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Abstract. MR – CT image registration has been used in the liver cancer treat- ment with an open MR Scanner to guide percutaneous puncture for ablation of.
MR-CT Image Registration in Liver Cancer Treatment with an Open Configuration MR Scanner Songyuan Tang1,2, Yen-wei Chen1, Rui Xu1, Yongtian Wang2 Shigehiro Morikawa3, and Yoshimasa Kurumi3 1

College of Information Science and Engineering, Ritsumeikan University, Japan [email protected], [email protected] [email protected] 2 Department of Opto-electronic Engineering, Beijing Institute of Technology, P.R. China [email protected] 3 Shiga University of Medical Science, Japan [email protected]

Abstract. MR – CT image registration has been used in the liver cancer treatment with an open MR Scanner to guide percutaneous puncture for ablation of tumors. Due to low magnetic field and limited acquisition time, MR images do not always show the target clearly. Sometimes, assistance of CT images is helpful for the navigation to the target. The shape of the liver within the surgical procedure is different from that of preoperative CT images due to the patient position for the convenience of surgery. It is quite difficult to match the images accuracy during surgery. In this paper, we have proposed a method to improve the registration accuracy of images with an open MR scanner and preoperative CT images of the liver. The method includes three parts. Firstly a semiautomatic method is used to extract the liver from MR and CT images as region of interest (ROI). Then, an affine registration is used to match the images roughly. Finally, BSpline-based nonrigid registration is applied. The results are found to be satisfactory with visual inspection by experts and with evaluation by the distance of two liver surfaces, while comparing with other methods.

1 Introduction Recently, various minimally invasive treatments have been widely spread for the treatment of liver tumors. For the image guidance, ultrasonography has been mainly used. With developments of open configuration MR scanners, MR images have also been utilized for the navigation of minimally invasive therapies, because MR images have many advantages for image navigation, such as good soft tissue contrast, free from ionizing radiation and multiplanar capabilities. Microwave ablation, an established procedure for the treatment of liver tumors, has been successfully combined with MR image guidance [1]. At the initial stage of surgery, only real-time MR image, which is continuously acquired with gradient echo sequence within 2 seconds, is used for image guidance. The image planes including the path of the needle are interactively controlled by surgeons using an optical tracking system. The contrast of real-time MR images acquired within 2 seconds is not always satisfactory. The cases, in which real-time MR images can show the target clearly, are selected for this treatment. As the second stage, J.P.W. Pluim, B. Likar, and F.A. Gerritsen (Eds.): WBIR 2006, LNCS 4057, pp. 289 – 296, 2006. © Springer-Verlag Berlin Heidelberg 2006

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navigation software on an external computer is used in combination with real-time MR images [2, 3]. After the patient position for the treatment is fixed, multi-slice MR images are acquired and transferred to the software as 3D volume data. Real-time MR images in the corresponding planes are simultaneously displayed on the surgeon’s monitor. Multi-slice MR images just before the treatment are much better in the image quality than real-time MR images. In addition, the tumor region is manually traced and shown with color on the display. The assistance of this software is quite useful and remarkably expands the indication of this treatment. In some cases with severe chirrhosis, however, visualization of the tumor is still difficult with MR images acquired by the open configuration MR system. In such cases, the combination of preoperative CT images will be greatly helpful, if CT images can be registered to the position of MR images accurately. Liver image registration goes back to 1983 [4], but most works on liver registration are done in recent years [5]-[9]. Only one paper is found about MR liver image registration with open MR system [9]. In this paper, intensity-based and rigid transform registration is used. There are no previous reports of liver registration using intensitybased nonrigid registration. Since nonrigid registration can deform an image, it is possible to get better result than that of rigid registration. In this paper, we have developed a non-rigid registration method to match MR and CT images in the liver tumor treatment with open MR system. Since CT images are acquired before surgery, we have enough time to carefully segment it by manual and obtain accuracy CT images of the liver. When multi-slice MR images are acquired during surgery, deformable model is adopted first to roughly segment the images, and then the roughly segmented images are trimmed manually. After segmentation, the intensity-based affine registration is applied to both segmented images, and then BSpline-based nonrigid registration is applied. Both registrations use mutual information (MI) as similarity metrics since MI have been proven robust in the multi-model image registration [10]. The registration accuracy is accessed by the visual inspection and the distance of two liver surfaces. Compared with the Andres’ method [9], the results are satisfied.

2 Method 2.1 Liver Segmentation of MR Images Since the intensity of liver and other tissues in abdomen is similar in the MR images, segmentation of the liver is difficult. Both consideration of speed and accuracy, we combine the automatic and the manual method to segment the liver. Firstly, we adopt automatic segment to segment liver roughly and remove the most non-liver part, such as wall of abdomen, spine etc, and then manually segment liver organ with a little effort. We adopt the deformable surface model to segment liver due to its robust and fast. The liver surface and soft tissue are modeled by a surface tessellation using connect triangles. The initial model is a tessellated sphere, which is put at the center of the image. Each vertex of the sphere surface is updated to approach the liver or soft tissue surface. When the vertex of the sphere surface

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approaches the liver or soft tissue surface enough, the updating is stopped. Detailed can be found in refs. [11][12]. 2.2 Affine Registration Mutual information is the most robust similarity metrics for the multimodal image registration and is widely used [13][14]. Therefore, we select it to match the two liver images. We use affine registration firstly to match two images roughly. The affine transformation is T = t x , t y , t z , rx , ry , rz , s x , s y , s z , γ xy , γ yz , γ zx , where t x , t y , t z

{

}

are translations along the x, y ,and z axes respectively, rx , ry , rz are rotations about the x, y ,and z axes respectively, s x , s y , s z are scales about the x, y ,and z axes respectively, and γ xy , γ yz , γ zx are shears about x − y, y − z and z − x plane respectively. The initial translations are determined by the centers of mass of the two segmented livers, the initial scales are set to one, and the initial rotations and shears are set to zero. The Powell optimization [15] is used to obtain these optimal parameters. 2.3 BSpline-Based Nonrigid Registration

BSpline-based free form deformation (FFD), which is firstly proposed [16] to process breast images, is used to deform the CT image. The shape of image space can be controlled by changing the control grids of the BSpline, and the transform is Cn-1 smooth continuous, where n is the order of BSpline basis function. Usually the C2 continue is enough, therefore, we select three order of BSpline basis function to deform the image space as followed.

θ 0 (s ) = (1 − s )3 / 6

( θ ( s ) = (− 3s

)

θ1 ( s ) = 3s 3 − 6s 2 + 4 / 6 3

2

)

+ 3s 2 + 3s + 1 / 6

(1)

θ3 (s) = s 3 / 6 The deformation field defined by FFD can be represented as:

r u ( x, y , z ) =

3

3

3

∑∑∑θ (u)θ l

m (v )θ n ( w) Pi +l , j + m ,k + n

(2)

l =0 m =0 n =0

Here P is the control grid. Usually the cost function includes two parts, one is similarity metrics, which characterizes the similarity of two images, and the other is deformation, which is associated with the particular deformations [17].

C = −Csimilarity + Cdeformation

(3)

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But computing cost increases greatly when the deformation part is added. Considering the C2 continuous of BSpline, when the two images aren’t different greatly, we can delete the deformation part and still get good results. Actually, after affine transform, the two images are very similar. Therefore we only use similarity part in the cost function so as to save much time.

3 Experiment Results 3.1 Data Acquisition

CT images were acquired with Somatom Sensation Cardiac/16 (Siemens). The size of CT images is 512 × 512 × 25 , slice thickness is 7.0 mm, and the in-plane dimensions are 0.58mm × 0.58mm . CT images were acquired 3 days before the microwave ablation. MR images were acquired by a 0.5 T open configuration MR system, Signa SP/I (GE Healthcare). The size is 256 × 256 × 28 , slice thickness is 5.0 mm, and the inplane dimensions are 1.172mm × 1.172mm . In this case, laparoscopic guidance was combined with MR image guidance. The abdominal cavity was inflated with CO2 gas for the laparoscopy. MR images were acquired after the inflation during surgical procedure. 3.2 Liver Segmentation

An example of manual segmentation of CT is shown in figure 1(a). It is segmented by experts carefully. The deformable surface model segments the Open MRI very quickly, it’s about 1 second. An example is shown in figure 1 (b). Then manual segmentation is used to trim the result further. The segmentation result is shown in figure 1 (c). It’s need about 10-15 minutes.

(a)

(b)

(c)

Fig. 1. (a) An example of manual segmentation of CTA (b) deformable surface model segmentation of Open MRI (c) manual segmentation of the result of (b)

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3.3 Results of Registration

After the livers are segmented, the images sizes can be deduced greatly. We reduce the size of CT image to 340 × 340 × 25 , and the size of MR image to 180 × 180 × 28 . An example is shown in figure 2 and 3. Fig.2 (a) is a slice of CT image and Fig.2 (b) a slice of Open MR image before registration. Fig.2 (c)-(e) is the results of CT images after rigid, affine and BSpline registration respectively, and Fig.2 (f)-(h) is the tumor extracted from registered CT images overlap on the MR image respectively.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 2. (a) a slice of CTA (b) a slice of Open MRI (c)-(e) results of rigid registration, affine registration , and BSpline registration (f)-(h) tumor extracted from CTA after rigid registration, affine registration , and BSpline registration and overlap on the Open MRI

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The contour of CT images after rigid, affine registration and BSpline-based nonrigid registration are overlapped on the Open MRI, and an example is shown in figure 3. The computing costs are listed in table 1.

(a)

(b)

(c)

Fig. 3. The contour of CTA overlapped on the Open MRI (a) rigid registration (b) affine registration (c) BSpline-based nonrigid registration Table 1. Computing cost

Proposed method (second) Segment liver from Open MRI Affine registration Nonrigid registration total

Andres’

900 500 200 1600

990

3.4 Algorithm Evaluation

We evaluate our method from visual inspection, distance of liver surfaces, and computing cost, and compare the results of the proposed method with those of Andres’. Andre’s method is rigid registration, only three translations and three rotations are used to match images. An example is shown as follow. Visual inspection. Figure 2 (c), (d), and (e) are the results of CT images after rigid, affine and BSpline registration. We can find the tumor is just beneath the surface of the liver. Figure 2 (f)-(h) show the tumor overlapped on the MR images during surgery. The tumor is extracted from the results of rigid, affine and BSpline registration respectively. It is easy to find that the position of the tumor is far away from the liver surface after rigid registration, a part of the tumor is outside theliver after affine registration, and the tumor position is just beneath the liver surface after BSpline registration. Therefore after BSpline registration, we can get satisfied result. In figure 3, from positions pointed by arrow A and B, we can easily find the liver surface of CT images after BSpline registration approaches the liver surface of MR images best, that of affine registration is better than that of rigid registration.

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From the visual inspection, the accuracy of proposed method is much better than Andres’. Distance of liver surface. We use the method mentioned in reference [9] to estimate the distance of two liver surfaces. Detailed can be found in the reference. The 3D error of the proposed method is about 1.5 mm, while Andre’s about 3mm. Computing Cost. The cost is listed in table 1. Since CT images are acquired before surgery, there is enough time to segment liver from it. We don’t include this time in the proposed method. We only consider the time in the surgery. Using deformable model and manually segmentation, it costs about 900 seconds. Affine registration needs about 500 seconds, and BSpline-based nonrigid registration about 200 seconds. The total is about 1600 second that is about 27 minutes, while Andres’s only needs about 900 seconds.

4 Discussion and Conclusion This case was extraordinary one, because MR image and laparoscopy were combined for the image navigation. The inflation of the abdominal cavity with CO2 gas caused bigger deformation of the liver than usual cases. Even in such a special case, the proposed method can be successfully applied to CT and MR image registration. Although the computing time is more than Andres’, 27 minutes can be accepted. The high accuracy of the proposed method can be more effective to remove the tumor of patients. Actually, the most time is used for segmentation of Open MR in the proposed method. If the automatically segmentation algorithm is more effective, the time will reduce greatly. In the future, we’ll research better liver segmentation method to reduce the computing cost needed in the proposed method.

Acknowledgement This work was supported partly by the Strategic Information and Communications R&D Promotion Programme (SCOPE) under the Grand No. 051307017 and the National Key Basic Research and Development Program (973) Grant No. 2003CB716105.

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