Three-dimensional Bone-free Rendering of the Cerebral Circulation by Use of Computed Tomographic Angiography and Fuzzy Connectedness

INTRODUCTION Three-dimensional computed tomographic angiography (3-D-CTA) can be used to evaluate cerebrovascular disease. A potential limitation of this technology is obscuration of vascular anatomy by bone. In view of this, we developed a method for bone-free rendering using iterative relative fuzzy connectedness (IRFC) of 3-D-CTA to examine the cerebral vasculature without the intervening cranial base. METHODS 3-D-CTA was obtained in 10 patients with cerebrovascular disease by use of an algorithm based on IRFC. Bone structures were removed and vascular anatomy was isolated with an almost completely automated process. Bone-removed images were rendered via maximum intensity projections, compared with digital subtraction angiography, and evaluated for vascular abnormalities. RESULTS Compared with digital subtraction angiography, IRFC 3-D-CTA successfully defined the intracranial vascular anatomy in all 10 patients. An aneurysm was identified in 6 patients, bilateral carotid-cavernous fistulae in 1 patient, and no structural abnormalities in the remaining 3 patients. CONCLUSION In 3-D-CTA, our preliminary findings suggest that bone obscuration, one of the current limitations of CTA, can be overcome through appropriate computer algorithms. We are now working on improving the computational efficiency of these algorithms to allow routine use of IRFC 3-D-CTA in an interventional or surgical suite.

[1]  R G Dacey,et al.  CT infusion scanning for the detection of cerebral aneurysms. , 1989, Journal of neurosurgery.

[2]  L D Cromwell,et al.  Three-dimensional computerized tomography angiography in the diagnosis of cerebrovascular disease. , 1992, Journal of neurosurgery.

[3]  Dewey Odhner,et al.  3DVIEWNIX: an open, transportable, multidimensional, multimodality, multiparametric imaging software system , 1994, Medical Imaging.

[4]  Robert E. Harbaugh,et al.  Three-dimensional computed tomographic angiography in the preoperative evaluation of cerebrovascular lesions. , 1995, Neurosurgery.

[5]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[6]  Glenn B. Anderson,et al.  Experience with computed tomographic angiography for the detection of intracranial aneurysms in the setting of acute subarachnoid hemorrhage. , 1997, Neurosurgery.

[7]  T Faillot,et al.  Three-dimensional computed tomographic angiography in detection of cerebral aneurysms in acute subarachnoid hemorrhage. , 1997, Neurosurgery.

[8]  P. Lai,et al.  Detection and assessment of circle of Willis aneurysms in acute subarachnoid hemorrhage with three-dimensional computed tomographic angiography: correlation with digital substraction angiography findings. , 1999, Journal of the Formosan Medical Association = Taiwan yi zhi.

[9]  Jayaram K. Udupa,et al.  Fuzzy connected object definition in images with respect to co-objects , 1999, Medical Imaging.

[10]  M Takahashi,et al.  Intracranial aneurysms: detection with three-dimensional CT angiography with volume rendering--comparison with conventional angiographic and surgical findings. , 1999, Radiology.

[11]  J P Villablanca,et al.  Volume-rendered helical computerized tomography angiography in the detection and characterization of intracranial aneurysms. , 2000, Journal of neurosurgery.

[12]  J M Wardlaw,et al.  Can noninvasive imaging accurately depict intracranial aneurysms? A systematic review. , 2000, Radiology.

[13]  Jayaram K. Udupa,et al.  Fuzzy-connected 3D image segmentation at interactive speeds , 2000, Medical Imaging: Image Processing.

[14]  J. Udupa,et al.  Iterative relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[15]  Jayaram K. Udupa,et al.  Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation , 2000, Comput. Vis. Image Underst..