Bayesian analysis of dynamic MR breast images

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Bayesian analysis of dynamic MR breast images

• P. Barone, F. de Pasquale, G. Sebastiani
• Istituto per le Applicazioni del Calcolo M.
  Picone
• CNR, Rome (Italy)
• J. Stander
• Department of Mathematics and Statistics
• University of Plymouth, UK
• M. Crecco, A. M. Di Nallo, F. P. Gentile
• Istituto Regina Elena
• Rome (Italy)

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Introduction

• Data and aims of the analysis
• Dynamic breast MRI consists of a temporal
  sequence of images of the same slice acquired
  after the injection of a contrast agent into the
  blood stream
• Typically, for breast studies, a few tens of 256
  x 256 images are acquired consecutively
• In vivo tissue perfusion maps in an organ of
  interest (breast)

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• These data are typically affected by two main
  kind of degradation
• random degradation due to the measurement
  noise
• deterministic degradation due to the
  patient motion
• Radiologistss aim is to extract as much clinical
  information as possible from the image sequence
  as few images of easy interpretation and with low
  degradation (label each pixel of the image as
  non tumoral, benign tumoral and malign tumoral
  tissues)
• real time analysis

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Data
first sequence image
last sequence image

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Non-parametric approach

• Bayesian estimation of true image intensity for
  each pixel of a selected R.O.I. and for each time
  without using a parametric spatio temporal model
  for the acquired signal
• Relevant quantities (class attributes) describing
  image intensity variation are computed
  independently for each pixel from these estimated
  image intensity profiles
• These are used to classify the underlying scene
  into a certain number of categories based on the
  temporal pattern of intensity

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Estimation results
Original R.O.I
Estimated R.O.I.
Patient 1
Patient 2
Patient 3
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Non-parametric approach estimated attributes
First attribute
Second attribute
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Parametric approach

• We adopted here a parametric spatio temporal
  model for the acquired signal to describe true
  image intensities at each pixel
• Model parameters are then displayed as images and
  can be used for image classification
• After paramater estimation, the model can be used
  to estimate true image intensities.

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Spatio temporal model paramaters
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Classification

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Classification results