Transcript Quantitative Analysis of Static Ventilation

Quantitative Analysis of Static Ventilation
Hyperpolarized 3He MR Images
Ajna Borogovac
Boston University - College of Engineering
Harvard Medical School - Radiology Department - Brigham and Women’s Hospital
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.
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
Inflammation
• Fourth Leading Cause of Death in U.S.
• Causes of airway obstruction:
Mucus
Destroyed
Alveoli
1.) Destruction and collapse of smaller airways
2.) Alveolar wall loss
3.) Thickening of inflamed airways
4.) Sticky mucus secretions
Background
• Pulmonary Imaging Modalities
– Computed Tomography (CT)
– Positron Emission Tomography (PET)
– Magnetic Resonance Imaging (MRI)
Background
• Pulmonary Imaging Modalities
– Magnetic Resonance Imaging (MRI)
Background
• Pulmonary Imaging Modalities
– Magnetic Resonance Imaging (MRI)
Background
• Pulmonary Imaging Modalities
– Magnetic Resonance Imaging (MRI)
Background
• Pulmonary Imaging Modalities
– Magnetic Resonance Imaging (MRI)
Background
– Magnetic Resonance Imaging
• Water based - can’t 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
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 patient’s
ventilation distribution over time.
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with
Simulated Defect
Patient Ventilation Profile
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with
Simulated Defect
Patient Ventilation Profile
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with
Simulated Defect
Patient Ventilation Profile
• A RMSE-minimizing mathematical fit
between pixel intensities and small area
increments across tube diameter was
found.
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with
Simulated Defect
Patient Ventilation Profile
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile
with Simulated Defect
Patient Ventilation Profile
• 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.
a.) Homogenous Defect
b.) Parabolic Defect:
Methods
Collect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with
Simulated Defect
• 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.
Patient Ventilation Profile
Ventilation
pixels located
using threshold
filtering
Lung
boundary
prescription
Resultant pixels
over which
ventilation is
calculated
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
Results
Healthy specific ventilation profiles were created.
Specific Ventilation
1
- Local specific ventilation in central
axial locations of lung is steady:
fluctuating by no more than 15%
from the local mean.
0 .2 .4 .6 .8
Left Lung
.1
.2
.3
.4
.5
.6
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.8
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1
.4
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1
1
0
.6 .8
Right Lung
0 .2 .4
Specific Ventilation
•
0
.1
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Axial Lung Length
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.
10
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.
•
0 .2 .4 .6 .8
Left Lung
.1
.2
.3
.4
.5
.6
.7
.8
.9
1
.4
.5
.6
.7
.8
.9
1
1
0
Right Lung
0 .2 .4 .6 .8
Specific Ventilation
1
Specific Ventilation
0
.1
.2
.3
Axial Lung Length
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.
Acknowledgments
• Mitchell Albert, Dr.
• Yang Tzeng Sheng