/cornucopia/qmri.py
index acc0594..fb877e3 100755
--- a/cornucopia/qmri.py
+++ b/cornucopia/qmri.py
@@ -32,7 +32,10 @@
 
 # internal
 from .base import FinalTransform, NonFinalTransform, Transform
-from .baseutils import Arguments, NoArguments, Returned, nested_get, prepare_output, return_requires, returns_find, returns_update
+from .baseutils import (
+    Arguments, NoArguments, Returned,
+    nested_get, prepare_output, return_requires, returns_find, returns_update
+)
 from .labels import GaussianMixtureFinalTransform, GaussianMixtureTransform
 from .intensity import (
     RandomMulFieldTransform, AddValueTransform, MulValueTransform,
@@ -52,7 +55,9 @@ class RandomSusceptibilityMixtureTransform(NonFinalTransform):
     A RandomGaussianMixtureTransform tailored to susceptibility maps.
 
     This transform returns a delta susceptibility map (with respect to
-    air), in ppm.
+    air), in ppm. That is, the susceptibility value in each voxel is the
+    susceptibility of the tissue in that voxel minus the susceptibility
+    of air (which is 0.4 ppm).
     """
 
     Next = GaussianMixtureTransform
@@ -63,9 +68,9 @@ class RandomSusceptibilityMixtureTransform(NonFinalTransform):
 
     def __init__(
         self,
-        mu_tissue: cct.SamplerOrBound[float] = Uniform(9, 10),
+        mu_tissue: cct.SamplerOrBound[float] = Uniform(-10, -9),
         sigma_tissue: cct.SamplerOrBound[float] = 0.01,
-        mu_bone: cct.SamplerOrBound[float] = Uniform(12, 13),
+        mu_bone: cct.SamplerOrBound[float] = Uniform(-13, -12),
         sigma_bone: cct.SamplerOrBound[float] = 0.1,
         fwhm: cct.SamplerOrBound[float] = 2,
         label_air: cct.NumberOrSequence[int] = 0,
@@ -79,17 +84,15 @@ def __init__(
         Parameters
         ----------
         mu_tissue : Sampler | float
-            Distribution of negative susceptibility offsets (in ppm)
+            Distribution of susceptibility offsets (in ppm)
             of soft tissues with respect to air.
-            Will be negated (air susceptibility is larger than all tissues).
             If float: upper bound.
         sigma_tissue : Sampler | float
             Standard deviation of susceptibility offsets, within class.
-            If float: uper bound.
+            If float: upper bound.
         mu_bone : Sampler | float
-            Distribution of negative susceptibility offsets (in ppm)
+            Distribution of susceptibility offsets (in ppm)
             of hard tissues with respect to air.
-            Will be negated (air susceptibility is larger than all tissues).
             If float: upper bound.
         sigma_bone: Sampler | float
             Standard deviation of susceptibility offsets, within class.
@@ -177,9 +180,6 @@ def _unroll(self, x: Tensor, max_depth: int = inf) -> Transform:
             for i in range(n):
                 fwhm[i] = self.fwhm
 
-       # Negate
-        mu = [-v for v in mu]
-
         return GaussianMixtureTransform(
             mu, sigma, fwhm, dtype=self.dtype, **self.get_prm()
         ).unroll(x, max_depth-1)
@@ -187,58 +187,104 @@ def _unroll(self, x: Tensor, max_depth: int = inf) -> Transform:
 
 class SusceptibilityToFieldmapTransform(FinalTransform):
     """
-    Convert a susceptibiity map (in ppm) into a field map (in Hz)
+    Convert a delta susceptibiity map (in ppm) into a field map (in Hz).
+
+    !!! note
+        The input should be a delta susceptibility map with respect to
+        air, or a tissue mask, in ppm.
+
+    !!! tip
+        If the input is a binary mask, the delta susceptibility value in
+        foreground voxels is set to `delta`.
+
+    ??? reference
+        1.  "Perturbation Method for Magnetic Field Calculations of
+            Nonconductive Objects."
+            Mark Jenkinson, James L. Wilson, and Peter Jezzard.
+            MRM, 2004.
+            https://doi.org/10.1002/mrm.20194
+        2.  "Application of a Fourier-Based Method for Rapid Calculation of
+            Field Inhomogeneity Due to Spatial Variation of Magnetic Susceptibility."
+            Jose P. Marques, and Richard Bowtell.
+            CMR B, 2005.
+            https://doi.org/10.1002/cmr.b.20034
     """
 
     def __init__(
         self,
         axis: tx.Union[int, cct.VectorLike[float]] = -1,
-        field_strength: float = 3,
+        field_strength: tx.Optional[float] = 3,
         larmor: float = 42.576E6,
-        s0: float = 0.4,
-        s1: float = -9.5,
+        chi0: float = 0.4,
+        delta: float = -9.5,
         voxel_size: float = 1,
         mask_air: tx.Union[bool, tx.Literal['fill']] = False,
+        mode: tx.Literal['jenkinson', 'marques'] = 'marques',
         **kwargs
      ) -> None:
         """
 
         Parameters
         ----------
+
         axis : int | sequence[float]
             Direction of the main magnetic field.
-            - If a vector or floats, it is unit vector that encodes the
+
+            - If a vector or floats, it is a unit vector that encodes the
               direction of the main magnetic field in the "scaled voxel"
               coordinate systems.
             - If an int, the main magnetic field is assumed to be aligned
               with one of the dimensions of the voxel grid, and `zaxis` is
               the index of this dimension.
-            I.e., `0` is equivalent to `[1, 0, 0]`.
-        field_strength : float
+              I.e., `0` is equivalent to `[1, 0, 0]`.
+
+        field_strength : float | None
             Strength of the main magnetic field, in Tesla.
-            If None, return a fieldmap in ppm.
+            If `None`, return a fieldmap in ppm.
+
         larmor : float
             Larmor frequency (default: Larmor frequency of water)
-        s0 : float, default=0.4
+
+        chi0 : float, default=0.4
             Susceptibility of air (ppm)
-        s1 : float, default=-9.5
-            Susceptibility of tissue minus susceptiblity of air (ppm)
-            (only used if `ds` is a boolean mask)
+
+            !!! changedin "![v0.6](https://img.shields.io/badge/v0.6-yellow) \
+                    Parameter `s0` renamed to `chi0`"
+
+        delta : float, default=-9.5
+            Delta susceptibility of tissue with respect to air (ppm)
+            (only used if the input tensor is a boolean mask).
+
+            !!! changedin "![v0.6](https://img.shields.io/badge/v0.6-yellow) \
+                    Parameter `s1` renamed to `delta`"
+
         voxel_size : [list of] float
-            Voxel size
-        mask_air : bool
-            Mask air (where delta susceptibility == 0) from
-            resulting fieldmap.
+            Voxel size.
+
+        mask_air : bool | {'fill'}
+            Mask air (where delta susceptibility == 0) from the resulting
+            fieldmap. If `'fill'`, the values in air are replaced by
+            interpolated foreground values. This can help when applying
+            a fieldmap-based displacement map.
+
+        mode : {'jenkinson', 'marques'}
+            Method to compute the fieldmap.
+
+            !!! addedin "![v0.6](https://img.shields.io/badge/v0.6-green) \
+                    New parameter `mode` added with default value `'marques'`"
+                Before this, the `'jenkinson'` method was used by default,
+                and the `'marques'` method was not available.
 
         """
         super().__init__(**kwargs)
         self.axis = axis
         self.field_strength = field_strength
         self.larmor = larmor
-        self.s0 = s0
-        self.s1 = s1
+        self.chi0 = chi0
+        self.delta = delta
         self.mask_air = mask_air
         self.voxel_size = voxel_size
+        self.mode = mode
 
     def _xform(self, x: Tensor) -> Returned:
         ndim = x.ndim - 1
@@ -246,8 +292,15 @@ def _xform(self, x: Tensor) -> Returned:
         axis = self.axis
         if isinstance(axis, int):
             axis = 1 + ((x.ndim - 1 + axis) if axis < 0 else axis)
-        field = b0.chi_to_fieldmap(x, zaxis=axis, ndim=x.ndim-1,
-                                   s0=self.s0, s1=self.s1, vx=self.voxel_size)
+
+        delta = self.delta
+        if x.dtype.is_floating_point:
+            delta = True
+
+        field = b0.chi_to_fieldmap(
+            x, zaxis=axis, ndim=x.ndim-1, vx=self.voxel_size,
+            delta=delta, chi0=self.chi0, mode=self.mode,
+        )
 
         if self.mask_air:
             field.masked_fill_(x == 0, 0)
@@ -283,10 +336,10 @@ def __init__(
         """
         Parameters
         ----------
-        linear : (3|1,) tensor | [list of] float
-            Linear components (3D: [XY, XZ, YX], 2D: [XY])
-        quadratic : (2|1,) tensor | [list of] float
-            Quadratic components (3D: [XXYY, XXZZ], 2D: [XXYY])
+        linear : (3|2,) tensor | [list of] float
+            Linear components: (X, Y, [Z,])
+        quadratic : (5|2,) tensor | [list of] float
+            Quadratic components: (XY, [XZ, YZ,] XX-YY, [XX-ZZ,])
         isocenter : (3|2,) tensor | [list of] float
             Coordinates of the isocenter, in voxels
 
@@ -508,7 +561,7 @@ def __init__(self, bandwidth: float = 30, **kwargs) -> None:
             pixel is much larger, and displacements are only meaningful
             in extremely high-resolution scans.
 
-            !!! changedin "![v0.4](https://img.shields.io/badge/v0.4-yellow) \
+            !!! changedin "![v0.5](https://img.shields.io/badge/v0.5-yellow) \
                         Default  changed from `140` to `30`"
 
         """
diff --git a/cornucopia/tests/test_backward_qmri.py b/cornucopia/tests/test_backward_qmri.py
index 3ae18c1..51730bf 100755
--- a/cornucopia/tests/test_backward_qmri.py
+++ b/cornucopia/tests/test_backward_qmri.py
@@ -66,8 +66,8 @@ def test_backward_qmri_tofieldmap():
     y = SusceptibilityToFieldmapTransform(
         field_strength=b,
         larmor=f,
-        s0=s0,
-        s1=s1,
+        chi0=s0,
+        delta=s1,
         voxel_size=v,
     )(x)
     y.sum().backward()
@@ -107,7 +107,7 @@ def test_backward_qmri_shim(size):
     x = SusceptibilityToFieldmapTransform(
         field_strength=b,
         larmor=f,
-        s0=s0,
+        chi0=s0,
         voxel_size=v,
     )(x)
     if x.ndim == 3:
@@ -158,7 +158,7 @@ def test_backward_qmri_shim_optimal():
     x = SusceptibilityToFieldmapTransform(
         field_strength=b,
         larmor=f,
-        s0=s0,
+        chi0=s0,
         voxel_size=v,
     )(x)
     y = OptimalShimTransform()(x)
diff --git a/cornucopia/utils/b0.py b/cornucopia/utils/b0.py
index 381cfff..89f9834 100755
--- a/cornucopia/utils/b0.py
+++ b/cornucopia/utils/b0.py
@@ -18,7 +18,7 @@
 # internal
 from .warps import identity as identity_grid, cartesian_grid
 from .py import prod, make_vector
-from .smart_inplace import mul_, div_
+from .smart_inplace import add_, mul_, div_, square_, sub_
 
 
 r"""
@@ -37,28 +37,33 @@
 References
 ----------
 1.  "Perturbation Method for Magnetic Field Calculations of
-       Nonconductive Objects"
-      Mark Jenkinson, James L. Wilson, and Peter Jezzard
-      MRM, 2004
-      https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.20194
-2.  "Susceptibility mapping of air, bone, and calcium in the head"
-      Sagar Buch, Saifeng Liu, Yongquan Ye, Yu-Chung Norman Cheng,
-      Jaladhar Neelavalli, and E. Mark Haacke
-      MRM, 2014
-3.  "Whole-brain susceptibility mapping at high field: A comparison
-       of multiple- and single-orientation methods"
-      Sam Wharton, and Richard Bowtell
-      NeuroImage, 2010
-4.  "Quantitative susceptibility mapping of human brain reflects
-       spatial variation in tissue composition"
-      Wei Li, Bing Wua, and Chunlei Liu
-      NeuroImage 2011
-5.  "Human brain atlas for automated region of interest selection in
-       quantitative susceptibility mapping: Application to determine iron
-       content in deep gray matter structures"
-      Issel Anne L.Lim, Andreia V. Faria, Xu Li, Johnny T.C.Hsu,
-      Raag D.Airan, Susumu Mori, Peter C.M. van Zijl
-      NeuroImage, 2013
+     Nonconductive Objects"
+    Mark Jenkinson, James L. Wilson, and Peter Jezzard
+    MRM, 2004
+    https://doi.org/10.1002/mrm.20194
+2.  "Application of a Fourier-Based Method for Rapid Calculation of
+     Field Inhomogeneity Due to Spatial Variation of Magnetic Susceptibility"
+    Jose P. Marques, and Richard Bowtell
+    CMR B, 2005
+    https://doi.org/10.1002/cmr.b.20034
+3.  "Susceptibility mapping of air, bone, and calcium in the head"
+    Sagar Buch, Saifeng Liu, Yongquan Ye, Yu-Chung Norman Cheng,
+    Jaladhar Neelavalli, and E. Mark Haacke
+    MRM, 2014
+4.  "Whole-brain susceptibility mapping at high field: A comparison
+     of multiple- and single-orientation methods"
+    Sam Wharton, and Richard Bowtell
+    NeuroImage, 2010
+5.  "Quantitative susceptibility mapping of human brain reflects
+     spatial variation in tissue composition"
+    Wei Li, Bing Wua, and Chunlei Liu
+    NeuroImage 2011
+6.  "Human brain atlas for automated region of interest selection in
+     quantitative susceptibility mapping: Application to determine iron
+     content in deep gray matter structures"
+    Issel Anne L.Lim, Andreia V. Faria, Xu Li, Johnny T.C.Hsu,
+    Raag D.Airan, Susumu Mori, Peter C.M. van Zijl
+    NeuroImage, 2013
 """
 mr_chi = {
     'air': 0.4,         # Jenkinson (Buch: 0.35)
@@ -73,14 +78,14 @@
 #             the convolution kernel is real + symmetric -> rfft or hfft
 
 
-def fieldmap_to_shift(delta, bandwidth=140):
+def fieldmap_to_shift(delta, bandwidth=30):
     """Convert fieldmap to voxel shift map
 
     Parameters
     ----------
     delta : tensor
         Fieldmap (Hz)
-    bandwidth : float, default=140
+    bandwidth : float, default=30
         Bandwidth (Hz/pixel)
 
     Returns
@@ -91,8 +96,21 @@ def fieldmap_to_shift(delta, bandwidth=140):
     return delta / bandwidth
 
 
-def labels_to_chi(label_map, label_dict=None,
-                  reference='air', dtype=None):
+def _label2chi(label):
+    if label in mr_chi:
+        return mr_chi[label]
+    elif label in jenkinson_chi:
+        return jenkinson_chi[label]
+    elif label in buch_chi_delta_water:
+        return mr_chi['water'] + buch_chi_delta_water[label]
+    elif label in li_chi_delta_water:
+        return mr_chi['water'] + li_chi_delta_water[label]
+    elif label in wharton_chi_delta_water:
+        return mr_chi['water'] + wharton_chi_delta_water[label]
+    return mr_chi['water']
+
+
+def labels_to_chi(label_map, label_dict=None, dtype=None):
     """Synthesize a susceptibility map from labels
 
     Parameters
@@ -101,49 +119,55 @@ def labels_to_chi(label_map, label_dict=None,
         Input label map
     label_dict : dict[int, float or str], default={0: 'air', 1: 'water'}
         Dictionary mapping labels to susceptibility values or region names
-    reference : float or str, default='air'
-        Reference susceptibility
     dtype : torch.dtype
         Data type
 
     Returns
     -------
-    delta_chi : tensor
-        Delta susceptibility map, with respect to the reference (ppm)
+    chi : tensor
+        Susceptibility map (ppm)
 
     """
     if not label_dict:
         label_dict = {0: 'air', 1: 'water'}
+
     unique_labels = label_map.unique().long().tolist()
+    label_range = max(unique_labels) - min(unique_labels) + 1
 
     dtype = dtype or torch.get_default_dtype()
-    delta = torch.empty_like(label_map, dtype=dtype)
-
-    for label in unique_labels:
-        susceptibility = label_dict.get(label, 'water')
-
-        if susceptibility in mr_chi:
-            susceptibility = mr_chi[susceptibility]
-        elif susceptibility in jenkinson_chi:
-            susceptibility = jenkinson_chi[susceptibility]
-        elif susceptibility in buch_chi_delta_water:
-            susceptibility = buch_chi_delta_water[susceptibility] \
-                           + mr_chi['water']
-        elif susceptibility in li_chi_delta_water:
-            susceptibility = li_chi_delta_water[susceptibility] \
-                           + mr_chi['water']
-        elif susceptibility in wharton_chi_delta_water:
-            susceptibility = wharton_chi_delta_water[susceptibility] \
-                           + mr_chi['water']
+
+    susceptibility = {
+        label: _label2chi(label_dict.get(label, 'water'))
+        for label in unique_labels
+    }
+
+    if label_range < 2**16:
+        # Use a fast lookup table if the label range is small enough
+        # (Otherwise, the lookup table would be too large)
+
+        conversion = torch.full(
+            [label_range], mr_chi['water'],
+            dtype=dtype, device=label_map.device
+        )
+        for src, dst in susceptibility.items():
+            conversion[src] = dst
+
+        if min(unique_labels) != 0:
+            label_map = label_map.to(torch.long, copy=True)
+            label_map -= min(unique_labels)
         else:
-            susceptibility = mr_chi['water']
+            label_map = label_map.to(torch.long)
+
+        chi = conversion[label_map]
+
+    else:
+        # Loop through labels
 
-        delta[label_map == label] = susceptibility
+        chi = torch.full_like(label_map, mr_chi['water'], dtype=dtype)
+        for src, dst in susceptibility.items():
+            chi[label_map == src] = dst
 
-    if isinstance(reference, str):
-        reference = mr_chi[reference]
-    delta -= reference
-    return delta
+    return chi
 
 
 def ppm_to_hz(fmap, b0=3, freq=42.576E6):
@@ -168,16 +192,23 @@ def ppm_to_hz(fmap, b0=3, freq=42.576E6):
 
 
 def chi_to_fieldmap(
-        ds, zaxis=-1, ndim=None, s0=mr_chi['air'],
-        s1=mr_chi['water'] - mr_chi['air'], vx=1):
+    chi,
+    zaxis=-1,
+    ndim=None,
+    chi0=mr_chi['air'],
+    delta=False,
+    vx=1,
+    mode='marques',
+):
     """Generate a MR fieldmap from a MR susceptibility map.
 
     Parameters
     ----------
-    ds : (..., *spatial) tensor
-        Susceptibility delta map (delta from air susceptibility) in ppm.
-        If bool, `s1` will be used to set the value inside the mask.
-        If float, should contain quantitative delta values in ppm.
+    chi : (..., *spatial) tensor
+        Magnetic susceptibility map (ppm).
+        Each voxel should contain the magnetic susceptibility of the
+        tissue in this voxel. Voxels that contain air should have the
+        susceptibility of air (e.g., 0.4 ppm).
     zaxis : int | sequence[float], default=-1
         Direction of the main magnetic field.
         If a vector or floats, it is unit vector that encodes the direction
@@ -187,60 +218,104 @@ def chi_to_fieldmap(
         index of this dimension. I.e., `0` is equivalent to `[1, 0, 0]`.
     ndim : int, default=`ds.ndim`
         Number of spatial dimensions.
-    s0 : float, default=0.4
+    chi0 : float, default=0.4
         Susceptibility of air (ppm)
-    s1 : float, default=-9.5
-        Susceptibility of tissue minus susceptiblity of air (ppm)
-        (only used if `ds` is a boolean mask)
+    delta : bool | float, default=False
+        * If a bool, the input `chi` is relative to the susceptibility of air
+          (no need to subtract `chi0`).
+          I.e., `true_chi = chi + chi0`.
+        * If a float, the input `chi` is relative to the susceptibility of air,
+          and should be scaled by the value of delta.
+          I.e., `true_chi = chi * delta + chi0`.
     vx : [sequence of] float
         Voxel size
+    mode : {'jenkinson', 'marques'}, default='jenkinson'
+        Method to compute the Greens kernel.
 
     Returns
     -------
-    delta_b0 : tensor
+    b : tensor
         MR field map (ppm).
 
     References
     ----------
-    1. "Perturbation Method for Magnetic Field Calculations of
-           Nonconductive Objects"
-          Mark Jenkinson, James L. Wilson, and Peter Jezzard
-          MRM, 2004
-          https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.20194
+    1.  "Perturbation Method for Magnetic Field Calculations of
+         Nonconductive Objects"
+        Mark Jenkinson, James L. Wilson, and Peter Jezzard
+        MRM, 2004
+        https://doi.org/10.1002/mrm.20194
+    2.  "Application of a Fourier-Based Method for Rapid Calculation of
+         Field Inhomogeneity Due to Spatial Variation of Magnetic Susceptibility"
+        Jose P. Marques, and Richard Bowtell
+        CMR B, 2005
+        https://doi.org/10.1002/cmr.b.20034
 
     """
-    ds = torch.as_tensor(ds)
-    backend = dict(dtype=ds.dtype, device=ds.device)
-    if ds.dtype is torch.bool:
+    chi = torch.as_tensor(chi)
+    backend = dict(dtype=chi.dtype, device=chi.device)
+    if chi.dtype is torch.bool:
         backend['dtype'] = torch.get_default_dtype()
 
-    ndim = ndim or ds.ndim
-    shape = ds.shape[-ndim:]
-    zaxis = zaxis - ds.ndim if zaxis >= 0 else zaxis
-    # ^ number from end so that we can apply to vx
+    ndim = ndim or chi.ndim
+    shape = chi.shape[-ndim:]
     vx = make_vector(vx, ndim)
-
-    if ds.dtype is torch.bool:
-        ds = ds.to(**backend)
+    if isinstance(zaxis, int):
+        zaxis = zaxis - chi.ndim if zaxis >= 0 else zaxis
+        # ^ number from end so that we can apply to vx
+
+    # Compute susceptibility difference
+    if delta is False:
+        chi1 = chi - chi0
+    elif delta is not True:
+        chi1 = chi * delta
     else:
-        s1 = ds.abs().max()
-        ds = ds / s1
-    s1 = s1 * 1e-6
-    s0 = s0 * 1e-6
-
-    # Analytical implementation following Jenkinson et al.
+        chi1 = chi
+
+    # Compute convolution kernel (in Fourier domain)
+    mode = mode[:1].lower()
+    greens = {
+        'j': greens_jenkinson,
+        'm': greens_marques,
+    }[mode]
     g = greens(shape, zaxis, voxel_size=vx, **backend)
-    f = greens_apply(ds, g)
-
-    # apply rest of the equation
-    out = ds * ((1. + s0) / (3. + s0))
-    out -= f
-    out *= s1 / (1 + s0)
 
+    # and apply it to the susceptibility map
+    f = greens_apply(chi1, g)
+
+    # Apply rest of the equation
+    if mode == 'j':
+        # δB = B0 * (δχ / (3 + χ0) - (G" * δχ) / (1 + χ0)) [Jenkinson.13]
+        # where G is the Green function of the (negative) Laplacian
+        #    -> G(x) = 1 / (4 * π * |x|)
+        out = chi1 * ((1. + chi0) / (3. + chi0))
+        out = sub_(out, f)
+        out = mul_(out, 1. / (1. + chi0))
+        # NOTE
+        #   The DC component of G" is -2/3, so the average delta field
+        #   is B0 * average(δχ) * (1 / (3 + χ0) + 2 / (3 * (1 + χ0)))
+    elif mode == 'm':
+        # δB = B0 * (δχ / 3 - (Rz^2) * δχ) [Marques.6]
+        #   where Rz^2 is the (negative) squared Riesz transform
+        #      -> Rz^2 = (∂z^2/∇^2)
+        out = chi1 / 3.
+        out = sub_(out, f)
+        # NOTE:
+        #   The kernel is ill-defined at the center of k-space.
+        #   Marques and Bowtell suggest to ensure that the average
+        #   delta field is - B0 * χ0 / 3, which is the theoretical
+        #   value for an infinite, homogeneous object with magnetic
+        #   susceptibility χ0, when the Lorentz correction is included
+        #   (i.e., B = (1 + χ0 / 3) B0).
+        #
+        # We therefore ensure that: average(δB) = - B0 * χ0 / 3
+        mean = out.sum().div_(out.numel())  # weird fix for pytorch 2.0
+        out = sub_(out, mean + chi0 / 3.)
+
+    out = mul_(out, 1e-6)  # account for ppm
     return out
 
 
-def greens(shape, zaxis=-1, voxel_size=1, dtype=None, device=None):
+def greens_jenkinson(shape, zaxis=-1, voxel_size=1, dtype=None, device=None):
     """Semi-analytical second derivative of the Greens kernel.
 
     This function implements exactly the solution from Jenkinson et al.
@@ -331,9 +406,11 @@ def dot(a, b):
             yaxis[1].neg_()
 
     # make zero-centered meshgrid
+    # center = [math.ceil((s-1) / 2) for s in shape]  # matches ifftshift
+    center = [(s-1) / 2 for s in shape]
     g0 = identity_grid(shape, dtype=dtype, device=device)
-    for g1, s in zip(g0.unbind(-1), shape):
-        g1 -= (s-1) / 2
+    for g1, c in zip(g0.unbind(-1), center):
+        g1 -= c
 
     def shift_and_scale_grid(grid, shift):
         return mul_(grid + make_vector(shift).to(grid), voxel_size.to(grid))
@@ -342,9 +419,9 @@ def shift_and_scale_grid(grid, shift):
     for shift in itertools.product([-0.5, 0.5], repeat=ndim):
         # Integrate across a voxel
         g1 = shift_and_scale_grid(g0, shift)
-        # Compute \int G" dx dy dz
-        # where G is the Green function of the 2D or 3D Laplacian and
-        # G" is the second derivative of G wrt z.
+        # Compute \int G" dx dy dz, where
+        # * G is the Green function of the Laplacian, and
+        # * G" is the second derivative of G wrt z.
 
         if isinstance(zaxis, int):
             z = g1[..., zaxis]
@@ -367,14 +444,134 @@ def shift_and_scale_grid(grid, shift):
         else:
             g += g1
 
+    # divide by 4*pi
     g = div_(g, (2 ** (ndim-1)) * math.pi)
-    g = fft.ifftshift(g, dims)  # move center voxel to first voxel
+
+    # move center voxel to first voxel
+    g = fft.ifftshift(g, dims)
 
     # fourier transform
     g = fft.fftn(g, dim=dims).real
     return g
 
 
+greens = greens_jenkinson  # backward compatibility
+
+
+def greens_marques(shape, zaxis=-1, voxel_size=1, dtype=None, device=None):
+    """Greens kernel according to Marques and Bowtell (2005)
+
+    The center of the kernel is ill-defined and set to zero. The DC
+    component of the convolved image will be set to s0/3.
+
+    The returned tensor has already been Fourier transformed and can
+    be cached if multiple field simulations with the same lattice size
+    must be performed in a row.
+
+    Parameters
+    ----------
+    shape : sequence of int
+        Lattice shape
+    zaxis : int | sequence[float], default=-1
+        Direction of the main magnetic field.
+        If a vector or floats, it is unit vector that encodes the direction
+        of the main magnetic field in the "scaled voxel" coordinate systems.
+        If an int, the main magnetic field is assumed to be aligned
+        with one of the dimensions of the voxel grid, and `zaxis` is the
+        index of this dimension. I.e., `0` is equivalent to `[1, 0, 0]`.
+    voxel_size : [sequence of] int
+        Voxel size
+    dtype : torch.dtype, optional
+    device : torch.device, optional
+
+    Returns
+    -------
+    kernel : (*shape) tensor
+        Fourier transform of the (second derivatives of the) Greens kernel.
+
+    """
+    def dot(a, b):
+        return a.unsqueeze(-2).matmul(b.unsqueeze(-1)).squeeze(-1).squeeze(-1)
+
+    ndim = len(list(shape))
+    dims = list(range(-ndim, 0))
+    dtype = dtype or torch.get_default_dtype()
+    voxel_size = make_vector(voxel_size, ndim, dtype=dtype, device=device)
+
+    if ndim not in (2, 3):
+        raise ValueError('Invalid ndim', ndim)
+
+    if isinstance(zaxis, int):
+
+        zaxis = -ndim + zaxis if zaxis >= 0 else zaxis  # use negative indexing
+
+        if zaxis not in range(-ndim, 0):
+            raise ValueError('Invalid zaxis', zaxis)
+
+        if ndim == 3:
+            yaxis, xaxis = ([-2, -3] if zaxis == -1 else
+                            [-1, -3] if zaxis == -2 else
+                            [-1, -2])
+        elif ndim == 2:
+            yaxis = -2 if zaxis == -1 else -1
+        else:
+            raise NotImplementedError
+
+    else:
+        zaxis = make_vector(zaxis, dtype=dtype, device=device)
+        zaxis = zaxis / zaxis.norm()  # ensure unit vector
+
+        if len(zaxis) not in (2, 3):
+            raise ValueError('Invalid zaxis', zaxis)
+
+        if ndim == 3:
+            yaxis, xaxis = zaxis.clone(), zaxis.clone()
+            yaxis[:2] = yaxis[:2].flip(0)
+            yaxis[1].neg_()
+            yaxis[2].zero_()
+            xaxis[-2:] = xaxis[-2:].flip(0)
+            xaxis[2].neg_()
+            xaxis[0].zero_()
+        else:
+            yaxis = zaxis.flip(0)
+            yaxis[1].neg_()
+
+    # make zero-centered meshgrid
+    center = [math.ceil((s-1) / 2) for s in shape]  # matches ifftshift
+    g0 = identity_grid(shape, dtype=dtype, device=device)
+    for g1, c in zip(g0.unbind(-1), center):
+        g1 -= c
+
+    # scale by voxel size
+    g0 = mul_(g0, voxel_size)
+
+    # compute kernel
+    if isinstance(zaxis, int):
+        g0 = square_(g0)
+        z = g0[..., zaxis]
+        y = g0[..., yaxis]
+        r = add_(y, z)
+        if ndim == 3:
+            x = g0[..., xaxis]
+            r = add_(r, x)
+    else:
+        z = square_(dot(g0, zaxis))
+        y = square_(dot(g0, yaxis))
+        r = add_(y, z)
+        if ndim == 3:
+            x = square_(dot(g0, xaxis))
+            r = add_(r, x)
+
+    r = r.clamp_min_(1e-8)
+    g = div_(z, r)
+    g[tuple(center)] = 0
+
+    # move center voxel to first voxel
+    g = fft.ifftshift(g, dims)
+
+    return g
+
+
 def greens_apply(mom, greens):
     """Apply the Greens function to a momentum field.
 
@@ -399,7 +596,7 @@ def greens_apply(mom, greens):
     mom = fft.fftn(mom, dim=dims)
 
     # voxel wise multiplication
-    mom *= greens
+    mom = mul_(mom, greens)
 
     # inverse fourier transform
     mom = fft.ifftn(mom, dim=dims).real
diff --git a/docs/examples/b0.ipynb b/docs/examples/b0.ipynb
index 957d868..92a462c 100755
--- a/docs/examples/b0.ipynb
+++ b/docs/examples/b0.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -62,7 +62,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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eTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCg0ogM9dNpBwCA4BTtAAAQnHgMAACVsHtMeTrtAAAQnKIdAACCE48BAKAS4jHl6bQDAEBwinYAAAhOPAYAgEqIx5Sn0w4AAMEp2gEAIDjxGAAAKiEeU55OOwAA1GDevHnp7W9/ezriiCPScccdly677LL00ksv9XufFStWpLa2tr0uL774YqqHoh0AAGqwcuXKdOWVV6Zvfetbafny5Wn37t1pypQpafv27fu9by7uN23a1H059dRTUz3EYwAAqESjx2MeeeSRXte/+MUvFh33p556Kk2aNKnf++bjjjzyyFSWTjsAAC2to6Oj12XHjh013W/btm3FP4866qj9HnvWWWelUaNGpYsvvjg9/vjjda9R0Q4AQEsbM2ZMGjFiRPclZ9dr6dLPnj07XXDBBemMM87Y53G5UL/99tvTkiVL0n333ZfGjRtXFO6rVq2qa43iMQAAtHQ8ZuPGjam9vb379uHDh+/3vldddVV67rnn0je+8Y1+j8tFer50mTBhQvF88+fP32+kpieddgAAWlp7e3uvy/6K9quvvjotXbq0iLmccMIJdT/feeedl9atW1fXfXTaAQCgxs58Ltjvv//+YivHsWPHpjLWrl1bxGbqoWgHAKCl4zG1yts93nPPPemf/umfir3aN2/eXNyec/CHHXZY8e9z585Nr7zySrr77ruL6wsWLEgnn3xyOv3009POnTvTokWLinx7vtRD0Q4AADVYuHBh8c/JkyfvtfXjb/zGbxT/nvdg37BhQ/ef5UJ9zpw5RSGfC/tcvD/00ENp+vTpqR5tnTV+xMhnbgIAILZInewueRvF3I1evXp1Ovzww1MUr732WjEYmrdu7DmIGpFOOwAAlWj0eMxQsnsMAAAEp2gHAIDgxGMAAKiEeEx5Ou0AABCcoh0AAIITjwEAoBLiMeXptAMAQHCKdgAACE48BgCASojHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp2gHAIDgxGMAAKiEeEx5inZgSMycOdM7//9bvHix9wKAfonHAABAcDrtAABUQjymPJ12AAAITtEOAADBiccAezEk2nzvt2FXIALxmPJ02gEAIDhFOwAABCceAwBAJcRjytNpBwCA4HTaoYkZKOVA/1swwAoQg6IdAIBKiMeUJx4DAADBKdoBACA48RgAACqNyFA/RTsEZ5iURv3vzxArwMARjwEAgOB02gEAqITdY8rTaQcAgOAU7QAAEJx4DAyRVh8wbfXX3woDm7X+jJv19QN7E48pT6cdAACCU7QDAEBw4jEAAFRCPKY8nXYAAAhOpx0GQTMOWTbja2rW97bRBjtref2N9poABpqiHQCASojHlCceAwAAwSnaAQAgOPEYAAAqIR5TnqIdmngYs5HW+8EPfjC1ii9/+csN83OPMgDq7KpAqxOPAQCA4HTaAQCohHhMeTrtAAAQXFtn/shTy4FtbYO/GhgijZT9jr7eVsqmN1sWvhmy7826XqhHjaVdpTo6OtKIESPSww8/nN74xjemKLZv356mTZuWtm3bltrb21Nk4jEAAFRCPKY88RgAAAhO0Q4AAMGJxwAAUAnxmPIMotKSog5yRl1XZsC0cRhYrZ/BVJpJ5EHUhx56KNwg6rvf/e6GGEQVjwEAgODEYwAAqIR4THk67QAAEJyiHQAAgjOISlOJPMgZeW17MnTafCIPpzbSUGjktUEjDKI++OCD4QZRL730UoOoAADAgROPAQCA4OweAwBAJeweU55OOwAABGcQlYYVZbAzyjrKrretrW3I1kKsYbVGG7KMst4o64BGGERdunRpuEHU9773vQ0xiCoeAwBAJcRjyhOPAQCA4BTtAAAQnHgMAACVEI8pT9FOQxiKYc9GHzDdF4On7Ou/hVqH16IMXtb63/xgr7evdUR5j4DmIR4DAADB6bQDAFAJ8ZjydNoBACA4RTsAAATnjKiEY+h04N4PQ6cM5ZkVIw9jDsXaIr8fNJfIZ0S97777wp0R9f3vf39DnBFVpx0AAIJTtAMAQHB2jwEAoBJ2jylPpp0hNVQnMIpw4qQq1iDTzoGqIhsbIes9VGuI8NppPpEz7UuWLAmXaZ8xY4ZMOwAAcODEYwAAqIR4THkGUQEAIDhFOwAABGcQlaYavowwYDpU6zB0SjMPuUUZ2BzsdUR5nTS2yIOoX/nKV9Ib3vCGFMWPf/zj9IEPfMAgKgAAcODEYwAAIDi7xwAAUAm7x5Sn0w4AAMEZRGXQGDqtlkFUWm3ILcrQpuFUoony/2hfg6j5/5dog6gzZ85siEFU8RgAACohHlOeeAwAAASnaAcAgODEYwAAqIR4THmKdgZEMw6dRjm7KnBg/48O9qBoX+sYyOcc7McHGoN4DAAABKfTDgBAJcRjytNpBwCA4BTtAAAQnHgMdTN0CjSSoRjkNJwKfROPKU+nHQAAglO0AwBAcOIxAABUQjymPJ12AAAITqedlhs6HcrnBWLY83dAFWcYNZwKHAhFOwAAlRCPKU88BgAAglO0AwBAcOIxAABUQjymPEU7lQ5nDsUAaKsMneZfhHtqa2sbkrXQ3P9dNaOhOGtqX8870M85VK8LGHjiMQAAEJxOOwAAlRCPKU+nHQAAgtNpp6m0Sn4dGHzy4EAkinYAACohHlOeeAwAAASnaAcAgODEYwAAqIR4THmK9hbVDCdSMnS6f064RD1a5URKkYdTqxh+NWAL5c2bNy/dd9996cUXX0yHHXZYOv/889NnPvOZNG7cuH7vt3LlyjR79uz0/PPPp9GjR6dPfOITadasWXU9t3gMAADUIBffV155ZfrWt76Vli9fnnbv3p2mTJmStm/fvs/7rF+/Pk2fPj1NnDgxrV27Nt1www3pmmuuSUuWLEn10GkHAKCl4zEdHR29bh8+fHhx2dMjjzzS6/oXv/jFdNxxx6WnnnoqTZo0qc/nuO2229KJJ56YFixYUFw/7bTT0po1a9L8+fPTjBkzal6rTjsAAC1tzJgxacSIEd2XHIOpxbZt24p/HnXUUfs8ZvXq1UU3vqepU6cWhfuuXbtqXqNOOwAALW3jxo2pvb29+3pfXfa+uvQ5p37BBRekM844Y5/Hbd68OY0cObLXbfl6jtZs3bo1jRo1qqY1KtpbRKMPhRo6HTh7fi3Z1tY2gI9OI4n0FXUja8bh1IF+fIgej2lvb+9VtNfiqquuSs8991z6xje+sd9j9/y7tut56/k7WNEOAAB1uPrqq9PSpUvTqlWr0gknnNDvsccff3zRbe9py5YtadiwYenoo4+u+Tll2gEAoAa5Q5477Hnbx8ceeyyNHTt2v/eZMGFCsdNMT48++mgaP358Ovjgg1OtFO0AAFQaj4l0qUfe7nHRokXpnnvuSUcccUTRQc+Xn/zkJ93HzJ07N11++eXd1/N+7C+//HKRf3/hhRfSnXfeme644440Z86cup5b0Q4AADVYuHBhsWPM5MmTiwHSrsu9997bfcymTZvShg0buq/nbvyyZcvSihUr0tve9rb0p3/6p+nmm2+ua7vHTKaduhk6bS7OmtoaIg1+tYJmGE4Fyv0uveuuu/a67Z3vfGd6+umn04FQtAMA0NK7xzQC8RgAAAhO0Q4AAMGJxwAAUAnxmPIU7U3I2UOpKuPnbKpDr5HymDQOg64Qj3gMAAAEp9MOAEAlxGPK02kHAIDgFO0AABCceAz9cvZT+mNgdfAYMG0+zpIK4jEHQqcdAACCU7QDAEBw4jEAAFRG/K8cRXuDa7QTKTXaeon3CzryCZ38RcSB/k5cvHhx2DfRCZdgaInHAABAcDrtAABUwsmVytNpBwCA4BTtAAAQnHgM3QyJ0ggMe0LtDI8SjXhMeTrtAAAQnKIdAACCE48BAKAS4jHl6bQDAEBwOu0NpNEGRRttvQBDrdEGRxttvdDIFO0AAFRCPKY88RgAAAhO0Q4AAMGJxwAAUAnxmPIU7S3KkChAazI8Co1JPAYAAILTaQcAoBLiMeXptAMAQHCKdgAACE48JqhGGxRttPUCNIpGGxxttPVSLfGY8nTaAQAgOEU7AAAEJx4DAEAlxGPK02kHAIDgdNpbhEFRAGr9O8LgKMSjaAcAoBLiMeWJxwAAQHCKdgAACE48hrrJxwMMLScwolGJx5Sn0w4AAMEp2gEAIDjxGAAAKiEeU55OOwAABKfT3oSDnQZFARjov0cO5IRLBmfhwCnaAQCohHhMeeIxAAAQnKIdAACCE48BAKAS4jHlKdrpl6FWgMZg2BOam3gMAAAEp9MOAEAlxGPK02kHAIDgFO0AABCceAwAAJUQjylPpx0AAIJTtAMAQHDiMQAAVEI8pjyddgAACE6nvWLOMAoAzuAK9VK0AwBQCfGY8sRjAAAgOEU7AAAEJx4DAEClERnqp2hvcAM52GpIFqC59PV7ffHixUP+WED9xGMAACA4nXYAACph95jydNoBACA4RTsAAAQnHgMAQCXEY8rTaQcAgOAU7QAAEJx4DAAAlRCPKU+nHQAAglO0AwBAcOIxAABUQjymPJ12AAAITtEOAADBiccAAFAJ8ZjydNoBACA4RTsAAAQnHgMAQCXEY8rTaQcAgOB02gfZzJkzQz4WAAzG30uLFy8esMc7kMeCZqNoBwCgEuIx5YnHAABAcIp2AAAITjwGAIBKiMeUp2hvUYZaAVrTQA+PAtUQjwEAgOB02gEAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgOAU7QAAVBqPiXSp16pVq9Kll16aRo8endra2tIDDzzQ7/ErVqwojtvz8uKLL9b1vDLtAABQo+3bt6czzzwzffSjH00zZsyo9W7ppZdeSu3t7d3Xjz322FQPRTsAANRo2rRpxaVexx13XDryyCNTWeIxAAC0tI6Ojl6XHTt2DPhznHXWWWnUqFHp4osvTo8//njd91e0AwBQmYh59jFjxqQRI0Z0X+bNmzdgrzcX6rfffntasmRJuu+++9K4ceOKwj1n4+shHgMAQEvbuHFjr7z58OHDB+yxc5GeL10mTJhQPN/8+fPTpEmTan4cnXYAAFpae3t7r8tAFu19Oe+889K6devquo9OOwAAlXBG1P/P2rVri9hMPRTtAABQo9deey1997vf7b6+fv369Mwzz6SjjjoqnXjiiWnu3LnplVdeSXfffXfx5wsWLEgnn3xyOv3009POnTvTokWLinx7vtRD0Q4AADVas2ZNuvDCC7uvz549u/jnFVdcke666660adOmtGHDhu4/z4X6nDlzikL+sMMOK4r3hx56KE2fPj3VQ9EOAEAlmiEeM3ny5H7vlwv3nj7xiU8UlwNlEBUAAIJTtAMAQHDiMQAAVKIZ4jFDRacdAACCU7QDAEBw4jEAAFRCPKY8nXYAAAhO0Q4AAMGJxwAAUAnxmPJ02gEAIDhFOwAABCceAwBAJcRjytNpBwCA4BTtAAAQnHgMAACVEI8pT6cdAACCU7QDAEBw4jEAAFRCPKY8nXYAAAhO0Q4AAMGJxwAAUAnxmPIU7S1q8eLFe902c+bMIVkLAEP7+x+ITzwGAACC02kHAKAS4jHl6bQDAEBwinYAAAhOPKbigZ8DGfY0PApAMw+1GpJtfuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeor3BLV68eK/bZs6cOeSPBcDQ6+v3eoTHAuonHgMAAMHptAMAUGlEhvrptAMAQHCKdgAACE48pmKGPQHAYGursntMeTrtAAAQnKIdAACCE48BAKAS4jHl6bQDAEBwinYAAAhOPAYAgEqIx5Sn0w4AAMEp2gEAIDjxGAAAKiEeU56inX45gytA4/6+BpqHeAwAAASn0w4AQCXEY8rTaQcAgOAU7QAAEJx4TBMOexoeBSDSUKshWbqIx5Sn0w4AAMEp2gEAIDjxGAAAKiEeU56inbrJzAMMLRlxaD3iMQAAEJxOOwAAlRCPKU+nHQAAglO0AwBAcOIxLTq0dCAnbwKguRhspSriMeXptAMAQHCKdgAACE48BgCASojHlKfTDgAAwem0B9VoZx1ttPUCNIpGGxJttPVCo1C0AwBQCfGY8sRjAAAgOEU7AAAEJx4DAEAlxGPKU7S3KIOjAK3JoCg0JvEYAAAITqcdAIBKiMeUp9MOAADBKdoBACA48ZgG0mjDo422XoCh1mhDoo22XoaeeEx5Ou0AABCcoh0AAIITjwEAoBLiMeXptAMAQHA67XQzOArQXAyKQvNQtAMAUAnxmPLEYwAAIDhFOwAABCceAwBAJcRjylO0N7hGGx7dc72R1wow2BppULSR1grNSDwGAACC02kHAKDSiAz102kHAIDgdNoZ0sx8o2XyAaJmwmXOobkp2gEAqITdY8oTjwEAgOAU7QAAEJx4DAAAlRCPKU/R3oQMdwIw0H+PAENLPAYAAILTaQcAoBLiMeXptAMAQHCKdgAACE48hro5SypA/b8nB5JBURqVeEx5Ou0AABCcoh0AAIITjwEAoBLiMeXptAMAQI1WrVqVLr300jR69OjU1taWHnjggf3eZ+XKlemcc85Jhx56aDrllFPSbbfdluql094i9hxamjlz5qA+/kA/h7O8AlE1w9CpwVao3fbt29OZZ56ZPvrRj6YZM2bs9/j169en6dOnp4997GNp0aJF6Zvf/Gb6+Mc/no499tia7t9F0Q4AQCWaIR4zbdq04lKr3FU/8cQT04IFC4rrp512WlqzZk2aP39+XUW7eAwAAC2to6Oj12XHjh0D9tirV69OU6ZM6XXb1KlTi8J9165dNT+Ooh0AgJY2ZsyYNGLEiO7LvHnzBuyxN2/enEaOHNnrtnx99+7daevWrTU/jngMAAAtHY/ZuHFjam9v7759+PDhA/o8eWC1r+fd8/b+KNpbVBWDnYZTgWZj6BSaU3t7e6+ifSAdf/zxRbe9py1btqRhw4alo48+uubHEY8BAIBBMmHChLR8+fJetz366KNp/Pjx6eCDD675cRTtAABUGo+JdKnXa6+9lp555pni0rWlY/73DRs2FNfnzp2bLr/88u7jZ82alV5++eU0e/bs9MILL6Q777wz3XHHHWnOnDl1Pa94DAAA1Cjv+nLhhRd2X8/FeHbFFVeku+66K23atKm7gM/Gjh2bli1blq677rp0yy23FCdluvnmm+va7jFTtAMAQI0mT57cb4c+F+57euc735mefvrpdCAU7TQVZ04FBvP3CdCcu8c0Apl2AAAITtEOAADBiccAAFAJ8Zjy2jprDPPUc8YmmsdAn3Cp6seP9rxATEOVX2+GkzURT8ScdkdHRxoxYkR6z3veU9fe5INt165d6atf/Wratm3boJ1caaCIxwAAQHDiMQAAVEI8pjyddgAACE7RDgAAwRlEpW7NOJxqMBVay1AMaBo6pSqRB1GnT58ebhB12bJlBlEBAIADJx4DAADB2T0GAIBK2D2mPJ12AAAIziAqA8JwKhCVoVNaTeRB1GnTpoUbRH344YcbYhBVPAYAgEqIx5QnHgMAAMEp2gEAIDjxGAAAKiEeU55BVAZNMw6nRl4HtJqhGDDtizOdEk3kQdSpU6eGG0T92te+1hCDqOIxAAAQnHgMAACVEI8pT6cdAACCU7QDAEBwBlGplOFUoAxDp9Acg6iXXHJJuEHU5cuXG0QFAAAOnHgMAAAEZ/cYAAAqYfeY8nTaAQAgOIOotOTZRCOcxTTCGiCCCEOmQ7WGCK+d5hN5EPVd73pXuEHUr3/96w0xiCoeAwBAS3+oaATiMQAAEJyiHQAAghOPAQCgEnaPKc8gKuEMxYBm5KHQyGuDRh2yHIq1RX4/aC6RB1EvvvjiNGxYnJ7x7t270z//8z83xCCqeAwAAAQX56MOAABNTTymPJ12AAAITqadhiDnHvM9ojU1WjZbfp1WEznTftFFF4XLtD/22GMNkWmP864BANDUxGPKE48BAIDgFO0AABCceAwAAJUQjynPICoNK8rgZZR1NOt6qY4B09Z432h+kQdRJ0+eHG4QdcWKFQ0xiCoeAwAAwcX5qAMAQFMTjylPpx0AAIJTtAMAQHAGUWkqkYcsI6+tFdbfShp9MDLy+iOvDRphEHXSpEnhBlFXrVplEBUAADhw4jEAABBcnO8nAABoanaPKU+nHQAAgjOISkuKOlQZdV1VaKXX3srDjFFfe9R1QbMNok6cODHcIOoTTzzREIOocd41AACamnhMeeIxAAAQnKIdAACCE48BAKAS4jHlGUSFBhyCbLT10vwabZCz0dYLzTKI+o53vCPcIOo3v/nNhhhEFY8BAIDg4nzUAQCgqYnHlKfTDgAAwSnaAQAgOPEYqGMoLcoAaKOtl8bVaAObjbZeaDXiMeXptAMAQHCKdgAACE48BgCASojHlKdohybOkg9kvjfKa6I1c93N+JoA6iEeAwAAwem0AwBQCfGY8nTaAQAgOEU7AAAEJx4DQzQ012iDnQM9CNhor38wtfqQZau/fmgl4jHl6bQDAEBwinYAAAhOPAYAgEqIx5Sn0w4AAMHptMMQabSzqw40w4fNz88YYOAo2gEAqIR4THniMQAAEJyiHQAAghOPAQCgEuIx5SnaoYmH+Zp1iJXqGCYFiEE8BgAAgtNpBwCg0ogM9dNpBwCA4BTtAAAQnHgMNLGyQ4QGWJuPgVIgArvHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp9MODMnQomHXat9vABqboh0AgEqIx5QnHgMAAMEp2gEAIDjxGGBIyHEDtB7xmPJ02gEAIDhFOwAABCceAwBAJcRjytNpBwCA4BTtAAAQnHgMAACVEI8pT6cdAACCU7QDAEBw4jEAAFRCPKY8nXYAAAhO0Q4AAMGJxwAAUAnxmPJ02gEAIDhFOwAABCceAwBAJcRjytNpBwCA4BTtAAAQnHgMAACVEI8pT6cdAACCU7QDAEBw4jEAAFRCPKY8nXYAAKjDrbfemsaOHZsOPfTQdM4556Qnnnhin8euWLEitbW17XV58cUX63lKRTsAANTq3nvvTddee2268cYb09q1a9PEiRPTtGnT0oYNG/q930svvZQ2bdrUfTn11FNTPdo68/cUtRzY1lbXAwMAUL0aS7tKdXR0pBEjRqRx48algw46aKiX0+31118viumNGzem9vb27tuHDx9eXPpy7rnnprPPPjstXLiw+7bTTjstXXbZZWnevHl9dtovvPDC9IMf/CAdeeSRpdcqHgMAQEsbM2ZM8aGi69JX8Z3t3LkzPfXUU2nKlCm9bs/Xn3zyyX6f46yzzkqjRo1KF198cXr88cfrXqNBVAAAWtrGPjrtfdm6dWvRnR85cmSv2/P1zZs393mfXKjffvvtRfZ9x44d6Utf+lJRuOcO/KRJk2peo6IdAICW3j2mvb29V9Feb2w8P86+ouQ5EpQvXSZMmFB8SJg/f35dRbt4DAAA1OCYY44pMvl7dtW3bNmyV/e9P+edd15at25dqoeiHQAAanDIIYcUMZfly5f3uj1fP//881Ot8q4zOTZTD/EYAABaOh5Tj9mzZ6ePfOQjafz48UXUJefV83aPs2bNKv587ty56ZVXXkl33313cX3BggXp5JNPTqeffnoxyLpo0aK0ZMmS4lIPRTsAANToQx/6UHr11VfTTTfdVOy3fsYZZ6Rly5alk046qfjzfFvPPdtzoT5nzpyikD/ssMOK4v2hhx5K06dPT/WwTzsAQBOJ1Mnec5/2fEKhaPu0r1u3Lm3btq2uQdShoNMOAEAlmiEeM1QMogIAQHCKdgAACE48BgCASojHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp2gHAIDgxGMAAKhMI53QKBKddgAACE7RDgAAwYnHAABQCbvHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp2gHAIDgxGMAAKiEeEx5Ou0AABCcoh0AAIITjwEAoBLiMeXptAMAQHCKdgAACE48BgCASojHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp2gHAIDgxGMAAKiEeEx5Ou0AABCcoh0AAIITjwEAoBLiMeXptAMAQHCKdgAACE48BgCASojHlKfTDgAAwSnaAQAgOPEYAAAqIR5Tnk47AAAEp2gHAIDgxGMAAKiEeEx5Ou0AABCcoh0AAJolHpO/zgAAgLLEY8rTaQcAgOAU7QAAEJzdYwAAqIR4THk67QAAEJyiHQAAghOPAQCgEuIx5em0AwBAcIp2AAAITjwGAIBKiMeUp9MOAADBKdoBACA48RgAACohHlOeTjsAAASnaAcAgODEYwAAqIR4THk67QAAEJyiHQAAghOPAQCg0ogM9dNpBwCA4BTtAAAQnHgMAAAtGY3pDLae/ui0AwBAcIp2AAAITjwGAICWjKN0BltPf3TaAQAgOEU7AAAEJx4DAEBLxlE6g62nPzrtAAAQnKIdAACCE48BAKAl4yidwdbTH512AAAITtEOAADBiccAANCScZTOYOvpj047AAAEp2gHAIDgxGMAAGjJOEpnsPX0R6cdAACCU7QDAEBw4jEAALRkHKUz2Hr6o9MOAADBKdoBACA48RgAAFoyjtIZbD390WkHAIDgFO0AABCceAwAAC0ZR+kMtp7+6LQDAEBwinYAAAhOPAYAgJaMo3QGW09/dNoBACA4RTsAAAQnHgMAQEvGUTqDrac/Ou0AABCcoh0AAIITjwEAoCXjKJ3B1tMfnXYAAAhO0Q4AAMGJxwAA0JJxlM5g6+mPTjsAAASnaAcAgODEYwAAaMk4Smew9fRHpx0AAIJTtAMAQHDiMQAAtGQcpTPYevqj0w4AAMEp2gEAIDjxGAAAWjKO0hlsPf3RaQcAgOAU7QAAEJx4DAAALRlH6Qy2nv7otAMAQHCKdgAACE7RDgBApZGUKJeybr311jR27Nh06KGHpnPOOSc98cQT/R6/cuXK4rh8/CmnnJJuu+22up9T0Q4AADW6995707XXXptuvPHGtHbt2jRx4sQ0bdq0tGHDhj6PX79+fZo+fXpxXD7+hhtuSNdcc01asmRJqkdbZyMl8AEAaDgdHR1pxIgRKapt27al9vb2mo4999xz09lnn50WLlzYfdtpp52WLrvssjRv3ry9jr/++uvT0qVL0wsvvNB926xZs9Kzzz6bVq9eXfMaddoBAEit/qGio8dlx44dfR63c+fO9NRTT6UpU6b0uj1ff/LJJ/u8Ty7M9zx+6tSpac2aNWnXrl01r1HRDgDAoDrkkEPS8ccfH/JdPvzww9OYMWOKbwK6Ln11zLOtW7em119/PY0cObLX7fn65s2b+7xPvr2v43fv3l08Xq3s0w4AwKDKA5g525071dF0dnamtra2XrcNHz683/vseXxfj7G/4/u6vT+KdgAAKinc86WRHXPMMemggw7aq6u+ZcuWvbrpXfI3DH0dP2zYsHT00UfX/NziMQAAUGPMJ2/duHz58l635+vnn39+n/eZMGHCXsc/+uijafz48enggw9OtVK0AwBAjWbPnp2+8IUvpDvvvLPYEea6664rtnvMO8Jkc+fOTZdffnn38fn2l19+ubhfPj7f74477khz5sxJ9RCPAQCAGn3oQx9Kr776arrpppvSpk2b0hlnnJGWLVuWTjrppOLP820992zPJ2HKf56L+1tuuSWNHj063XzzzWnGjBmpHvZpBwCA4MRjAAAgOEU7AAAEp2gHAIDgFO0AABCcoh0AAIJTtAMAQHCKdgAACE7RDgAAwSnaAQAgOEU7AAAEp2gHAIAU2/8D/+DoG5jMw4EAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -72,7 +72,7 @@
     }
    ],
    "source": [
-    "shape = [128, 128, 128]\n",
+    "shape = [256] * 3\n",
     "grid = torch.stack(meshgrid_ij(*[torch.arange(s).float() for s in shape]), -1)\n",
     "\n",
     "radius = grid.clone()\n",
@@ -80,9 +80,9 @@
     "radius = radius.square().sum(-1).sqrt()\n",
     "\n",
     "lab = torch.zeros_like(radius, dtype=torch.long)\n",
-    "lab[radius < 48] = 1\n",
-    "lab[radius < 44] = 2\n",
-    "lab[radius < 40] = 3\n",
+    "lab[radius < shape[0] * 3/8] = 1\n",
+    "lab[radius < shape[0] * 11/32] = 2\n",
+    "lab[radius < shape[0] * 5/16] = 3\n",
     "\n",
     "# insert sinus-like structure\n",
     "radius = grid.clone()\n",
@@ -93,10 +93,10 @@
     "radius[..., 1] *= 1.5\n",
     "radius = radius.square().sum(-1).sqrt()\n",
     "\n",
-    "lab[radius < 16] = 0\n",
+    "lab[radius < shape[0] * 1/8] = 0\n",
     "\n",
     "plt.figure(figsize=(10, 10))\n",
-    "plt.imshow(lab[64], cmap='gray', interpolation='nearest')\n",
+    "plt.imshow(lab[shape[0]//2], cmap='gray', interpolation='nearest')\n",
     "plt.axis('off')\n",
     "plt.title('Underlying labels')\n",
     "plt.colorbar()\n",
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -127,7 +127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x1000 with 4 Axes>"
       ]
@@ -146,17 +146,53 @@
     ")\n",
     "chi = make_chi(lab[None])[0]\n",
     "\n",
-    "chi2hz = SusceptibilityToFieldmapTransform(axis=2, mask_air=True)\n",
+    "chi = SmoothTransform(fwhm=1)(chi[None])[0]\n",
+    "\n",
+    "chi2hz = SusceptibilityToFieldmapTransform(axis=2, mask_air=False)\n",
     "fmap = chi2hz(chi[None])[0]\n",
     "\n",
     "plt.figure(figsize=(20, 10))\n",
     "plt.subplot(1, 2, 1)\n",
-    "plt.imshow(chi[64] + 0.4, cmap='gray', interpolation='nearest')\n",
+    "plt.imshow(chi[shape[0]//2] + 0.4, cmap='gray', interpolation='nearest')\n",
     "plt.axis('off')\n",
     "plt.title('Delta susceptibility map (ppm)')\n",
     "plt.colorbar()\n",
     "plt.subplot(1, 2, 2)\n",
-    "plt.imshow(fmap[64], cmap='coolwarm', interpolation='nearest', vmin=-2000, vmax=2000)\n",
+    "plt.imshow(fmap[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-2000, vmax=2000)\n",
+    "plt.axis('off')\n",
+    "plt.title('Fieldmap (Hz)')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chi2hz = SusceptibilityToFieldmapTransform(axis=2, mask_air=False, mode=\"marques\")\n",
+    "fmap_m = chi2hz(chi[None])[0]\n",
+    "\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(chi[shape[0]//2] + 0.4, cmap='gray', interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.title('Delta susceptibility map (ppm)')\n",
+    "plt.colorbar()\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(fmap_m[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-2000, vmax=2000)\n",
     "plt.axis('off')\n",
     "plt.title('Fieldmap (Hz)')\n",
     "plt.colorbar()\n",
@@ -177,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -189,13 +225,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RSS centered fmap: 77.73831176757812\n",
-      "RSS shimmed fmap: 76.5639419555664\n"
+      "RSS centered fmap: 52.93035888671875\n",
+      "RSS shimmed fmap: 52.526695251464844\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x1000 with 4 Axes>"
       ]
@@ -206,26 +242,78 @@
    ],
    "source": [
     "# simple shim (zero-th order == frequency adjust)\n",
-    "centered_fmap = fmap - fmap[fmap != 0].mean()\n",
-    "centered_fmap *= (fmap != 0)\n",
+    "centered_fmap = fmap - fmap[lab != 0].mean()\n",
+    "centered_fmap *= (lab != 0)\n",
     "\n",
     "print(\"RSS centered fmap:\", (centered_fmap ** 2).mean().sqrt().item())\n",
     "\n",
     "# optimal shim (second order)\n",
-    "shim = OptimalShimTransform(lam_grad=0)\n",
+    "shim = OptimalShimTransform(lam_grad=0, mask=(lab != 0))\n",
     "shimed_fmap = shim(fmap[None])[0]\n",
-    "shimed_fmap *= (fmap != 0)\n",
+    "shimed_fmap *= (lab != 0)\n",
     "\n",
     "print(\"RSS shimmed fmap:\", (shimed_fmap ** 2).mean().sqrt().item())\n",
     "\n",
     "plt.figure(figsize=(20, 10))\n",
     "plt.subplot(1, 2, 1)\n",
-    "plt.imshow(centered_fmap[64], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
+    "plt.imshow(centered_fmap[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
+    "plt.axis('off')\n",
+    "plt.title('Centered Fieldmap (Hz)')\n",
+    "plt.colorbar()\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(shimed_fmap[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
+    "plt.axis('off')\n",
+    "plt.title('Shimmed Fieldmap (Hz)')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RSS centered fmap: 34.38340759277344\n",
+      "RSS shimmed fmap: 33.12874221801758\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ggg==",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# simple shim (zero-th order == frequency adjust)\n",
+    "centered_fmap_m = fmap_m - fmap_m[lab != 0].mean()\n",
+    "centered_fmap_m *= (lab != 0)\n",
+    "\n",
+    "print(\"RSS centered fmap:\", (centered_fmap_m ** 2).mean().sqrt().item())\n",
+    "\n",
+    "# optimal shim (second order)\n",
+    "shim = OptimalShimTransform(lam_grad=0, mask=(lab != 0))\n",
+    "shimed_fmap_m = shim(fmap_m[None])[0]\n",
+    "shimed_fmap_m *= (lab != 0)\n",
+    "\n",
+    "print(\"RSS shimmed fmap:\", (shimed_fmap_m ** 2).mean().sqrt().item())\n",
+    "\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(centered_fmap_m[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
     "plt.axis('off')\n",
     "plt.title('Centered Fieldmap (Hz)')\n",
     "plt.colorbar()\n",
     "plt.subplot(1, 2, 2)\n",
-    "plt.imshow(shimed_fmap[64], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
+    "plt.imshow(shimed_fmap_m[shape[0]//2], cmap='coolwarm', interpolation='nearest', vmin=-500, vmax=500)\n",
     "plt.axis('off')\n",
     "plt.title('Shimmed Fieldmap (Hz)')\n",
     "plt.colorbar()\n",
@@ -242,12 +330,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x2000 with 8 Axes>"
       ]
@@ -260,7 +348,7 @@
     "# smooth vdm a bit\n",
     "smooth = SmoothTransform(fwhm=3)\n",
     "smooth_fmap = smooth(shimed_fmap[None])[0]\n",
-    "smooth_mask = smooth((fmap != 0).float()[None])[0]\n",
+    "smooth_mask = smooth((chi != 0).float()[None])[0]\n",
     "smooth_fmap[smooth_mask > 0] /= smooth_mask[smooth_mask > 0]\n",
     "\n",
     "# convert to voxel shifts\n",
@@ -304,6 +392,71 @@
     "plt.colorbar()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# smooth vdm a bit\n",
+    "smooth = SmoothTransform(fwhm=3)\n",
+    "smooth_fmap_m = smooth(shimed_fmap_m[None])[0]\n",
+    "smooth_mask = smooth((chi != 0).float()[None])[0]\n",
+    "smooth_fmap_m[smooth_mask > 0] /= smooth_mask[smooth_mask > 0]\n",
+    "\n",
+    "# convert to voxel shifts\n",
+    "hz2vdm = HertzToVoxelShiftTransform()\n",
+    "vdm_m = hz2vdm(smooth_fmap_m[None])[0]\n",
+    "\n",
+    "# apply vdm\n",
+    "# = push voxels to their distorted location\n",
+    "# = pull via the inverse vdm\n",
+    "ivdm_m = -vdm_m\n",
+    "\n",
+    "# apply voxel displacement map\n",
+    "zreadout_m = ApplyB0DistortionTransform(vdm=ivdm_m[None], axis=2)\n",
+    "yreadout_m = ApplyB0DistortionTransform(vdm=ivdm_m[None], axis=1)\n",
+    "xreadout_m = ApplyB0DistortionTransform(vdm=ivdm_m[None], axis=0)\n",
+    "\n",
+    "wlabz_m = zreadout_m(lab[None])[0]\n",
+    "wlaby_m = yreadout_m(lab[None])[0]\n",
+    "wlabx_m = xreadout_m(lab[None])[0]\n",
+    "\n",
+    "plt.figure(figsize=(20, 20))\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.imshow(vdm_m[64], cmap='coolwarm', interpolation='nearest', vmin=-10, vmax=10)\n",
+    "plt.axis('off')\n",
+    "plt.title('Voxel displacement map (voxels)')\n",
+    "plt.colorbar()\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.imshow(wlabz_m[64], cmap='gray', interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.title('Z-axis Readout Distortion')\n",
+    "plt.colorbar()\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.imshow(wlaby_m[64], cmap='gray', interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.title('Y-axis Readout Distortion')\n",
+    "plt.colorbar()\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.imshow(wlabx_m[64], cmap='gray', interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.title('X-axis Readout Distortion')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {