Quantitation of vessel morphology from 3D MRA

19Citations
Citations of this article
31Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Three dimensional magnetic resonance angiographic images (3D MRA) are routinely inspected using maximum intensity projections (MIP). However, accuracy of stenosis estimates based on projections is limited. Therefore, a method for quantitative 3D MRA is introduced. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. First, the central vessel axis is determined. Subsequently, the vessel wall is segmented using knowledge of the acquisition process. The user interaction to initialize the model is performed in a 3D setting. The method is validated on a carotid bifurcation phantom and also illustrated on patient data.

References Powered by Scopus

Snakes: Active contour models

13656Citations
N/AReaders
Get full text

MARCHING CUBES: A HIGH RESOLUTION 3D SURFACE CONSTRUCTION ALGORITHM.

8343Citations
N/AReaders
Get full text

Multiscale vessel enhancement filtering

3571Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes

818Citations
N/AReaders
Get full text

A review on MR vascular image processing: Skeleton versus nonskeleton approaches: Part II

120Citations
N/AReaders
Get full text

Analyzing attributes of vessel populations

71Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Frangi, A. F., Niessen, W. J., Hoogeveen, R. M., Van Walsum, T., & Viergever, M. A. (1999). Quantitation of vessel morphology from 3D MRA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1679, pp. 358–368). Springer Verlag. https://doi.org/10.1007/10704282_39

Readers over time

‘09‘11‘12‘13‘14‘15‘16‘17‘18‘19‘20‘22‘25036912

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 13

52%

Researcher 8

32%

Professor / Associate Prof. 4

16%

Readers' Discipline

Tooltip

Computer Science 8

40%

Engineering 7

35%

Medicine and Dentistry 4

20%

Environmental Science 1

5%

Save time finding and organizing research with Mendeley

Sign up for free
0