Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography

Ling Zhang, Brian E. Chapman, Dennis L. Parker, John A. Roberts, Junyu Guo, Prashanthi D Vemuri, Sung M. Moon, Frederic Noo

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Objectives: We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed. Materials and Methods: After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting. Results: Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual [ intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels. Conclusions: To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.

Original languageEnglish (US)
Pages (from-to)661-671
Number of pages11
JournalInvestigative Radiology
Volume40
Issue number10
DOIs
StatePublished - Oct 2005
Externally publishedYes

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Magnetic Resonance Angiography
Blood Vessels
Costs and Cost Analysis
Skeleton

Keywords

  • Bifurcation detection
  • Centerline extraction
  • Curve fitting
  • Dynamic programming
  • High-resolution magnetic resonance angiography (MRA)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography. / Zhang, Ling; Chapman, Brian E.; Parker, Dennis L.; Roberts, John A.; Guo, Junyu; Vemuri, Prashanthi D; Moon, Sung M.; Noo, Frederic.

In: Investigative Radiology, Vol. 40, No. 10, 10.2005, p. 661-671.

Research output: Contribution to journalArticle

Zhang, Ling ; Chapman, Brian E. ; Parker, Dennis L. ; Roberts, John A. ; Guo, Junyu ; Vemuri, Prashanthi D ; Moon, Sung M. ; Noo, Frederic. / Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography. In: Investigative Radiology. 2005 ; Vol. 40, No. 10. pp. 661-671.
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AU - Guo, Junyu

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AU - Moon, Sung M.

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N2 - Objectives: We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed. Materials and Methods: After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting. Results: Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual [ intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels. Conclusions: To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.

AB - Objectives: We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed. Materials and Methods: After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting. Results: Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual [ intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels. Conclusions: To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.

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