Difference between revisions of "AngioLab"

From user's Wiki!
Jump to: navigation, search
(Created page with 'The AngioLab suite for GIMIAS allows to perform an entire workflow from medical images to morphological characterization of the aneurysm sac and parent vessels, virtual treatment…')
 
Line 17: Line 17:
 
The display module allows three-dimensional reconstruction of the high quality DICOM images interactively. In this manner allows display of images by multiplanar reformation, in view orthogonal cuts, cuts multiple views and volume rendering. The viewing window allows adjustment of the level of contrast and brightness, the display of images with all demographics, zoom and pan, rotation, selection of areas of interest (Regions of Interest - ROI). Volume rendering can be performed using standard techniques Volume Raycast 3D Texture Mapping or volumetric image, together with a transfer function optimized for angiography images. The accuracy and speed of volume rendering can be adjusted enabling or disabling acceleration Graphical Processing Unit (GPU) and the level of detail (Level of Details - LOD). Volume rendering includes the option of displaying the volumetric image with a 2D Texture Mapping technique which simulates acquisition technique of X-ray, so as to obtain images similar to those obtained by fluoroscopy (see Figure 2).
 
The display module allows three-dimensional reconstruction of the high quality DICOM images interactively. In this manner allows display of images by multiplanar reformation, in view orthogonal cuts, cuts multiple views and volume rendering. The viewing window allows adjustment of the level of contrast and brightness, the display of images with all demographics, zoom and pan, rotation, selection of areas of interest (Regions of Interest - ROI). Volume rendering can be performed using standard techniques Volume Raycast 3D Texture Mapping or volumetric image, together with a transfer function optimized for angiography images. The accuracy and speed of volume rendering can be adjusted enabling or disabling acceleration Graphical Processing Unit (GPU) and the level of detail (Level of Details - LOD). Volume rendering includes the option of displaying the volumetric image with a 2D Texture Mapping technique which simulates acquisition technique of X-ray, so as to obtain images similar to those obtained by fluoroscopy (see Figure 2).
  
[[File:XRayRenderingAngio.png | Figure 2. Volume rendering angiography imaging techniques using 3D Texture Mapping and GPU-acceleration (left) and 2D Texture Mapping technique which simulates the X-ray acquisition (right) ]]
+
[[File:XRayRenderingAngio.png | thumb | none| 500px | Figure 2. Volume rendering angiography imaging techniques using 3D Texture Mapping and GPU-acceleration (left) and 2D Texture Mapping technique which simulates the X-ray acquisition (right) ]]
  
 
= Image segmentation =
 
= Image segmentation =
Line 25: Line 25:
 
The technique used in AngioLab consists of an algorithm based on geodesic active regions (GAR) [1] and is optimized for segmentation of vascular tissue through the standardization of image intensity [2]. The GAR is a geometric deformable model that is governed by a power function where the internal force is defined by the local curvature of the surface to extract and combine external regional and gradient descriptors that drive the evolution of the model to the vascular limit. Regional descriptors are derived from a training set, which is a collection of images which have already performed a manual segmentation and has well defined depth between what is background and what is blood vessel.
 
The technique used in AngioLab consists of an algorithm based on geodesic active regions (GAR) [1] and is optimized for segmentation of vascular tissue through the standardization of image intensity [2]. The GAR is a geometric deformable model that is governed by a power function where the internal force is defined by the local curvature of the surface to extract and combine external regional and gradient descriptors that drive the evolution of the model to the vascular limit. Regional descriptors are derived from a training set, which is a collection of images which have already performed a manual segmentation and has well defined depth between what is background and what is blood vessel.
  
[[File:GARSegmentationAngio.png | Figure 3. Comparison between manual measurements (left column) and measurements obtained from the segmentation GAR (right column) of a blood vessel (top row) and aneurysm (bottom row). Figure adapted from [2]. ]]
+
[[File:GARSegmentationAngio.png | thumb | none| 500px |  Figure 3. Comparison between manual measurements (left column) and measurements obtained from the segmentation GAR (right column) of a blood vessel (top row) and aneurysm (bottom row). Figure adapted from [2]. ]]
  
 
This technique uses different training sets, which enable segmentation of blood vessels from medical images from different modalities: 3DRA, CTA, MRA. Finally, the segmentation surface consists of a mesh composed of triangular elements (see Figure 4).
 
This technique uses different training sets, which enable segmentation of blood vessels from medical images from different modalities: 3DRA, CTA, MRA. Finally, the segmentation surface consists of a mesh composed of triangular elements (see Figure 4).
  
[[File:GARSegmentationMeshAngio.png | Figure 4. Segmentation obtained through AngioLab RAG. ]]
+
[[File:GARSegmentationMeshAngio.png | thumb | none| 500px |  Figure 4. Segmentation obtained through AngioLab RAG. ]]
  
 
There are very few occasions when the GAR algorithm fails, but for these occasions an auxiliar segmentation algorithm has been added. This method is called Otsu and is used to perform a segmentation dependent histogram of gray levels in an image [3]. The technique must be assumed that only two classes of voxels (the blood vessel and background) and calculates the optimum threshold separating those two classes. Generally, the segmentation from Otsu method is very crude and contains many flaws, but it is suitable only where the failure to segment GAR
 
There are very few occasions when the GAR algorithm fails, but for these occasions an auxiliar segmentation algorithm has been added. This method is called Otsu and is used to perform a segmentation dependent histogram of gray levels in an image [3]. The technique must be assumed that only two classes of voxels (the blood vessel and background) and calculates the optimum threshold separating those two classes. Generally, the segmentation from Otsu method is very crude and contains many flaws, but it is suitable only where the failure to segment GAR
Line 37: Line 37:
 
Depending on the quality of the image and of the morphology of the patient's vessels, the mesh obtained with the automatic segmentation (GAR) may need some manual corrections. These circumstances appear, for example when there are arteries that are fused with the aneurysm or when the contrast has failed to completely fill the artery which one wishes to study, to name a few (see Figure 5).
 
Depending on the quality of the image and of the morphology of the patient's vessels, the mesh obtained with the automatic segmentation (GAR) may need some manual corrections. These circumstances appear, for example when there are arteries that are fused with the aneurysm or when the contrast has failed to completely fill the artery which one wishes to study, to name a few (see Figure 5).
  
[[File:MeshEditingAngio.png | Figure 5. Common problems on a segmentation: Melting arterial aneurysm (left), incomplete filling of contrast within the aneurysm (red arrow), and fusion artery-artery (right).]]
+
[[File:MeshEditingAngio.png | thumb | none| 500px | Figure 5. Common problems on a segmentation: Melting arterial aneurysm (left), incomplete filling of contrast within the aneurysm (red arrow), and fusion artery-artery (right).]]
  
 
Corrections AngioLab available are:
 
Corrections AngioLab available are:
Line 44: Line 44:
 
** Holes filling: lets close all the holes of a surface or choose only a few to be closed.
 
** Holes filling: lets close all the holes of a surface or choose only a few to be closed.
  
[[File:MeshEditingGlobalAngio.png | Figure 6. Tools global mesh editing: cut vessels (left) and repair of holes (right). ]]
+
[[File:MeshEditingGlobalAngio.png | thumb | none| 500px | Figure 6. Tools global mesh editing: cut vessels (left) and repair of holes (right). ]]
  
 
* Local Tools (see Figure 7):
 
* Local Tools (see Figure 7):
 
** Local surface editing: select elements of a triangular surface mesh using different selection techniques (brush, unique triangle selection and sphere) for local editing operations (delete triangles, refine and smooth mesh).
 
** Local surface editing: select elements of a triangular surface mesh using different selection techniques (brush, unique triangle selection and sphere) for local editing operations (delete triangles, refine and smooth mesh).
  
[[File:MeshEditingLocalAngio.png | Figure 7. Local Tools mesh editing: selection field (left) and brushed (right). ]]
+
[[File:MeshEditingLocalAngio.png | thumb | none| 500px | Figure 7. Local Tools mesh editing: selection field (left) and brushed (right). ]]
  
 
After making the necessary corrections and edits, you get a proper surface mesh vascular model for the following steps: morphological analysis, building centerlines and virtual treatments.
 
After making the necessary corrections and edits, you get a proper surface mesh vascular model for the following steps: morphological analysis, building centerlines and virtual treatments.
Line 59: Line 59:
 
This tool uses specific algorithms to blood vessels which are based on the creation of central lines from a Voronoi diagram defined for vascular model surface mesh in question [4]. The Voronoi diagram is used to define center points from the maximum inscribed spheres along a geometry, in this case, a blood vessel (see Figure 8). It is said that an inscribed sphere is maximum when it does not let other inscribed spheres to contain it. From these key central points, the shortest paths are defined between source points and target points. These paths are the centerlines.
 
This tool uses specific algorithms to blood vessels which are based on the creation of central lines from a Voronoi diagram defined for vascular model surface mesh in question [4]. The Voronoi diagram is used to define center points from the maximum inscribed spheres along a geometry, in this case, a blood vessel (see Figure 8). It is said that an inscribed sphere is maximum when it does not let other inscribed spheres to contain it. From these key central points, the shortest paths are defined between source points and target points. These paths are the centerlines.
  
[[File:CenterlineAngio.png | Figure 8. Vascular model of carotid artery (left), the Voronoi diagram (middle) and center lines obtained from the Voronoi diagram (right). Figure obtained from [4].]]
+
[[File:CenterlineAngio.png | thumb | none| 500px |  Figure 8. Vascular model of carotid artery (left), the Voronoi diagram (middle) and center lines obtained from the Voronoi diagram (right). Figure obtained from [4].]]
  
 
The algorithms used are part of VMTK AngioLab (Vascular Modeling Toolkit) [5]. This tool is especially necessary for virtual treatment with stent.
 
The algorithms used are part of VMTK AngioLab (Vascular Modeling Toolkit) [5]. This tool is especially necessary for virtual treatment with stent.
Line 69: Line 69:
 
From this simplified geometry, different measures are obtained as the aspect ratio, non-sphericity index, volume of the aneurysm, the aneurysm surface, surface of the aneurysm neck, the height and width of the aneurysm neck (see Figure 9). The aspect ratio is a measure that relates the height to the width of the aneurysm neck, while the non-sphericity factor relates the volume of the aneurysm with its area.
 
From this simplified geometry, different measures are obtained as the aspect ratio, non-sphericity index, volume of the aneurysm, the aneurysm surface, surface of the aneurysm neck, the height and width of the aneurysm neck (see Figure 9). The aspect ratio is a measure that relates the height to the width of the aneurysm neck, while the non-sphericity factor relates the volume of the aneurysm with its area.
  
[[File:DescriptorsAngio.png | Figure 9. Morphological descriptors obtained AngioLab aneurysm. ]]
+
[[File:DescriptorsAngio.png | thumb | none| 500px |  Figure 9. Morphological descriptors obtained AngioLab aneurysm. ]]
  
 
In addition to these measures, the computation of the descriptors called dimensional invariant Zernike moments [6] is also performed. These descriptors are used to characterize the shape of the aneurysm and its nearby vessels, without depending on the orientation or scale geometry. This means that to some level of detail, you can get to rebuild the geometry from now.
 
In addition to these measures, the computation of the descriptors called dimensional invariant Zernike moments [6] is also performed. These descriptors are used to characterize the shape of the aneurysm and its nearby vessels, without depending on the orientation or scale geometry. This means that to some level of detail, you can get to rebuild the geometry from now.
Line 75: Line 75:
 
The calculation of the Zernike moments is used to provide a comparison between the new patient aneurysm with another aneurysm which has been discussed previously, their geometric descriptors which are stored in a database (see Figure 10) . Being able to list similar aneurysms, offered further support the clinician in making therapeutic decisions.
 
The calculation of the Zernike moments is used to provide a comparison between the new patient aneurysm with another aneurysm which has been discussed previously, their geometric descriptors which are stored in a database (see Figure 10) . Being able to list similar aneurysms, offered further support the clinician in making therapeutic decisions.
  
[[File:SimilarAneurysmAngio.png | Figure 10. An aneurysm being compared with ten other similar morphology using the calculation of the Zernike moments. ]]
+
[[File:SimilarAneurysmAngio.png | thumb | none| 500px |  Figure 10. An aneurysm being compared with ten other similar morphology using the calculation of the Zernike moments. ]]
  
 
All descriptors are calculated automatically in real time and are easily stored in a database.
 
All descriptors are calculated automatically in real time and are easily stored in a database.
Line 84: Line 84:
 
The release of the stent within the arterial geometry is performed by the method of 'fast virtual stenting', developed by Larrabide et al [7]. This technique is based on deformable models, where a partial differential equation of second order is used to deform a mesh under the effect of physical constraints such as the geometry, the length and radius of the stent.
 
The release of the stent within the arterial geometry is performed by the method of 'fast virtual stenting', developed by Larrabide et al [7]. This technique is based on deformable models, where a partial differential equation of second order is used to deform a mesh under the effect of physical constraints such as the geometry, the length and radius of the stent.
  
[[File:VirtualStentingAngio.png | Figure 11. Enterprise release of a stent (left) and a stent SILK (right) within an idealized geometry of a bifurcation aneurysm. Figure adapted from [7]. ]]
+
[[File:VirtualStentingAngio.png | thumb | none| 500px |  Figure 11. Enterprise release of a stent (left) and a stent SILK (right) within an idealized geometry of a bifurcation aneurysm. Figure adapted from [7]. ]]
  
 
The technique Stenting Virtual requires as input the 3D model vessel segment and a center line from which the stent is deployed, with the restriction of the geometry patient's vasculature. The user can select a point on the centerline where to place the stent for treating an aneurysm.
 
The technique Stenting Virtual requires as input the 3D model vessel segment and a center line from which the stent is deployed, with the restriction of the geometry patient's vasculature. The user can select a point on the centerline where to place the stent for treating an aneurysm.
Line 102: Line 102:
  
  
[[File:VirtualCoilingAngio.png | Figure 12. Examples of virtual embolization with coils in two aneurysms in real patients, using the technique of Virtual Coiling. Figure adapted from [8].]]
+
[[File:VirtualCoilingAngio.png | thumb | none| 500px |  Figure 12. Examples of virtual embolization with coils in two aneurysms in real patients, using the technique of Virtual Coiling. Figure adapted from [8].]]
  
 
AngioLab allows the user to visualize the coils within the aneurysm, try different numbers of coils within the aneurysm and filling know what percentage corresponds to a certain amount of coils.
 
AngioLab allows the user to visualize the coils within the aneurysm, try different numbers of coils within the aneurysm and filling know what percentage corresponds to a certain amount of coils.
Line 112: Line 112:
 
Upon completion of clinical analysis, the user can review the information that has been added to the report to decide what information is to be eliminated or added. You can also design the final look and print the report in PDF format.
 
Upon completion of clinical analysis, the user can review the information that has been added to the report to decide what information is to be eliminated or added. You can also design the final look and print the report in PDF format.
  
[[File:ClinicalReportTableAngio.png | Figure 13. Example of a table containing a morphological measures three arteries near aneurysm.]]
+
[[File:ClinicalReportTableAngio.png | thumb | none| 500px |  Figure 13. Example of a table containing a morphological measures three arteries near aneurysm.]]
[[File:ClinicalReportImageAngio.png | Figure 14. Example of a screenshot for bifurcation aneurysm untreated (left) and treated with two stents Enterprise and 11 coils. Both vascular geometries are visualized using 2D Texture Mapping.]]
+
[[File:ClinicalReportImageAngio.png | thumb | none| 500px |  Figure 14. Example of a screenshot for bifurcation aneurysm untreated (left) and treated with two stents Enterprise and 11 coils. Both vascular geometries are visualized using 2D Texture Mapping.]]
 +
 
 +
<references />
 +
 
 +
* [1] Hernandez, M. & Frangi, A. F. Non-parametric geodesic active regions: Method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Medical Image Analysis, 2007, 11, 224 – 241.
 +
* [2] Bogunovic, H.; Pozo, J. M.; Villa-Uriol, M. C.; Majoie, C. B. L. M.; van den Berg, R.; van Andel, H. A. F. G.; Macho, J. M.; Blasco, J.; Roman, L. S. & Frangi, A. F. Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF-MRA using geodesic active regions: An evaluation study. Medical Physics, AAPM, 2011, 38, 210-222.
 +
* [3] Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9, 62-66.
 +
* [4] http://www.vmtk.org/Tutorials/Centerlines
 +
* [5] http://www.vmtk.org
 +
* [6] Millan, R.; Dempere-Marco, L.; Pozo, J.; Cebral, J. & Frangi, A. Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants. IEEE Transactions on Medical Imaging, 2007, 26, 1270-1282.
 +
* [7] Larrabide, I.; Kim, M.; Augsburger, L.; Villa-Uriol, M. C.; Rüfenacht, D. & Frangi, A. F. Fast virtual deployment of self-expandable stents: Method and in vitro evaluation for intracranial aneurysmal stenting. Medical Image Analysis, 2010, In Press, Corrected Proof.
 +
* [8] Morales, H. G.; Larrabide, I.; Kim, M.; Villa-Uriol, M.-C.; Macho, J. M.; Blasco, J.; San Roman, L. & Frangi, A. F. Virtual Coiling of Intracranial Aneurysms based on Dynamic Path Planning. To be published in MICCAI 2011.

Revision as of 03:34, 12 September 2012

The AngioLab suite for GIMIAS allows to perform an entire workflow from medical images to morphological characterization of the aneurysm sac and parent vessels, virtual treatment and advanced visualization of fluid dynamic simulation results. This helps the clinician to assess the aneurysm rupture risk and choose the best treatment option.

AngioLab will be used by neurosurgeons and interventional neuroradiologists at the stage of decision making on the type of treatment to be applied to a given patient. AngioLab provides the clinician with additional information provided by the patient's medical images, such as aneurysm morphological characterization and simulation of various types of treatment.

Includes the possibility to simulate and visualize the virtual treatment with minimally invasive techniques, such as stenting and coil embolization. These last two are called Virtual Stenting and Virtual Coiling, respectively. AngioLab also includes the necessary tools to perform virtual treatment of intracranial aneurysms, such as medical image segmentation, mesh editing tools and building centerlines and morphological characterization of intracranial aneurysms.

This suite is divided in two workflows:

  • Morphological Analysis
  • Virtual Device Deployment

DICOM image viewing and PACS connection

This plugin can load medical images in DICOM format acquired from radiological examinations, from machines of different modalities, such as computed tomography angiography (CTA), magnetic resonance angiography (MRA), 3D rotational angiography (3DRA). This module is DICOM-compatible, and thus is prepared to work with integrated health information systems such as PACS.

AngioLab also includes functionality to set parameters and connect to a PACS (Picture Archive Communication System) and run the query operations, retrieve and send, using the communication protocol and the services provided by the DICOM Message Service Element ( DIMSE). Depending on the configuration of the PACS server you want to connect, others may also be required authentication parameters for each of the services you want to use through AngioLab. AngioLab allows you to search on the following fields: Patient Name, Patient ID, Patient Birth Date, Study ID and date of the study.

The display module allows three-dimensional reconstruction of the high quality DICOM images interactively. In this manner allows display of images by multiplanar reformation, in view orthogonal cuts, cuts multiple views and volume rendering. The viewing window allows adjustment of the level of contrast and brightness, the display of images with all demographics, zoom and pan, rotation, selection of areas of interest (Regions of Interest - ROI). Volume rendering can be performed using standard techniques Volume Raycast 3D Texture Mapping or volumetric image, together with a transfer function optimized for angiography images. The accuracy and speed of volume rendering can be adjusted enabling or disabling acceleration Graphical Processing Unit (GPU) and the level of detail (Level of Details - LOD). Volume rendering includes the option of displaying the volumetric image with a 2D Texture Mapping technique which simulates acquisition technique of X-ray, so as to obtain images similar to those obtained by fluoroscopy (see Figure 2).

Figure 2. Volume rendering angiography imaging techniques using 3D Texture Mapping and GPU-acceleration (left) and 2D Texture Mapping technique which simulates the X-ray acquisition (right)

Image segmentation

Segmentation from a medical image is used to extract information about a structure or anatomy of interest to the clinician, in our case, is the 3D representation of the brain blood vessels.

The technique used in AngioLab consists of an algorithm based on geodesic active regions (GAR) [1] and is optimized for segmentation of vascular tissue through the standardization of image intensity [2]. The GAR is a geometric deformable model that is governed by a power function where the internal force is defined by the local curvature of the surface to extract and combine external regional and gradient descriptors that drive the evolution of the model to the vascular limit. Regional descriptors are derived from a training set, which is a collection of images which have already performed a manual segmentation and has well defined depth between what is background and what is blood vessel.

Figure 3. Comparison between manual measurements (left column) and measurements obtained from the segmentation GAR (right column) of a blood vessel (top row) and aneurysm (bottom row). Figure adapted from [2].

This technique uses different training sets, which enable segmentation of blood vessels from medical images from different modalities: 3DRA, CTA, MRA. Finally, the segmentation surface consists of a mesh composed of triangular elements (see Figure 4).

Figure 4. Segmentation obtained through AngioLab RAG.

There are very few occasions when the GAR algorithm fails, but for these occasions an auxiliar segmentation algorithm has been added. This method is called Otsu and is used to perform a segmentation dependent histogram of gray levels in an image [3]. The technique must be assumed that only two classes of voxels (the blood vessel and background) and calculates the optimum threshold separating those two classes. Generally, the segmentation from Otsu method is very crude and contains many flaws, but it is suitable only where the failure to segment GAR

Mesh editing tools

Depending on the quality of the image and of the morphology of the patient's vessels, the mesh obtained with the automatic segmentation (GAR) may need some manual corrections. These circumstances appear, for example when there are arteries that are fused with the aneurysm or when the contrast has failed to completely fill the artery which one wishes to study, to name a few (see Figure 5).

Figure 5. Common problems on a segmentation: Melting arterial aneurysm (left), incomplete filling of contrast within the aneurysm (red arrow), and fusion artery-artery (right).

Corrections AngioLab available are:

  • global tools (see Figure 6):
    • Ring Cut: to cut one or more vessels with a plane from a point selected by the user.
    • Holes filling: lets close all the holes of a surface or choose only a few to be closed.
Figure 6. Tools global mesh editing: cut vessels (left) and repair of holes (right).
  • Local Tools (see Figure 7):
    • Local surface editing: select elements of a triangular surface mesh using different selection techniques (brush, unique triangle selection and sphere) for local editing operations (delete triangles, refine and smooth mesh).
Figure 7. Local Tools mesh editing: selection field (left) and brushed (right).

After making the necessary corrections and edits, you get a proper surface mesh vascular model for the following steps: morphological analysis, building centerlines and virtual treatments.

Tools for building centerlines

This tool allows creation of central lines from the vascular model surface mesh, as obtained from the previous step mesh editing.

This tool uses specific algorithms to blood vessels which are based on the creation of central lines from a Voronoi diagram defined for vascular model surface mesh in question [4]. The Voronoi diagram is used to define center points from the maximum inscribed spheres along a geometry, in this case, a blood vessel (see Figure 8). It is said that an inscribed sphere is maximum when it does not let other inscribed spheres to contain it. From these key central points, the shortest paths are defined between source points and target points. These paths are the centerlines.

Figure 8. Vascular model of carotid artery (left), the Voronoi diagram (middle) and center lines obtained from the Voronoi diagram (right). Figure obtained from [4].

The algorithms used are part of VMTK AngioLab (Vascular Modeling Toolkit) [5]. This tool is especially necessary for virtual treatment with stent.


Morphological Analysis

To perform morphological analysis is required to extract a simplified geometry of the aneurysm and its closest vessels from vascular model obtained during segmentation. Once obtained this, the aneurysmal sac is isolated by a manual selection of the aneurysm neck.

From this simplified geometry, different measures are obtained as the aspect ratio, non-sphericity index, volume of the aneurysm, the aneurysm surface, surface of the aneurysm neck, the height and width of the aneurysm neck (see Figure 9). The aspect ratio is a measure that relates the height to the width of the aneurysm neck, while the non-sphericity factor relates the volume of the aneurysm with its area.

Figure 9. Morphological descriptors obtained AngioLab aneurysm.

In addition to these measures, the computation of the descriptors called dimensional invariant Zernike moments [6] is also performed. These descriptors are used to characterize the shape of the aneurysm and its nearby vessels, without depending on the orientation or scale geometry. This means that to some level of detail, you can get to rebuild the geometry from now.

The calculation of the Zernike moments is used to provide a comparison between the new patient aneurysm with another aneurysm which has been discussed previously, their geometric descriptors which are stored in a database (see Figure 10) . Being able to list similar aneurysms, offered further support the clinician in making therapeutic decisions.

Figure 10. An aneurysm being compared with ten other similar morphology using the calculation of the Zernike moments.

All descriptors are calculated automatically in real time and are easily stored in a database.

Also, you can refine your search depending on the location (near the posterior communicating artery, ophthalmic artery, etc.) And / or type (saccular or fusiform) aneurysm.

Virtual Stenting

The release of the stent within the arterial geometry is performed by the method of 'fast virtual stenting', developed by Larrabide et al [7]. This technique is based on deformable models, where a partial differential equation of second order is used to deform a mesh under the effect of physical constraints such as the geometry, the length and radius of the stent.

Figure 11. Enterprise release of a stent (left) and a stent SILK (right) within an idealized geometry of a bifurcation aneurysm. Figure adapted from [7].

The technique Stenting Virtual requires as input the 3D model vessel segment and a center line from which the stent is deployed, with the restriction of the geometry patient's vasculature. The user can select a point on the centerline where to place the stent for treating an aneurysm.

Once the stent is deployed, the user can view the initial and final position of the stent within the vascular geometry, so that it can redisplay the stent to the desired position. The output of this module is a deployed stent mesh, which can be used for the treatment of visual verification and flow simulations (see Figure 11).

For this tool, the geometries of the following stents are available: Neuroform (Boston Scientific), Enterprise (Cordis), SILK (Balt Extrusion) and PED (ev3).


Virtual Coiling

Virtual Coiling technique is a dynamic route planning to mimic the insertion of coils into a three dimensional model of an aneurysm, giving plausible distribution of coils within a specific patient anatomy [8]. Essentially, the technique involves the following:

  • Insert coils sequentially, while moving the tip of the coil along a path already established, initializing an initial position and direction within the region where the coils are placed.
  • Avoid coil migration out of the aneurysmal area and coils crahses, for proper construction of the path.
  • If the tip of the coil was trapped between other coils, the technique allows the removal of the tip of the coil to redirect their advance.

As a result, the Virtual Coiling is capable of obtaining a representation similar to the coils inserted in clinical practice that may be used for visual verification for treatment and flow simulations (see Figure 12).


Figure 12. Examples of virtual embolization with coils in two aneurysms in real patients, using the technique of Virtual Coiling. Figure adapted from [8].

AngioLab allows the user to visualize the coils within the aneurysm, try different numbers of coils within the aneurysm and filling know what percentage corresponds to a certain amount of coils.


Preparation of the clinical report

AngioLab allows to create a clinical report with all the information that the user considers appropriate. This is possible either before, or after the start of the analysis of the case. The report may include: text, tables (see Figure 13) and screen (see Figure 14).

Upon completion of clinical analysis, the user can review the information that has been added to the report to decide what information is to be eliminated or added. You can also design the final look and print the report in PDF format.

Figure 13. Example of a table containing a morphological measures three arteries near aneurysm.
Figure 14. Example of a screenshot for bifurcation aneurysm untreated (left) and treated with two stents Enterprise and 11 coils. Both vascular geometries are visualized using 2D Texture Mapping.

<references />

  • [1] Hernandez, M. & Frangi, A. F. Non-parametric geodesic active regions: Method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Medical Image Analysis, 2007, 11, 224 – 241.
  • [2] Bogunovic, H.; Pozo, J. M.; Villa-Uriol, M. C.; Majoie, C. B. L. M.; van den Berg, R.; van Andel, H. A. F. G.; Macho, J. M.; Blasco, J.; Roman, L. S. & Frangi, A. F. Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF-MRA using geodesic active regions: An evaluation study. Medical Physics, AAPM, 2011, 38, 210-222.
  • [3] Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9, 62-66.
  • [4] http://www.vmtk.org/Tutorials/Centerlines
  • [5] http://www.vmtk.org
  • [6] Millan, R.; Dempere-Marco, L.; Pozo, J.; Cebral, J. & Frangi, A. Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants. IEEE Transactions on Medical Imaging, 2007, 26, 1270-1282.
  • [7] Larrabide, I.; Kim, M.; Augsburger, L.; Villa-Uriol, M. C.; Rüfenacht, D. & Frangi, A. F. Fast virtual deployment of self-expandable stents: Method and in vitro evaluation for intracranial aneurysmal stenting. Medical Image Analysis, 2010, In Press, Corrected Proof.
  • [8] Morales, H. G.; Larrabide, I.; Kim, M.; Villa-Uriol, M.-C.; Macho, J. M.; Blasco, J.; San Roman, L. & Frangi, A. F. Virtual Coiling of Intracranial Aneurysms based on Dynamic Path Planning. To be published in MICCAI 2011.