quantitative analysis of static ventilation hyperpolarized 3 he mr images ajna borogovac boston...
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Quantitative Analysis of Static Ventilation Hyperpolarized 3He MR Images
Ajna BorogovacBoston 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 lining3.) Sticky mucus secretions
Destroyed Alveoli
Mucus
Inflammation
Collapsed Airway – COPD
• Fourth Leading Cause of Death in U.S.• Causes of airway obstruction:
1.) Destruction and collapse of smaller airways2.) Alveolar wall loss 3.) Thickening of inflamed airways 4.) Sticky mucus secretions
• Pulmonary Imaging Modalities
Background
– Magnetic Resonance Imaging (MRI)
– Computed Tomography (CT)– Positron Emission Tomography (PET)
• Pulmonary Imaging Modalities
Background
– Magnetic Resonance Imaging (MRI)
• Pulmonary Imaging Modalities
Background
– Magnetic Resonance Imaging (MRI)
Magnetic
Field
• Pulmonary Imaging Modalities
Background
– Magnetic Resonance Imaging (MRI)
RF Pulse
Magnetic
Field
• Pulmonary Imaging Modalities
Background
– Magnetic Resonance Imaging (MRI)
RF (MR SIGNAL)
Magnetic
Field
– Magnetic Resonance Imaging• Water based - can’t image lungs
Homogenous signal: healthy ventilation
Heterogenous signal: ventilation defect
Background
– Hyperpolarized 3He MR Imaging• 3He based - enables ventilation studies• Previous Studies: Qualitative analysis of signal distribution
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.
MethodsCollect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
MethodsCollect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
MethodsCollect 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.
MethodsCollect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
MethodsCollect 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:
MethodsCollect HP 3He MR Images
Pixel Intensity vs. 3He Amount
Healthy Ventilation Profile
Healthy Ventilation Profile with Simulated Defect
Patient Ventilation Profile
• 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.
Resultant pixels over which ventilation is calculated
Lung boundary prescription
Ventilation pixels located using threshold filtering
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.
- Local specific ventilation in central axial locations of lung is steady: fluctuating by no more than 15% from the local mean.
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Specific Ventilation
Left Lung
Right Lung
Axial Lung Length
0
.2
.4
.6
.8
1
0
.2
.4
.6
.8
1
Spe
cifi
c V
entil
atio
n
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 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Specific Ventilation
Left Lung
Right Lung
Axial Lung Length
0
.2
.4
.6
.8
1
0
.2
.4
.6
.8
1
Spe
cifi
c V
entil
atio
n
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