A New Automatic Approach for Cerebrovascular 3-D Volume Segmentation with New MIP-Based Technique for Validation

1. A New Automatic Approach for Cerebrovascular 3-D Volume Segmentation with New MIP-Based Technique for Validation M. Sabry M., Charles B. Sites, Aly A. Farag, Stephen Hushek, and Thomas Moriarty

2. Terminology MRI :Magnetic Resonance Imaging MRA :Magnetic Resonance Angiography MRV :Magnetic Resonance Ventriculargrams DSA : Digital Subtraction Angiography MIP : Maximum Intensity Projection VTK : Visualizarion Toolkit

3. Why Cerebrovascular Segmentation? • Accurate description of the vascular tree is very • important to assist in clinical applications, e.g., • Surgical Planning. • Quantitative Diagnosis. • Monitoring Disease Progress.

4. Objectives • Segment Arteries from MRA • Segment Veins from MRV • Validate Results

5. Background • The histogram of MRA volume could be classified into three intensity regions • (LOW) Fluid surrounding brain tissue, bone and the air. • (Medium) Brain tissues including both the grey and white matter. • 3. (High) fat adjacent to arteries & veins [α :255]. α

6. Design • We present a modified version of 3-D computer graphics • Region-Filling algorithm to sweep the vascular tree from • Seed Locations and segment the desired structure. • Our Algorithm Consists of Two Basic Steps: • Image Seed Selection • Definition of Vascular Tree

7. Image Seed Selection • MRA images are acquired from the lower portion of the nasal cavity upward to the top of the skull. • Since Carotid Arteries is the largest artery in brain, The seed image will be selected automatically, since it contains the largest high intensity cross sections of Carotid • All Carotid voxels are marked parents. They will be used to retrieve children voxels later.

8. Vascular Tree Definition • Each voxel in MRA volume is the center of a cube of length 3 voxels. • Each parent is compared with the 26-adjacent neighbor children. • If the intensity level of the child falls in the range [α-255], it is added to the Segmented_Volume . • Parent / Child method eliminated the possibility of having unrelated tissues that get connected to segmented vascular tree.

9. How to Calculate α ? • Let’s defineζas the ratio of the segmented volume to the totalvolume. • Our Research team found that whenζ = [1.5 : 4.5],excellent segmentation results are achieved. • Ifζ is greaterthan this range,α should be Increased. • Ifζ is lowerthan this range,α should be Decreased. • It is found thatα [20 : 40]is a good initial estimate.

10. MRA Validation • MRA validation is extremely difficult because of the vascular tree complexity. • There exists no phantoms that mimics the complexity of the vascular tree especially those vessels whose diameters less than 1mm. • Practitioners usually validate the resulting cerebrovascular volume by rotating the visualized 3-D model by certain angles in order that the views at these angles agree with the available 2-D images taken by DSA, X-ray, or MIP.

11. MIP (Maximum Intensity Projection) • MIP is the state-of-the-art volume rendering technique to produce two-dimensional images of the vascular trees from 3D MRA data at different viewpoints. • MIP Exploits The Fact: • Intensity of Vascular tree are higher than those of the surrounding tissues. • Although the idea behind MIP algorithm seems to be easy especially at angles 0, 90, but the implementation is quite difficult to be general for any angle θ.

12. MIP Cont. • How Does it Work ? • Parallel virtual rays are applied to the volume at certain angle θ. • The max. intensity voxel in the path of each ray is searched. • The searched voxels forms a MIP image at angle θ. • Rays are rotated around certain axis to find the rest of the MIP images.

13. MIP Cont.

14. MIP Cont. The Rays are allowed to rotate around X-axis Y-axis  Z-axis In steps of 5 degrees Z X Y

15. Results • MRA data sets were collected using GE MEDICAL SYSTEMS Genesis Signa MRI system. • MRA data are 256x256x117, 1 mm. • MRV data are 256x256x84, 2 mm. • Our algorithms are implemented using C++ under Unix on SGI-Onyx 2 Supercomputer. • We used VTK as a visualization toolkit to render results in stereo on Immersa-Desk virtual reality system.

16. MRA Results Side By Side Visual Validation for Patient 1 Before Segmentation (First Row) θ = 0 θ = 25 θ = 45 θ = 90 After Segmentation (Second Row)

17. MRA Results Cont. Side By Side Visual Validation for Patient 2 Before Segmentation (First Row) θ = 0 θ = 25 θ = 45 θ = 90 After Segmentation (Second Row)

18. MRA Results Cont. 3D Visualization on Imersa-Desk Patient 1 Patient 2 After Segmentation

19. MRV Results 3D Visualization on Imersa-Desk Patient 3

20. Conclusion • We have presented a robust technique for automatic segmentation of the vascular tree from MRA and MRV. • We also presented a new MIP-based technique for validating the segmentation algorithm by projecting both the original and segmented volume at different angles and compare the resultant MIP images side by side. • A radiologist and a neurosurgeon have validated our results successfully. The accuracy of the results has shown details of the vascular structure down to the limits of the MRI system.

22. CVIP_Medica • I started the nucleus of a medical software package called CVIP_Medica. • This library is supposed to offer an extensive practical algorithms and utility functions for medical researchers. • The library is built entirely in C++ under Unix. • Although it is a medical library, but there are common utilities that may be shared between different groups in the lab. • The library is open source for CVIP students, such that each one can access and modify it to suit his goals.

23. Medica Design • The library is designed to be AS GENERAL AS possible; i.e, not specific to certain application. • The function names are selected to reflect their usage. Ex: PGMVolume class void Read(int READING); void SaveProcessedVolume(); void SaveProcessedVolume(char *pathPrefix); void SaveSegmentedVolume(); void SegmentTree(int Rmin, int Rmax, int SeedSliceNo); void Histogram(); void MIP_Z(int startAngle, int endAngle, int stepAngle); void MIP_Y(int startAngle, int endAngle, int stepAngle); void MIP_X(int startAngle, int endAngle, int stepAngle); Ex: PGMFile class char** Read(char *filename); Void Save(); int SaveProcessedFile(char *newfilename); int Save(char *newfilename, char **image); char ** Invert(); char ** Segment(int Rmin, int Rmax, int RENDER); int * Histogram();

24. Medica Example 1 • Ex 1: • #include “PGMVolume.h”; • int main() { • PGMFile file; • char ** image = file.Read(“/sw/people.kingtut/msabry/patient1.pgm"); • image = file.Segment(40, 200, WHITE); • image = file.Invert(); • file.Save(“segment.pgm", image); • }

25. Medica Example 2 • Ex 1: • #include “PGMVolume.h”; • int main() { • PGMVolume volume("/msabry/sarah.006","/msabry/tmp/va", "pgm" , 1, 117); • volume.Read(FORMAL); • volume.SegmentTree(40, 255, 113); • volume1.MIP_Z(0, 90, 5); • volume1.MIP_Y(0, 90, 5); • volume1.MIP_X(0, 90, 5); • }

26. Future Work Segmentation of the Vascular Tree from the surrounding noise and tissues using one of the classification techniques.

27. CARS 2002Computer Assisted Radiology and Surgery 16th International Congress and Exhibition Achievements • This work has been accepted in CARS CARS 2002 Computer Assisted Radiology and Surgery 16th International Congress and Exhibition June 26-29, 2002 Palais des Congrès, Paris, France • This work has been nominated for SGI Award • for “Excellence in Visualization”

28. Thank You Mohamed Sabry