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Focused Registration of Tracked 2D US to 3D
CT Data of the Liver
Janine Olesch 1,2,3, Bernd Fischer 1,2
1Institute of Mathematics and Image Computing, University of L¨
ubeck, Germany;
2Fraunhofer MEVIS, Project Group Image Registration, L¨
ubeck, Germany;
3Graduate School for Computing in Medicine and Life Sciences, Univ. L¨
ubeck,
Germany
olesch@mic.uni-luebeck.de
Abstract. The paper deals with the registration of pre-operative 3D-
CT-data to tracked intra-operative 2D-US-slices in the context of liver
surgery. To bring such a method to clinical practice, it has to be fast
and robust. In order to meet these demanding criteria, we propose two
strategies. Instead of applying a time-consuming compounding process
to obtain a 3D-US image, we use the 2D-slices directly and thereby dras-
tically reduce the complexity and enhance the robustness of the scheme.
Naturally, the surgeon does not need the same high resolution for the
whole liver. We make use of this fact by applying a focusing technique
to regions of special interest. With this, we reduce the overall amount
of data to register significantly without sacrificing the accuracy in the
ROIs. In contrast to other attempts, the high resolution result in the
ROI is combined in a natural way with a global deformation field to
obtain a smooth registration of the whole liver. Overall we arrive at
a method with a favorable timing. The proposed algorithm was ap-
plied to four different patient data-sets and evaluated with respect to
the reached vessel-overlap on validation slices. The obtained results are
very convincing and will help to bring non-linear registration techniques
to the operation theater.
1 Introduction
The best treatment for tumors in the liver is still their resection. This is a tricky
intervention, as one would like to remove all tumorous tissue and to preserve as
much healthy liver as possible. To this end planning-data is acquired and pro-
cessed, which ideally provides an optimal resection plan. As the liver is deformed
during the intervention, intra-operative navigation is needed to guide the surgeon
with respect to the planning-data. To enable the navigation during the inter-
vention, ultrasound (US) is often the method of choice. It is the purpose of this
paper to come up with a reliable and fast scheme which aligns the pre-processed
3D planning-data (CT) to the intra-operative situation, given the information
by the tracked 2D-US-slices. Instead of determining a non-linear transformation
for the whole liver, we propose a different strategy: In the first step we perform
80 Olesch & Fischer
a fast rigid pre-alignment based on the full CT-volume. The second step is then
used to improve this result non-linearly in a region of interest (ROI). As the
non-linear step is computationally more expensive as opposed to the rigid one
and the surgeon is not interested in the same resolution throughout the whole
liver, it makes very much sense to focus on a ROI. To achieve nevertheless a
transformation for the full volume, we apply a technique developed in [1], where
the authors introduce the idea of focused registration. Figure 1 (a) visualizes
pre-processed 3D planning-data superimposed by the proposed resection plane,
the ROI (b), (c), and some of the US-slices (d). It should be noted, that the
ROI is automatically derived from the location of the resection plane and the
associated US-slices.
2 Materials and Methods
We start by briefly describing the different steps of our proposed method. First
we select jrigid slices from the US-data based on an entropy measure. Next,
the rigid registration process with respect to these slices and the CT-data takes
place. Then, based on the resection plane, we identify a region of interest ΩROI
in the CT-volume. Subsequently, the US-slices within the ROI are determined.
For the non-linear step, a subset of jnl slices is selected. Naturally, the non-linear
step makes use of the rigid result on TROI and is performed solely based on the
selected US-slices. Finally, the result yopt of the non-linear step is combined
with the result of the rigid step to gain the deformation for the full CT-volume.
The registration-steps described above are based on the idea of volume-to-
slice registration, which was introduced for rigid registration by Penney et al. [2]
and for non-linear registration by Heldmann and Papenberg [3]. In the following
we describe the specialized non-linear registration problem that will be solved
using the discretize-then-optimize approach in our proposed algorithm. In the
first step we apply the rigid volume-to-slice registration, as described in [4]. The
general version of the non-linear registration problem reads as follows: given a
template Tand a reference R, where T,R:Ω⊂R3→R, find a transformation
y:R3→R3such that
J(y) = D(R, T ;y) + αS(y) = min!
(a) (b) (c) (d)
Fig. 1. CT planning-data including the vessel-system (a), the lesions and a proposed
resection plane; bounding box covering the ROI (b); stripped version of ROI (c); liver
and some of the tracked 2D-US-slices (d).
Focused Registration 81
where Ddenotes a distance measure, Sa regularizer, and α∈R+a regulariza-
tion parameter.
The present registration problem is originally a multi-modal registration
problem (US-CT). To circumvent this, we make use of the fact that the ves-
sel systems are of foremost interest for the surgeon. More precisely, we only
register the segmented vessel systems TVand RV. Note, that the CT-data is
already segmented in the planning step. Consequently, we are able to treat the
former multi-modal data by the cheap sum of squared differences (SSD) distance
measure. The distance measure, which is evaluated only on the known jnl slices
Mnl
j,j= 1, ..., jnl reads
Dslice(y) =
jnl
∑
j=1 ∫Mnl
j
(TV(y(x)) − RV(x))2ds(x)
where ds(x) denotes the two-dimensional surface measure.
To arrive at smooth deformations and to regularize the ill-posed problem for
the non-linear step, a regularization term Sis needed. For the special case of
volume-to-slice registration we need a high order regularizer in order to assure a
smooth deformation field yin between the US-slices [3]. To this end, Sis chosen
as the second order curvature regularizer [5]
Scurv(y) =
3
∑
ℓ=1 ∫ΩROI
|∆yℓ|2dx
In contrast to the distance measure, the regularizer works on the full deformation
field (ΩROI instead of Mnl
j).
The discretize-then-optimize framework makes it quite straightforward to
apply the idea of focused registration to our scheme. To calculate a result, which
covers the full CT-volume, we apply Dirichlet zero boundary conditions to the
regularizer in the focused non-linear step. With this we are able to combine the
rigid and the non-linear result in an interpolation-step to an overall deformation-
field.
case 5 slices 15 slices 30 slices 50 slices
1 2.23 s 5.68 s 10.51 s 16.09 s
2 3.94 s 3.45 s 9.27 s 13.31 s
3 2.25 s 4.22 s 21.81 s 37.27 s
4 2.64 s 6.02 s 14.19 s 24.87 s
Fig. 2. Factors of improvement of the rigid registration step in relation to the vessel-
overlap before rigid registration (left) and run-times (right), for all four different cases.
82 Olesch & Fischer
In the beginning of this section, we mentioned two steps, where we are obliged
to select slices from the US-data. Those slices, in conjunction with the regular-
izer, are supposed to be sufficient to guide the registration. It is obviously
important that the selected slices are distributed throughout the volume to be
registered and that they contain meaningful information. We therefore apply a
strategy proposed by Wein et.al [6], that is, we partition the original slices into
several groups and choose slices based on their entropy with these groups. Only
the selected slices are then used to calculate the deformation. We tested differ-
ent numbers of slices and evaluated the outcome of the methods based on these
numbers. To further speed up the computation and to avoid local minima we
apply a multi-level strategy in the rigid as well as in the non-linear registration
step. We start on a broad resolution of the chosen slices and refine it, until a user
prescribed finest resolution. Simultaneously we apply a multi-scale approach to
the CT-volume that starts with the main vessels and includes stepwise finer ves-
sels using morphological operations [7]. To solve the final registration problem
we apply the discretize-then-optimize strategy [8] by evoking the Gauss-Newton
method.
3 Results
We tested the proposed algorithm on four different cases. Note that all run-
time results were obtained using MATLAB 2010b code, on a 2 ×2.8 GHz
Quad-Core Intel Xeon Mac Pro, which was not optimized for run-time. To
evaluate the algorithm, different configurations of the parameters were chosen.
For the multi-level setup-up we chose two rigid steps and two non-linear steps
in all tests. First we evaluated the influence of the number of slices used in the
rigid registration step. The validation subset of slices is fixed throughout all
our validation steps to make the results comparable. The left part of Figure 2
visualizes the factors of improvement in relation to the vessel-overlap before
rigid registration. This together with the run-time table suggests that a choice
case 5 slices 10 slices 15 slices 30 slices
1 142.63 s 241.50 s 251.42 s 267.28 s
2 105.03 s 165.39 s 226.11 s 234.00 s
3 127.84 s 167.87 s 175.85 s 211.12 s
4 204.55 s 306.64 s 317.28 s 355.18 s
Fig. 3. Factors of improvement of non-linear registration in relation to the vessel-
overlap after rigid registration (left) and run-times (right), for all four different cases.
Focused Registration 83
of 15 slices seems to be a reasonable compromise between favorable run-time and
accuracy for the rigid step. The non-linear steps are performed in the region of
interest solely and started from the rigid results based on 15 slices. We tested the
non-linear step also on different numbers of slices. Knowing that the number of
slices will have direct impact on the run-times we chose (Mnl
j, j = 5,10,15,30).
For non-linear registration the choice of αis always a crucial step, we tested
different possibilities. In our tests α= 0.5 turned out to be the best choice in
terms of improvement of the results with respect to run-times and regularity of
the resulting grid. Figure 3 indicates for the non-linear step in the regions of
interest, choices of Mnl
j, j = 5,10 seem to be equally reasonable, resulting in an
improvement up to factor of two.
4 Discussion
For the first time, the focused registration methodology is combined with rigid
and non-linear volume to slice registration techniques. We tested the method on
four clinical data-sets and observe very promising results both in terms of timing
and accuracy. Future plans include the evaluation on more clinical data-sets and
the porting of the code to a run-time optimized environment.
References
1. Papenberg N, Modersitzki J, Fischer B. Registrierung im Fokus. In: Proc BVM;
2008. p. 138–42.
2. Penney GP, Blackall JM, Hamady MS, et al. Registration of freehand 3D ultrasound
and magnetic resonance liver images. Med Image Anal. 2004;8(1):81–91.
3. Heldmann S, Papenberg N. A variational approach for volume-to-slice registration.
Lect Notes Computer Sci. 2009;4485:624–35.
4. Heldmann S, Beuthien B, Olesch J, et al. Improved minimal-invasive laproscopic
liver surgery by regstration of 3D CT and 2D ultrasound slices. Proc BMT. 2010.
5. Fischer B, Modersitzki J. Fast curvature-based registration of MR mammography
images. In: Proc BVM; 2002. p. 139–42.
6. Wein W, Brunke S, Khamene A, et al. Automatic CT-ultrasound registration for
diagnostic imaging and image-guided intervention. Med Image Anal. 2008;12:577–
85.
7. Heldmann S, Papenberg N. A scale-space approach for image registration of vessel
structures. In: Proc BVM; 2009. p. 137–41.
8. Modersitzki J. FAIR: Flexible Algorithms for Image Registration. Philadelphia:
SIAM; 2009.
