Lung Outline Reconstruction for VentilationPerfusion Images Towards PIOPEDCompliant Feature Extracti

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Lung Outline Reconstruction for
Ventilation/Perfusion Images Towards
PIOPED-Compliant Feature Extraction

• Advisor Dr. Gursel Serpen
• Graduate student Ms. Rama Iyer

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Contents

• Pulmonary Embolism and V/Q scans
• Automated diagnosis system
• Lung outline reconstruction system
• Eigenlungs algorithm
• Neural PCA network algorithm
• Simulation
• Testing Results
• Conclusions
• Future study recommendations

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Pulmonary Embolism (PE)

• Blood clots break off from their source and
  become emboli
• Emboli travel through the heart into the
  pulmonary arteries
• They occlude the arteries to various anatomic
  regions of the lung

300,000 to 600,000 hospitalizations and 50,000
People die each year from PE NIH Consensus
Statement cited May 2001
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Groups Facing Higher Probability of Pulmonary
Embolism

• Patients undergoing various types of surgery -
  general, urological, neuro-surgical,
  gynecological
• Patients with orthopedic problems and chronic
  diseases

• These groups face a higher probability of
  pulmonary embolism due to the high risk of
  developing deep venous thrombosis

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Perfusion Scans

• An intravenous injection of the radioactive
  isotope technetium-99m is given
• Detection of any blockages of blood flow through
  the vessels of the lungs
• If the pulmonary blood vessels are blocked by
  pulmonary emboli (clots), a reduced amount or no
  blood flows into the blocked area
• If pulmonary embolism is present, the image shows
  a reduced or absent distribution of
  technetium-99m to the involved area

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Perfusion Scans in Multiple Views
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Ventilation Scans

• A radioactive gas is inhaled. Which gets into
  the airways of the lung
• This shows the distribution of the inhaled
  radioactive gas in the airway
• By combining the information obtained from both
  scans a more accurate diagnosis of pulmonary
  embolism can be made

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Segmented Lung Anatomy(Anterior View)
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PIOPED Criteria

• Low probability
• Multiple matching V/Q defects
• Corresponding V/Q defects and CXR parenchymal
  opacity in upper or middle lung zone
• gt 3 small SPD
• Very low probability
• lt 3 small SPD
• Normal
• No perfusion defects and perfusion outlines the
  shape of the lung seen on CXR

• High probability
• gt 2 large segmental perfusion defects (SPD)
• 1 large SPD and gt 2 moderate SPD
• gt 4 moderate SPD
• Intermediate probability
• 1 moderate SPD
• Corresponding V/Q defect and CXR opacity in lower
  lung
• Single moderately matched V/Q defect

CXR Chest Radiograph V/Q
Ventilation-Perfusion
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Process Followed by a Radiologist to Diagnose PE
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Why Automated Diagnosis???

• It has the potential to reduce inter-observer
  variability
• It can offer a competent second opinion
• It offers the expertise of an expert radiologist
  in interpreting scans when an expert radiologist
  is not available

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Block Diagram of Automated System
V/Q Scans
Image Pre-Processing
Lung Outline Reconstruction
Labeling of Lung Segments
Listing of VQ Defects
PE Diagnosis
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Recent Work

• Armato et. Al.1997 superimposed ventilation and
  perfusion images on chest radiographs for
  reconstructing the outline of the lung
• Drawback outline can only be created for the
  anterior or the posterior view of
  lungs
• Hasegawa et. Al. 1998 made use of a shift
  invariant neural network to reconstruct the lung
  outline from digital chest radiographs
• Drawbacks outline can be created only for the
  anterior or posterior view
• The recreated outline consists of
  eroded boundaries which

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Algorithms Used for Lung Outline Reconstruction

• Eigenlungs algorithm
• Neural PCA network algorithm

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Lung Templates

• These templates are a good representative of the
  general human population. (Confirmed by the
  radiologists at MCO)
• Each template has a resolution of 120x128.

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Graphical Representation of the Eigenlungs
Algorithm
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Eigenlungs Method for Lung Outline Reconstruction

• Eleven lung templates are stored using
  the eigenlungs algorithm
• Where x has a dimension of 15360x1
• A matrix P is created, whose columns
  consists of the eleven lung
• Templates
• A matrix is computed as
  ,which has a dimension of 11x11

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• The eigenvalues and eigenvectors of
  this matrix are computed
• Note that the covariance matrix would have
  been computed as
• Which would have had a dimension of 15360x15360
• The eigenvalues and eigenvectors of the
  covariance matrix can be computed using the
  eigenvalues and eigenvectors of the matrix

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Now to recognize a new image , An
inner product of the new image vector is computed
with each of these 11 eigenvectors. The
resultant is a point in the eigenspace given by
,    In a similar manner all the 11 lung
templates are projected onto the eigenspace.
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• The distance between the test image and each of
  the 11 lung templates in eigenspace is computed
• The lung template corresponding to the minimum
  distance is the closest matching template to the
  test image

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Graphical Representation of Neural PCA Network
Algorithm
Lung Templates
NEURAL PCA NETWORK
EIGENLUNGS USING NEURAL PCA
RECALLED LUNG TEMPLATE
INPUT IMAGE
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Neural PCA Network
Network Topology
x1
x2
x15360
Number of Input nodes -15360 Number of Output
nodes - 1
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• The output of the network is given by
• The weight matrix is updated using the formula
• where h is the learning rate.

• Once the network weights stop changing
• The output Y is the first principal component of
  the input vector.
• The weight matrix is the eigenvector associated
  with the first
• principal component.

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• The eleven lung templates are given as input to
  the neural network one at a time and the
  eigenvector of their first principal component
  are computed
• These eleven eigenvectors are then used to recall
  the matching template using the same method as in
  Eigenlungs

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Testing Results

• The segmented view of the anterior lung is
  superimposed on this template
• Testing was done using two sets of test cases

Synthetically Created Test Cases - 25 test
cases were created. - 5 for each
probability of PE.
Test Cases Obtained from the Medical College of
Ohio - 5 test cases were obtained from the MCO
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Procedure for Creating the Test Cases

• One of the eleven lung templates is selected at
  random
• Defects(small,moderate or large) are created
  according to PIOPED criteria in various segments

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Creating the Test Case
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Test Results(Normal, Very-Low, Low and
Intermediate Probability
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Test Results(High Probability)
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Comparing the Test Results for Synthetically
Created Test Cases
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Test Results for Test Cases From MCO
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Comparing the Test Results for Test Cases
Obtained From MCO
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Conclusions

• Both Eigenlungs algorithm and neural PCA
  algorithm can be extended to all the other
  views(posterior, right lateral, left lateral,
  etc.)
• Reconstructed outline does not result in eroded
  boundaries
• Both the algorithms performed satisfactorily
• Neural PCA performs 12 better for synthetically
  created test cases

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Future Tasks

• Storing more than eleven templates which show
  variations in shape and size of the lung using
  the algorithms
• Using a probabilistic matching criteria to find
  the closest matching template in eigenspace

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Thank You