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Flow Quantification from 2D PC-MRI in Renal Arteries using Clustering

F.G. Zöllner1,2, J. A. Monssen3, J. Roervik2,3, A. Lundervold4
1Computer Assisted Clinical Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Germany
2Dept. of Surgical Sciences, University of Bergen, Norway
3Dept. of Radiology, Haukeland University Hospital , Norway
4Dept. of Biomedicine, University of Bergen,Norway

Introduction

  • Renal Disease caused e.g. by hypertension
  • Renal Artery Stenosis (RAS) leading cause for renal hypertension
  • Loss of renal parenchyma in the stenosed kidney
  • Microangiopathy in glomeruli in non-stenosed contralateral kidney
  • Important parameter for choosing patients to treat by PTA:
    • Kidney volume
    • Flow reduction
    • Flow velocity
    • Renal function
  • Cine Phase Contrast MRI tool for grading stenosis
  • Can be used for assessing blood flow and velocity

Methods

  • Today: mainly manual ROI drawing in magnitude images, frame-by-frame
  • Automated procedures:
    • Active contours
    • Correlation and manual thresholding
  • K-Means clustering of velocity profiles in phase images
  • Random initialisation of algorithm, 10 repetitions
  • Explored cosine distance function, Eucleadian and correlation distance

Data Acquisition

  • 5 subjects (3 healthy volunteers, 2 patients)
  • ECG gated 2D Cine PC-MR sequence
  • TE=4ms,TR=37ms, FA=30°, VENC=100 cm/s
  • 20-25 images per cardiac cycle, RR-Interval 758-1072ms, 2 averages, matrix 256x192
  • Slice thickness 6mm, in-plane resolution <0.5mm2
  • Transec. slice through vessel 1-2cm distal of stenosis

Results
  • Descrimination of vessel from background for K=2
  • Analysis of optimal cluster number:
    • 2 local minima for DB-Index
    • K=3-5 and K=8-10 depending on the subject
  • For K>2 vessel area devided into subcluster
  • Cosine distance function yielded best results
  • Phase wrap included in one dataset showed up as a seperate cluster
  • Comparison manual vs. clustering showed high similarity in the velocity profiles

Discussion

  • Semi-automatic approach using K-Means clustering
  • Reduced manual interaction
  • Only number of clusters has to be given
  • Obtained velocity profiles similar to manual delineations
  • Detection of different blood flow patterns at higher K values, i.e. more allowed clusters
  • Interpretation needs further investigation
  • Conclusion: approach can aid in clinical assessment and grading of renal artery stenosis
Contact: Dr. Frank Zöllner last modified: 16.12.2010
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