A digital contrast agent for vulnerable plaque Imaging

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A digital contrast agent for vulnerable plaque
Imaging
Manos Papadakis Mathematics, University of Houston
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Collaboration Members
Image Analysis R. Azencott, PhD B. G. Bodmann,
PhD D. J. Kouri, PhD M. Papadakis, PhD S. K.
Alexander, PhD J. R. Romero, PhD S. Baid, S.
Jain, Xiao Li, I. Walimuni.
THI Collaborators S. W. Casscells, MD S. D.
Gertz, MD, PhD D. Vela, MD I. Aboshady, MD
P. Cherukuri (Rice U.) J. L. Conyers, PhD, Q.
Frazier RN, DSN, NP R. M. Mazraeshadi, MD, A.
Zarrabi, MD and J.T. Willerson, MD. MDACC
Collaborators D. Cody, PhD G. Gladish, MDE.
Johnson
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Digital Contrast Agent

• A Digital Contrast Agent is an algorithm that
  helps an expert interpreter to identify
  diagnostically significant tissue types in
  biomedical images.
• A Digital Contrast Agent is an electronic
  biomarker sensitive to certain tissue types.

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Why do we need a DCA?

• A DCA marks the location of a lesion of interest.
  
• It produces digital stains for different tissue
  types
• A DCA is a software platform that can be used
  with many modalities and for various diagnostic
  purposes.
• So far we are using our DCA to image vulnerable
  plaque ex-vivo.

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How it works (Principles)

• Tissues of interest have unique texture
  signatures.
• These signatures can be created by the structure
  of a tissue(s) of interest and/or by the presence
  of biomarker.
• DCA is trained to recognize these signatures.
• DCA can work with high levels of noise and even
  when tissues of interest are laden.

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Advantages of DCA

• Can be adopted for a variety of modalities, e.g.
  IVUS, intravascular MRI, conventional MRI, PET
  and others.
• Image processing is done in 3D or 2D resulting in
  higher sensitivity.
• Can stain lesions which are laden.

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Results of Ex-vivo plaque study

• Micro-CT voxel size is 0.027mm
• Flat panel voxel size is 0.127mm. Tomographic
  reconstruction can be carried out at 0.080mm
• Study of clinical 64-slice angiography in initial
  stage.

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Results of Ex-vivo study

• Processed CT-volumes with the Digital Contrast
  Agent algorithm were examined. The expert
  interpreter looked for lipid pools in each
  processed CT-volume
• The sample average of the sensitivity to lipid
  pools
• Reached 83 with Ca and
• Lipid can be detected without speckled Ca.
• The specificity is greater than 95
• With the flat panel we see similar qualitative
  results in tissue characterization

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28 WITH CALCIUM
UNPROCESSED
PROCESSED
µCT
DCA OUTPUT
HISTOLOGY
TISSUE CLASSIFICATION
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28 DECAL, µCT VS Flat Panel CT
µCT/ Flat Panel
TISSUE CLASSIFICATION
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