A Generic Framework for Non-rigid Registration Based
on Non-uniform Multi-level Free-Form Deformations
Julia A. Schnabel1, Daniel Rueckert2, Marcel Quist3, Jane M. Blackall1,
Andy D. Castellano-Smith1, Thomas Hartkens1, Graeme P. Penney1, Walter A. Hall4,
Haiying Liu5, Charles L. Truwit5, Frans A. Gerritsen3, Derek L. G. Hill1, and
David J. Hawkes1
1 Computational Imaging Science Group, Radiological Sciences and Medical Engineering,
Guy’s Hospital, King’s College London, UK
julia.schnabel@kcl.ac.uk
2 Visual Information Processing, Dept. Computing, Imperial College of Science, Technology
and Medicine, London, UK
3 EasyVision Advanced Development, Philips Medical Systems, Best, NL
4 Dept. Neurosurgery, University of Minnesota, Minneapolis, MN, USA
5 Dept. Radiology, University of Minnesota, Minneapolis, MN, USA
Abstract. This work presents a framework for non-rigid registration which ex-
tends and generalizes a previously developed technique by Rueckert et al. [1].
We combine multi-resolution optimization with free-form deformations (FFDs)
based on multi-level B-splines to simulate a non-uniform control point distribu-
tion. We have applied this to a number of different medical registration tasks
to demonstrate its wide applicability, including interventional MRI brain tissue
deformation compensation, breathing motion compensation in liver MRI, intra-
modality inter-modality registration of pre-operative brain MRI to CT electrode
implant data, and inter-subject registration of brain MRI. Our results demonstrate
that the new algorithm can successfully register images with an improved perfor-
mance, while achieving a significant reduction in run-time.
1 Introduction
Non-rigid image registration is playing an increasingly important role in both clinical
and research applications. Even though there is a large number of registration methods
to be found in the literature [2,3], existing methods have usually been designed for or
fine-tuned to specific, mostly single-modality applications. In this paper we propose
a flexible framework for non-rigid registration which covers a range of deformation
tasks in medical applications. This framework extends and generalizes a previously
published method based on free-form deformations (FFDs) using B-splines which was
developed for registration of contrast enhanced MR mammography images [1]. The
original method was formulated as a two-stage process: first, the global motion is cor-
rected using a rigid or affine transformation. The global motion then becomes the start-
ing estimate for the second stage, where the local motion is further modelled using
FFDs based on B-splines. Manipulating the underlying mesh of control points yields a
W. Niessen and M. Viergever (Eds.): MICCAI 2001, LNCS 2208, pp. 573–581, 2001.
c© Springer-Verlag Berlin Heidelberg 2001
574 J.A. Schnabel et al.
smooth deformation of structures embedded in the image, where the control points act
as parameters of the transformation. The combined motion model can be written as:
T(x, y, z) = Tglobal(x, y, z) + Tlocal(x, y, z) (1)
with the local motion at each point given by the 3D tensor product of the familiar
1D cubic B-splines [4]. The optimal transformation T is determined by minimizing a
registration cost function:
C = −Csimilarity(IA,T(IB)) + λCdeformation(T) (2)
The similarity term maximizes the voxel similarity between the image pair, and is cho-
sen to be normalized mutual information (NMI) [5]. The deformation cost term is de-
fined as the 3D equivalent of a thin-plate bending energy in order to maximize the
smoothness of the transformation, weighted by a factor λ.
The performance of this registration method is limited by the resolution of the con-
trol point mesh, which is linearly related to the computational complexity: more global
and intrinsically smooth deformations can only be modelled using a coarse control point
spacing, whereas more localized and intrinsically less smooth deformations require a
finer spacing. If it is known a priori which magnitude of deformation is to be expected,
the mesh resolution can be chosen accordingly, and folding can be penalized using a
suitable deformation cost term. However, often a range of deformations needs to be
modelled which cannot be captured by a single mesh resolution. More importantly, it
may be desirable to have a non-uniform control point spacing to restrict deformation
to localized regions in the image pair, while excluding regions where the images are
already in alignment, form part of image background, or have been identified as rigid
bodies. For many applications, non-uniform control point spacing may not be neces-
sary, however the computational complexity can be decreased without compromising
the registration performance. In the following, we present our multi-resolution non-
rigid registration framework that can have a non-uniform control point distribution.
2 Framework
2.1 Multi-resolution Registration
To model a large range of deformations, a multi-resolution mesh representation is need-
ed. This issue has been addressed in [1] in a coarse-to-fine fashion where the mesh is
progressively refined by alternating between mesh deformation and mesh subdivision
using suitable B-spline subdivision techniques [6]. As an alternative, Rueckert et al. [1]
formulated an approach using multi-level B-splines to create a hierarchy of local defor-
mation meshes [4] which will be presented in this work. Multi-resolution registration
using this concept is then achieved by deforming a sequence of control point meshes
Φ1, · · · , ΦH of arbitrarily increasing resolution using multi-level B-splines (each level
corresponding to a single-resolution B-spline mesh). After registration, the local de-
formation of each point in the image volume domain is given by the sum of the local
deformations across levels:
Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations 575
Tlocal(x, y, z) =
H∑
h=1
Thlocal(x, y, z) (3)
where each Thlocal(x, y, z) is computed with respect to the B-spline of that level h. To
avoid the overhead of recalculating previously recovered local deformations up to level
h− 1, these can be efficiently pre-computed for the deformation of level h.
�
�
�
�)
Folding
+ + =
Fig. 1. 2D slices through 3D single- and multi-resolution FFDs for a synthetic reg-
istration task. Top: Single-resolution FFDs are limited by low mesh resolutions, and
may develop folding at high resolutions (see arrow). Bottom: Multi-resolution FFDs
are modelling deformations of different magnitudes at the respective mesh resolution,
without developing any folding. Note that at intermediate resolution, deformations of
the preceding lower resolution are partially corrected.
Fig. 1 shows 2D slices through 3D single- and multi-resolution FFDs for the regis-
tration of a synthetic cube and sphere. One can observe that multi-resolution FFDs can
model large deformations without folding at higher resolution levels, whereas FFDs at
a single mesh resolution are locally not flexible enough at low resolutions, and can be
subject to folding at higher resolutions without any additional smoothness constraints.
Hence, when using multi-resolution FFDs, the deformation cost term regularizing the
smoothness of the deformation in Eq. 2 is no longer crucial and and we can set λ = 0.
2.2 Non-uniform Control Point Spacing
Non-uniform control point spacing for FFDs is a well known concept from computer
graphics. For example, Non-Uniform Rational B-Splines (NURBS) [7] have been used
for FFDs to represent and interactively manipulate object shapes. We propose to sim-
ulate this concept by extending the multi-resolution registration presented in section
2.1, while avoiding the more complex lattice traversal and control point manipulation
of NURBS. We introduce a control point status S associated with each control point at
each level in the multi-resolution mesh hierarchy, marking it as either active or passive.
576 J.A. Schnabel et al.
Fig. 2. Simulation of non-uniform control point spacing by combining multi-resolution
registration with a control point status. Active control points are marked as black, pas-
sive ones as grey.
Active control points are allowed to move during the registration process, whereas pas-
sive control points remain fixed. Fig. 2 illustrates this concept. Prior knowledge derived
from segmentation may be used to assign a control point status. Alternatively, we have
developed two different, complementary approaches which derive the status automati-
cally from the image data. Both investigate the local B-spline support regions Φi,j,k of
each control point φi,j,k , which provide natural bounds for the local support region that
a B-spline control point at a certain resolution level has. Within these bounds, and over
the whole image domain, a statistical measureM can be calculated from the underlying
image data to determine the status of each control point as:
S(φi,j,k) =
{
active if M(Φi,j,k) >
passive otherwise (4)
where
is a selection threshold, which may be normalized by the global measure M.
In the following, we distinguish between measures computed over the reference image,
and joint measures computed over the image pair.
Reference image measures characterize the reference image locally, and are appli-
cable for serial registration to a baseline scan, inter-subject registration to a common
reference subject, or registration to an atlas. They are computed prior to the registration
process for all undeformed meshes in the B-spline hierarchy. One such measure which
describes the information content of the image intensity distribution is the Shannon-
Wiener entropy H = −∑ p(a) log p(a), where p(a) is the probability that an im-
age voxel has intensity a. Other local intensity measures include intensity variance σ,
features based on higher order image differentials, or more complex noise estimators.
Using such measures, image background regions can be naturally excluded without ex-
plicit segmentation, and local image structure is deformed with the appropriate mesh
resolution. For the inter-subject registration application in this work, we have investi-
gated both H and σ as suitable reference image measures.
Joint image pair measures describe the degree of local image alignment. They need
to be recomputed for each level after the deformation of the preceding levels for pro-
gressive adaptation, and include local measures such as intensity or gradient differences,
local correlation, and information-theoretic measures computed over local histograms.
These measures should be normalized by the integration measure over the whole image
domain. We propose a more consistent, generalized joint image pair measure which is
based on the local gradient ∂C/∂φi,j,k of the global registration cost term of Eq. (2),
which does not rely on potentially insufficient statistics defined over small local image
Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations 577
regions or histograms. Computing the gradient of the cost functions to determine local
adaptivity has also very recently been proposed in [8].
3 Applications
In the following, we present example results for the new framework for intra- and inter-
modality, intra-subject and inter-subject registration tasks. Figs. 3-6 show example 2D
slices through 3D volumes, where registration has been performed on 3D volume pairs.
Intra-subject intra-modality registration:
(a) (b) (c)
Fig. 3. Intra-subject registration of pre- to post-operative brain MRI. Subtraction images
after registration: (a) rigid, and non-uniform non-rigid using joint image pair adaptation
at (b) 15mm spacing, and (c) 15mm refined to 7.5mm spacing.
Fig. 3 shows an example slice through subtraction images after rigid and non-rigid
registration of an MP-RAGE MR brain scan before dura puncture to an MP-RAGE MR
brain scan after a functional surgical procedure where a unilateral thalamic stimulator
was inserted stereotactically to suppress tremor. After rigid registration, a shift in the
brain tissue can be observed which may have been caused by patient positioning on
the operating table, loss of cerebral spinal fluid, and tissue deformation. It was previ-
ously shown that the non-rigid algorithm by Rueckert et al. [1] can correct for this shift
[9]. The locality of the shift, and the expected magnitude of up to 15mm for this case
can be used to adapt B-spline meshes of 15mm and refinement to 7.5mm control point
spacing using the local similarity gradient as a joint image pair measure M (
= 0.01).
Control points in areas where shift can be observed, and where M >
, are automati-
cally marked as active, and as passive in remaining areas which are sufficiently aligned
after rigid registration. This has reduced the number of active control points and the
associated computing time by 60% (15mm) and 97% (7.5mm) for this case.
Fig. 4 shows an example for registration of liver MRI between exhale and inhale
positions for a volunteer. To correct for the rigid, mostly translational movement of the
578 J.A. Schnabel et al.
(a) (b)
Fig. 4. Intra-subject registration of liver MRI. Inhale contour overlays onto exhale im-
age after (a) rigid and (b) non-rigid registration. Deformation was restricted to the liver
at exhale.
liver, and the additional deformation within the liver tissue, we have segmented the liver
in the reference image (at exhale) prior to registration to exclude all external structure
from the registration process. Non-rigid registration was performed using a mesh reso-
lution of 23.12mm, chosen as a multiple of the in-plane voxel size. Note the improved
alignment of the surface and vessels within the liver after non-rigid registration, shown
as contour overlays. The non-uniform control point spacing has reduced the computing
time by around 94%.
Intra-subject inter-modality registration:
(a) (b) (c)
Fig. 5. Intra-subject registration of pre-operative brain MRI to CT with electrode im-
plants. (a) CT, and CT contour overlays showing brain surface and electrodes onto MR
after (b) rigid and (c) non-rigid registration. Deformation was restricted to the intra-
cranial cavity.
Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations 579
Fig. 5 shows an example for inter-modality registration for a pre-operative MR
brain scan to a post-operative CT scan of a patient with epilepsy after an electrode
grid implantation to map electrical activity. Similar to the liver registration task, we
have performed a segmentation prior to registration. The reason for this is that the high
intensity values of the electrode grid would otherwise be mapped to the dura in the
MR scan. Adapting the mesh with respect to setting only the control points within the
intra-cranial cavity to active reduced the non-rigid registration time by 85%, and, more
importantly, the shift of the brain surface caused by the grid implantation was recovered
without deforming the dura. Another example for intra-subject inter-modality registra-
tion, which is not shown in this paper, is the registration of pre-contrast to contrast-
enhanced MR mammographic images. In [1], B-spline subdivision was proposed to
perform a coarse-to-fine deformation compensation for that application, which alterna-
tively can be achieved using the multi-resolution scheme presented in this work.
Inter-subject intra-modality registration:
(a) (b) (c)
(d) (e) (f)
Fig. 6. Inter-subject registration of brain MRI of 7 healthy subjects. ROI of (a) reference
subject and averages after registration: (b) affine, (c) uniform non-rigid at 20mm, (d)
uniform non-rigid at 20mm, refined to 10mm and 5mm, and non-uniform non-rigid at
20mm, refined to 10mm and 5mm using (e) entropy H and (f) variance σ as reference
image measures.
As an example for inter-subject, intra-modality registration, we have registered MR
T1-weighted brain volumes of 6 control subjects to one reference control subject. After
580 J.A. Schnabel et al.
registration, averages of the aligned image volumes were generated. Fig. 6 shows an
example region of the reference subject and averages after affine and non-rigid registra-
tion using uniform control point spacing of 20mm, and refinement via 10mm to 5mm
spacing. One can visually perceive the improved alignment when going from affine to
non-rigid registration using 20mm spacing, as well as an improved refinement to 5mm.
We have also used non-uniform control point spacing on the basis of the local entropy
(
= 0.75Htotal) and variance (
= 0.5σtotal) as reference image measures. The result
is comparable to the uniform mesh refinement, but needed considerably less computing
time (reduction by 43-66% for M = H and by 29-55% for M = σ, for increasing
mesh resolutions, respectively). Using a 5mm control point spacing from the start was
found to be computationally prohibitively expensive. Both reference image measures
were found to be robust and well-behaved estimates with respect to the control point
support regions and a measure-specific, but constant threshold
across resolutions.
4 Discussion and Conclusions
We have presented a non-rigid image registration framework based on multi-resolution
refinement of adaptable FFDs using B-splines, which extends the work of Rueckert et
al. [1]. We have applied the framework to a variety of medical registration problems,
demonstrating its flexibility and wide applicability. In particular, its ability to constrain
deformation to selective regions, on the basis of segmentation, or automatically using
reference or joint image pair measures provides a large gain in computing time without
any apparent loss of registration quality. Although folding of the deformation field was
not explicitly constrained to ensure diffeomorphism, we have found no occurrences of
folding because of the intrinsically smooth deformation modelling capabilities of the
multi-resolution FFD. Validation of non-rigid registration is a challenging task, and of-
ten limited to visual assessment. We have recently developed a biomechanical deforma-
tion simulator using finite element methods [10] which we have successfully applied to
the registration algorithm by Rueckert et al. for contrast-enhanced MR mammography
[1]. We are planning to use this method to validate and further improve the non-rigid
registration framework presented in this paper. In particular, we will investigate the pa-
rameter selection for the adaptivity criteria, and the range and sampling values for the
multi-level FFDs. For inter-subject registration, we are investigating target registration
errors on the basis of anatomical landmarks which is subject to ongoing work.
Acknowledgements
JAS is funded by Philips Medical Systems EV-AD, DR is partially funded by EPSRC
GR / N / 24919, GPP is funded by EPSRC GR / M53752, ADCS and TH are funded
by EPSRC GR / M47294. The electrode data were provided by Prof. Charles Polkey
from Dept. Healthcare, KCL, the brain control data by the Institute of Psychiatry, KCL,
the interventional MRI data by the University of Minnesota, and the liver data were
acquired at Guy’s Hospital.
Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations 581
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Transactions on Medical Imaging, 18(8):712–721, 1999.
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