Sie befinden sich hier

Inhalt

Flow Quantification from 2D PC-MRI

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

F.G. Zöllner1,2, J. A. Monssen3, J. Roervik2,3, A. Lundervold4

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

1 Computer Assisted Clinical Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Germany
2 Dept. of Surgical Sciences, University of Bergen, Norway
3 Dept. of Radiology, Haukeland University Hospital , Norway
4 Dept. of Biomedicine, University of Bergen, Norway

Nach obenDruckenImpressumDatenschutz