Quantitative Analysis of Static Ventilation Hyperpolarized 3He MR Images

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Quantitative Analysis of Static Ventilation
Hyperpolarized 3He MR Images

• Ajna Borogovac
• Boston University - College of Engineering
• Harvard Medical School - Radiology Department -
  Brigham and Womens Hospital

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Objectives

• Determine mathematical relationship between
  intensity of a HP 3He MR image pixel and amount
  of 3He in the corresponding object voxel
• Determine trachea ventilation
• Develop means of creating specific ventilation
  profiles of healthy and diseased lungs
• Investigate sensitivity of the ventilation
  profiles to defect magnitude and size.

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Background

• Pulmonary Ventilation Disorders
• Asthma
• Afflicts 18 million Americans
• Causes of airway obstruction
• 1.) Bronchospasm
• 2.) Inflammation of airway lining
• 3.) Sticky mucus secretions

Collapsed Airway

• COPD
• Fourth Leading Cause of Death in U.S.
• Causes of airway obstruction
• 1.) Destruction and collapse of smaller airways
• 2.) Alveolar wall loss
• 3.) Thickening of inflamed airways
• 4.) Sticky mucus secretions

Inflammation
Mucus
Destroyed Alveoli
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Background

• Pulmonary Imaging Modalities

• Computed Tomography (CT)
• Positron Emission Tomography (PET)

• Magnetic Resonance Imaging (MRI)

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Background

• Pulmonary Imaging Modalities

• Magnetic Resonance Imaging (MRI)

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Background

• Pulmonary Imaging Modalities

• Magnetic Resonance Imaging (MRI)

Magnetic Field
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Background

• Pulmonary Imaging Modalities

• Magnetic Resonance Imaging (MRI)

Magnetic Field
RF Pulse
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Background

• Pulmonary Imaging Modalities

• Magnetic Resonance Imaging (MRI)

Magnetic Field
RF (MR SIGNAL)
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Background

• Magnetic Resonance Imaging
• Water based - cant image lungs

• Hyperpolarized 3He MR Imaging
• 3He based - enables ventilation studies
• Previous Studies
• Qualitative analysis of signal distribution

Homogenous signal healthy ventilation
Heterogenous signal ventilation defect
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Our Interest

• Development of Quantitative Analysis Methods
• Possibility of developing more accurate
  diagnostic tools for measurement of ventilation.
• Test efficacy of various treatments
• Map progress of the ailment by tracking a
  patients ventilation distribution over time.

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Methods

Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
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Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
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Methods

• A RMSE-minimizing mathematical fit between
  pixel intensities and small area increments
  across tube diameter was found.

Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
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Methods

Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
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Methods

• Simulated defects of various radii and strengths
  across the healthy ventilation HP 3He MR image
  slices.
• Compared the resulting specific ventilation
  profiles with the healthy ventilation profile
  obtained previously.

Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
a.) Homogenous Defect b.) Parabolic Defect
Patient Ventilation Profile
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Methods

• The specific ventilation profile for one mild
  asthmatic was created with the same algorithm as
  used for healthy lungs.
• One modification lung boundary has to be user
  defined where lung edge is affected by a
  ventilation defect.

Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
Resultant pixels over which ventilation is
calculated
Lung boundary prescription
Ventilation pixels located using threshold
filtering
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Results

• Linear relationship is the best mathematical fit
  between image pixel intensity and amount of 3He
  in a corresponding image voxel.

Representative data for 1.5875 cm diameter tube
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Results

• Healthy specific ventilation profiles were
  created.

- Local specific ventilation in central axial
locations of lung is steady fluctuating by no
more than 15 from the local mean.
Specific Ventilation
Left Lung
0 .1 .2 .3 .4 .5 .6
.7 .8 .9 1
0 .2 .4 .6 .8 1 0
.2 .4 .6 .8 1
Specific Ventilation
Right Lung
0 .1 .2 .3 .4 .5 .6
.7 .8 .9 1
Axial Lung Length
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Results

• Specific ventilation profiles obtained using our
  methods are not sensitive enough to detect
  defects that are too small or too weak.
• The overall effect of any defect on specific
  axial ventilation profile has at least 15
  uncertainty associated with it.

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Results

• HP 3He MRI scan of a patient lung showed small
  defects along the axial center of the left lung.
• The specific ventilation profile of the patient
  was found to be not sensitive enough to locate
  these defects.

Specific Ventilation
Left Lung
0 .1 .2 .3 .4 .5 .6
.7 .8 .9 1
Specific Ventilation
0 .2 .4 .6 .8 1 0
.2 .4 .6 .8 1
Right Lung
0 .1 .2 .3 .4 .5 .6
.7 .8 .9 1
Axial Lung Length
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Conclusions

• There exists a linear relationship between
  intensity of an image pixel and the amount of 3He
  in a corresponding object voxel.
• Ventilation profile of healthy lung is steady in
  central axial locations, fluctuating by no more
  than 15 from the local mean.
• The specific ventilation profiles obtained using
  our methods are not sensitive enough to detect
  ventilation defects of too small a size or
  magnitude.

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Acknowledgments

• Mitchell Albert, Dr.
• Yang Tzeng Sheng