Analysis of the Parameter Space of a Metric for Registering 3D Vascular Images

Aylward, Stephen; Weeks, Sue; Bullitt, Elizabeth

doi:10.1007/3-540-45468-3_111

Cited by 8 publications

(5 citation statements)

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“…Although deformable mapping of all vasculature into a single patient's coordinate system might reduce normal variability and, thus, be desirable, such mapping might also have undesirable effects. We do not yet know the optimal method of mapping vasculature between patients, and it is an active area of research of our group [35], [36].…”

Section: Discussionmentioning

confidence: 99%

Measuring tortuosity of the intracerebral vasculature from MRA images

Bullitt

Gerig

Pizer

et al. 2003

IEEE Trans. Med. Imaging

371

351

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The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

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Section: Discussionmentioning

confidence: 99%

Measuring tortuosity of the intracerebral vasculature from MRA images

Bullitt

Gerig

Pizer

et al. 2003

IEEE Trans. Med. Imaging

371

351

View full text Add to dashboard Cite

show abstract

“…Additionally, the parameter w i is used to weight samples depending on their radius r i ; weighting increases from w i =0 at r i =0 to an asymptote of w i ∼ = 1 at r i ∼ = 3. These parameters have been analyzed in [2] and [3].…”

Section: Vessel-to-image Affine Registrationmentioning

confidence: 99%

Vascular Atlas Formation Using a Vessel-to-Image Affine Registration Method

Chillet

Jomier

Cool

et al. 2003

Lecture Notes in Computer Science

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Abstract. We have developed a method for forming vascular atlases using vascular distance maps and a novel vascular model-to-image registration method. Our atlas formation process begins with MR or CT angiogram data from a set of subjects. We extract blood vessels from those data using our tubular object segmentation method. One subject's vascular network model is then chosen as a template, and its vascular distance map (DM) image is computed. Each of the remaining vascular network models is then registered with the DM template using our vascular model-to-image affine registration method. The DM images from the registered vascular models are then computed. The mean and variance images formed from those registered DM images are the vascular atlas. In this paper we apply the atlas formation process to build atlases of normal brain and liver vasculature. We use Monte Carlo simulations to demonstrate the reliability of the underlying registration method. Additionally, we explain the clinical potential of those atlases and conduct z -score analyses to compare individuals with the atlases to detect abnormal vessels.

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“…Alternative Ansätze zur Realisierung von Navigationssystemen in der Leberchirurgie werden im Computer Aided Diagnosis and Display Lab (CADDLab) der University of North Carolina (Gefäßmodell zu 3D-Ultraschall-Registrierung), am Center for Technology-Guided Therapy (TGT) der Vanderbilt University (Registrierung optischer Marker auf der Leberoberfläche mit korrespondierenden Punkten im Computertomogramm) und im Dept. of Radiology, Faculty of Medicine der University of Tokyo (Registrierung geometrischer und physikalischer Lebermodelle mit Oberflächenabtastpunkten der Leber) untersucht[14][15][16].Mit dem ARION-Projekt (Augmented Reality for intraoperative Navigation) des DKFZ in Heidelberg wurde erstmalig ein System zur Navigation von leberchirurgischen Eingriffen vorgestellt…”

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