Visualization of Risk Structures for Interactive Planning of Image Guided Radiofrequency Ablation of Liver Tumors Christian Rieder, Michael Schwier, Andreas Weihusen, Stephan Zidowitz and Heinz-Otto Peitgen Fraunhofer MEVIS, Institute for Medical Image Computing, Universit¨atsallee 29, D-28359 Bremen, Germany ABSTRACT Image guided radiofrequency ablation (RFA) is becoming a standard procedure as a minimally invasive method for tumor treatment in the clinical routine. The visualization of pathological tissue and potential risk structures like vessels or important organs gives essential support in image guided pre-interventional RFA planning. In this work our aim is to present novel visualization techniques for interactive RFA planning to support the physician with spatial information of pathological structures as well as the finding of trajectories without harming vitally important tissue. Furthermore, we illustrate three-dimensional applicator models of different manufactures combined with corresponding ablation areas in homogenous tissue, as specified by the manufacturers, to enhance the estimated amount of cell destruction caused by ablation. The visualization techniques are embedded in a workflow oriented application, designed for the use in the clinical routine. To allow a high-quality volume rendering we integrated a visualization method using the fuzzy c-means algorithm. This method automatically defines a transfer function for volume visualization of vessels without the need of a segmentation mask. However, insufficient visualization results of the displayed vessels caused by low data quality can be improved using local vessel segmentation in the vicinity of the lesion. We also provide an interactive segmentation technique of liver tumors for the volumetric measurement and for the visualization of pathological tissue combined with anatomical structures. In order to support coagulation estimation with respect to the heat-sink effect of the cooling blood flow which decreases thermal ablation, a numerical simulation of the heat distribution is provided. Keywords: Image-Guided Therapy, Visualization, Therapy Planning, Radiofrequency-Ablation
1. DESCRIPTION OF PURPOSE In the past few years, image-guided ablation therapies using thermal energy has been developed as a minimal invasive alternative for the treatment of liver tumors.1 Particularly, the radiofrequency ablation (RFA) has taken a significant part in the clinical routine because of its common technical procedure, low complication rate and low cost. The principles of RFA are that electrodes are placed percutaneously at the center of the tumor. A high-frequency electric field is induced into the tumor which causes a local resistive heating of the tissue by ionic agitation. This leads to a coagulative necrosis as a result of irreversible protein denaturation of the cells. To treat a large target zone, repositioning of a single applicator or inducing multiple applicators are often used procedures. Treatment is only of value if complete destruction of tumor cells is guaranteed. As available methods for online monitoring of the thermal destruction are insufficient, the success of a tumor treatment depends considerably on the pre-interventional planning of the RFA where choice of adequate applicator trajectory is important to prevent harming healthy structures as vessels, ribbons and lungs.2 Also choice of appropriate applicator type and exact positioning of electrodes is essential to achieve complete destruction of cancer cells with respect to heat-sink effect of vessels located in the immediate vicinity.3 Further author information: (Send correspondence to) Christian Rieder E-mail:
[email protected] Telephone: +49 421 218 8194
In our previous work4 we described a workflow oriented software platform for image guided RFA oriented to radiological interventions which are mainly planned using slicing in 2D viewers. In this work we developed advanced 3D visualization methods to additionally support physicians such as surgeons and gastroenterologists with a spatial view of the scene including all relevant anatomical and computational results for RFA planning. In comparison to medical applications where RFA planning depends on huge amount of accurate segmentation procedures,5 our goal is to visualize risk structures in a way that physicians are able to perform fast RFA planning in the clinical routine, e.g. without the need of time-consuming data manipulation.
2. METHODS In this section we give a description of the algorithms and visualization methods we integrated in our RFA planning application. These techniques were designed under consideration of the time and usability constraints of the clinical routine i.e. robustness, intuitive interaction and little time effort.
2.1 Tumor and Vessel Segmentation To support the physician with knowledge of local anatomy such as tumors and vessels, we integrated two segmentation algorithms. The tumor segmentation is a morphology based region growing algorithm described by Moltz et al.6 This algorithm requires only a stroke across the lesion from the user and has proven to achieve robust segmentation results with little time effort. The vessel segmentation is an automatic algorithm which works in a local region of interest around an coagulation zone and starts by mouse-clicking into one vessel.7
2.2 Electrode Placement and Affected Area Visualization in 2D The radiofrequency applicators of different manufacturers are visualized by corresponding virtual models, which can be placed and moved within the scene in 2D as well as in 3D. The location of an applicator model is determined by a pair of spatial coordinates which define the position of the applicator electrode and the position of the applicator handle. Those two spatial coordinates can separately or collective be positioned by the user to move the applicator within the scene. Also, the RF applicator can intuitively be moved in shaft direction to simulate back-tracking ablation known from clinical routine. Supplementary to the familiar 2D representation with its axial, sagittal and coronal views, we integrated two orthogonal multi-planar reformatted (MPR) views to allow fast exploration of tissue in the vicinity of the applicator electrode. The first MPR view is oriented along the applicator axis and can be rotated 360 degrees around the applicator. The second MPR view shows the scene plane normal to the applicator along shaft direction and can be sliced from shaft to target. The advantage of the MPR views is that risk structures in the vicinity of the electrode can intuitively explored even if the applicator is not aligned to the main slicing axes. The affected area of an applicator is the region of complete cell destruction in homogeneous tissue announced by the manufacturer. This area should be large enough to enclose the tumor as well as a safety margin of 1 cm at all sides. On the other hand, the coagulation zone should be small enough to avoid ablation of risk structures.8 Thus, we create a three-dimensional model from the electrode parameters,9 which is an ellipsoid geometry, to allow a visual estimation whether the coagulation zone of the RF applicator is large enough for complete tumor ablation. However, one should be aware of the heat sink effect caused by the cooling blood flow in vessels.
2.3 Three-dimensional Volume Rendering Besides the 2D representations we described in Section 2.2, an additional three-dimensional volume rendering can be chosen for a spatial view of the scene10 and all computational results which helps physicians like surgeons or gastroenterologists planning RFA more intuitive than by slicing in 2D.
2.3.1 Anatomical Volume Rendering The anatomy is visualized as three-dimensional direct volume rendering (DVR) using the possibilities of modern graphics hardware.11 The goal of the anatomical visualization is to allow a fast recognition of potential risk structures like important vasculature of the liver, which has to be preserved from harming, as well as important landmarks for optimal placement of RF applicators. Because of the limited available planning time in the clinical routine, often few minutes before intervention, accurate but time consuming segmentation procedures can not be taken into account. Nevertheless, the difficulty in displaying anatomical structures such as vessels or lungs by volume rendering is to set up an appropriate transfer function using simple interaction techniques like windowing, known from medical workstations. To overcome this issue and to allow a fast exploration of the data set, we automatically calculate an appropriate transfer function. Due to its robustness to noise and varying intensity distributions, we utilize the fuzzy c-means (FCM) clustering to determine adequate grey value thresholds to set up a transfer function for the volume rendering that emphasizes liver vessel structures.12 To save computation time we apply the clustering in a local region of interest (ROI) around the selected tumor. The input image for our method is the ROI of a contrast enhanced venous phase CT image of the liver. The algorithm groups the voxels into four clusters. The clusters define the appropriate gray value thresholds to set up the transfer function we use for the volume rendering of the complete data set. Thus, our method allows to explore liver vasculature without the need of segmented vessels in a visual quality which is close to state-of-the-art visualization techniques for segmented vasculature as convolution surfaces.13 Compared with traditional adjustment of rendering values (see Figure 1 a) based on manual windowing, the visual quality of our automatic method is as good as quality of the manual technique, often on top of it – particularly with regard to the visual separation of different structures – without the need of substantial interaction (see Figure 1 b).
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Figure 1. In (a) the contrast of the volume rendering is manually set by the user; (b) shows the rendering with automatically determined transfer function. Pulmonary structures and other soft tissue is visible, too.
2.3.2 Smooth Volume Rendering of Segmentation Results In the two-dimensional visualization, we blend labeled segmentation results within the anatomical context. We also integrated the tumor mask into the three-dimensional volume rendering14 to allow exploration of the spatial relations around lesions. For high-quality visualization results, each tumor mask is smoothed using a diffusion filter. To enhance the spatial perception of tumors, volume shading is enabled. For that, gradients, which are needed for volume shading computation, have to be computed using a Sobel filter as a pre-processing step. Figure 2 shows the visualization of un-smoothed, un-shaded and smoothed-shaded segmentation mask.
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Figure 2. In (a) the tumor is rendered as a simple mask. In (b) the tumor is visualized using volume shading. Image (c) shows the shaded volume rendering of the smoothed segmentation mask.
2.3.3 Volume Visualization of Affected Areas To visually estimate the amount of cell destruction caused by ablation, we integrated the rendering of affected areas, specified as ellipsoids by the manufacturers, in our volume visualization method. We change the coloring of lesions and healthy risk structures such as vessels, located in the affected area in order to allow the recognition of the area of destruction. Also, silhouettes are drawn at the areas surface to enhance the boundary of tissue located inside and outside of the affected area (see Figure 3).
Figure 3. An RF applicator is positioned into a tumor. The color of the tumor is yellow if it is located inside the affected area and blue outside. Silhouettes are drawn at the boundary to emphasize tumor regions which will not be burned by ablation.
Technically we compute in the volume rendering shader for every voxel if the voxel position x is inside or outside of the ellipsoid (see Figure 4). The result of following equation is negative if x is inside the ellipsoid and positive outside: r − rmax 2 x) = |x x − x 0 + min x − x 0 ))|2 − rmin vˆ(ˆ v · (x f (x rmax where x 0 denotes the middle point, rmin is the minimal radius of the ellipsoid and rmax is the maximal radius along orientation v .
In clinical routine, multiple applicator placement is becoming a standard procedure for ablation of large lesions. To support multiple affected areas we compute the union of all visible affected areas, which means that every voxel has to be tested if its location is inside the ellipsoids. For that purpose we developed a dynamic shader framework for direct volume rendering to enable manipulation of shader code during application runtime. Consequently, applicators as well was affected areas can be interactively created, removed or manipulated. The individual applicator parameters are immediately forwarded into the shader and the affected area ellipsoids are computed. rmin RF applicator
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Figure 4. An illustrative description of an RF applicator and corresponding affected area.
2.4 Numerical Simulation of Affected Areas Due to the patients individual anatomy where tumors can be located in the vicinity of vessels, the cooling of the blood flow should be considered in RFA planning.15 As a consequence, an ellipsoid coagulation zone of constant diameter can not be expected.16 Since the manufacturers description of the affected area does not incorporate heat sink effects, we integrated a numerical simulation. The computation of the heat distribution is based on FEM17 and incorporates the characteristics of the chosen RF applicator type as well as the neighboring vascular structure. We visualize the resulting heat distribution in 2D using a color coded coagulation temperature map (see Figure 5 a). Tissue, which is located in the zone of immediate cell destroyed is colored red. Coagulation zones of incomplete destruction are colored orange and yellow. In 3D, we visualize the computed coagulation mask using geometric surfaces. Those surfaces (see Figure 5 b) are integrated into the volume rendering. Thus, the physician is able to interpret the simulation result which could lead to repositioning of RF applicators because of heat sink effects, which influence the coagulation zone.
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Figure 5. In (a) the simulation result is visualized using a color coded coagulation mask; image (b) shows the corresponding three-dimensional rendering with the computed coagulation mask using WEM surfaces.
2.5 Object Navigation To allow intuitive handling of multiple objects as applicators, affected areas, annotations, measurements, lesions and segmented vessels we integrated a data navigation tool in our application. For clear representation, objects
can be selected, hidden and deleted using the navigation tool. The 2D viewers show the corresponding slice of the selected object to allow the physician a fast exploration of the objects location. By this, the physician is able to optimize the placement of multiple applicators in a intuitive manner. Figure 9 shows the GUI of the application. A single Lesion is selected in the Object Navigator and displayed orange in the 2D viewers. The 3D rendering shows the applicator with affected area around the green tumor.
3. RESULTS Clinical partners use the application for scientific projects and clinical evaluations. They regard the methods as helpful and intuitive for RFA planning. The volume rendering method with automatic determined transfer function enables a 3D visualization of multiple anatomical structures like bones, vessels and lungs without the need of expansive segmentation tasks and thus enables an immediate spatial view of the planning situation. Particularly, pulmonary structures can be distinguished which is important to prevent harming these structures during electrode placement. To assess the value of our anatomical volume rendering technique, we compared the visual quality of several volume rendered vessel systems with corresponding convolution surface visualizations of manually segmented vasculature. We define a good visual quality as a visualization of all vessels greater than two millimeter diameter. To our knowledge, vessels smaller than two millimeter are no substantial risk structures for radiofrequency ablation. The manual segmentation, which is part of an extensive vasculature analysis for oncological surgery planning, was done by medical technical assistants with high amount of expertise and is thus our ground-truth. The time effort for the manual segmentation process ranges from 10 minutes for image data with good quality to 40 minutes for image data with bad quality or strong anatomical deformations. Depending on the CT image quality as noise, contrast and resolution, with our method it is possible to achieve a visual quality which is close to the convolution surfaces visualization for segmented vasculature (compare Figure 6). In summary, this comparison shows that the rate of accuracy depends on the image data quality whereas bad image quality causes higher segmentation process time. Nonetheless, vessels with minor visual quality supports the physician with important anatomical information for radiofrequency planning if accurate segmentation masks of vessels are not available.
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Figure 6. Image (a) shows the combined visualization of bone structures using volume rendering and of vessels using convolution surfaces. In (b) the same data set is visualized with our automatic rendering technique.
The visual quality of our automatic transfer function calculation depends mainly on two parameters: the contrast between vessel structures and liver parenchyma as well as the noise in the CT image. On the one hand,
if the contrast is very high, a good visualization is achievable with high noise in the image. At the other hand, if the noise in the image is negligible low a good visual quality is also possible with low contrast. The explanation of that effect is that a small intensity difference between liver parenchyma and vessel structures with respect to the image noise level results in intensity clustering of voxels without reasonable object classification. If the contrast between liver and vessels is high, i.e. substantial difference of the mean intensities, a good visual quality is possible in defiance of strong noise, i.e. a huge standard deviation. Figure 7 shows the mean and standard deviation values for liver and vessels taken in a ROI image (see Figure 8 a) where gaussian noise was added. The vertical axes shows the Hounsfield Units (HU), the horizontal axes the σ values of the gaussian noise (with mean value = 1). At a gaussian noise level of σ = 17 the standard deviation of liver parenchyma and vessel structures begin to overlap and at a noise level of σ = 44 the standard deviations cross the mean values. Whereas the visualization of the ROI at σ = 17 is minor different (see Figure 8 b), the visual quality at σ = 44 is significantly lower because visualization artifacts appear which are mainly undesired liver parenchyma instead of vessels (see Figure 8 c). 275 HU
std. deviation 192 HU
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Figure 7. The mean and standard deviation of liver parenchyma as well as vessel structures subject to the standard deviation σ of the added gaussian noise. At a noise level of σ = 17 the standard deviation of liver parenchyma and vessel structures begin to overlap (see Figure 8 b) and at σ = 44 the standard deviations cross the mean values (see Figure 8 c).
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Figure 8. Image (a) shows a ROI with automatic determined transfer function. In (b) a gaussian noise (mean = 1, σ = 17) was applied onto the image, the image quality begins to decrease. Image (c) shows the resulting rendering with gaussian noise of mean = 1 and σ = 44 where the calculated transfer function overlaps vessel structures as well as liver parenchyma.
Additionally we performed user experiments for a feasibility study to evaluate how our visualization methods
could influence and improve the planning of RF ablations. Using manual windowing, medical experts had to justify the volume rendering to achieve a clear view to vascular structures as well as bones. We measured the required time effort, calculated the mean value and compared it with the mean computation time of our automatic method. On the one hand, we could prove our assumption that manual windowing of the transfer function with similar visualization results is more difficult and time consuming. On the other hand, in 22 of 31 test cases, we could not observe significant differences to our automatic transfer function determination focused on vascular structures. In 9 cases, the users were able to visualize smaller vessels. Because of that, we also added manual windowing to adjust the calculated transfer function afterwards, if needed. Furthermore, most of the respondents judged that the proposed technique for the affected area visualization provide an immediate and spatial recognition of untreated tumor tissue. Compared with the 2D visualization, needed repositioning of the electrodes was earlier recognized. According to the 3D visualization, a required adaption of the access path as well as the rapid detection of risk structures along the trajectory is intuitive to achieve.
Figure 9. This screen shot shows the GUI of our RFA planning software with enabled volume rendering. The RF applicator is positioned close to the selected lesion but the affected area surrounds not completely the lesion.
4. CONCLUSIONS In this work we described visualization methods for a workflow optimized application, which assists physicians in pre-interventional RFA planning. We integrated segmentation algorithms to support knowledge of local anatomy. To support electrode placement we developed different RF applicator models with corresponding affected areas. Among the 2D viewers, the three-dimensional volume rendering provides an intuitive placement of RF applicators to ensure complete destruction of tumor cells as well as preserving risk structures considering the patients anatomy. Future work will focus on the transfer of RFA planning data into the intervention and the support of ultrasonic navigated devices. Also we make plans of an intervention monitoring application.
ACKNOWLEDGMENTS This work was funded by the Federal Ministry of Education and Research (SOMIT-FUSION project FKZ 01IBE03C). We would like to thank the people at Fraunhofer MEVIS as well as our clinical partners for their contribution to our work.
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