| dc.description.abstract |
<p>Radiation-induced pulmonary toxicity poses a serious challenge and limiting factor
in delivering a sufficient amount of dose to eradicate thoracic tumors without compromising
lung function. Functional avoidance radiation therapy (RT) using lung ventilation
mapping techniques would allow for preferential avoidance of functional lung tissue
during radiotherapy and potentially reduce RT-induced lung injuries. Additionally,
lung ventilation is also a key metric to assess lung function in patients with pulmonary
diseases such as asthma, pulmonary embolism, cystic fibrosis, and chronic obstructive
pulmonary disease (COPD). In contrast to global pulmonary function tests such as spirometry,
ventilation images provide a regional measure of pulmonary function. Conventional
methods for lung ventilation imaging include gamma camera scintigraphy and positron
emission tomography scan after inhaling a gaseous radionuclide, as well as hyperpolarized
(HP) gas magnetic resonance imaging (MRI) using Helium-3 and Xenon-129 as imaging
contrast. Recently, a new method has been proposed in which deformable image registration
(DIR) is performed on a pair of anatomical lung images at different respiratory phases
to obtain the displacement vector field (DVF) between both phases, and generate a
lung ventilation map from the Jacobian Determinant of the DVF. This DIR-based method
is advantageous in its high image resolutions and simpler imaging procedures making
it a more feasible option for implementation into the clinical workflow. However,
current DIR-based lung ventilation methods have been largely hampered due to two major
deficiencies: 1) current DIR algorithms are morphologically based, lacking of sufficient
physiological realism and thus resulting in erroneous calculations of lung ventilation;
and 2) there is a lack of validation of the DIR-based lung ventilation calculation
against clinical ground truth, as well as large uncertainties and variations among
different DIR algorithms. The long-term goal of this proposal is to develop the necessary
tools and metrics for validation and testing of DIR-based lung ventilation mapping
techniques to contribute to their clinical implementation in advanced radiotherapy
of lung cancer and diagnosis of obstructive pulmonary diseases. The objective of the
proposed research is to develop digital thoracic phantoms from physiologically-plausible
lung motion models as a valuable tool for validation of DIR algorithms, and evaluate
deformation-based lung ventilation mapping techniques against reference HP gas MRI
ventilation images. The specific aims of this dissertation are the following. (1)
Develop digital thoracic phantoms based on physiological modeling of respiratory motion
from hyperpolarized gas tagging MRI. (2) Investigate the differences between HP gas
tagging-based, DIR-based, and HP MRI ventilation mapping methods. (3) Evaluate and
compare the sensitivity to deformation changes of ventilation and strain as lung functionality
metrics. </p><p>This study investigates a unique dataset which includes three types
of MR images acquired using a novel hybrid technique in a single breath-hold maneuver
including a HP Helium-3 ventilation image, a pair of proton MR images, and a pair
of HP Helium-3 tagging images at end of inhalation (EOI) and end of exhalation (EOE).</p><p>In
order to create a physiologically plausible lung motion model, we used the novel HP
gas tagging MRI technique. The tagged elements in the 3-dimensional (3D) tagged grid
pattern are essentially ~500 evenly distributed landmarks throughout the entire lung
area. Therefore, the displacement vector field calculated by tracking their motion
from the EOI to the EOE phases provided us with a true lung deformation model which
is physiologically-based. </p><p>The respiratory motion model was utilized to evaluate
DIR-based displacement vector fields. The mean absolute DVF differences were found
to be 8.2 mm for Subject 1, 7.5 mm for Subject 2, 5.6 mm for Subject 3, and 8.8 mm
for Subject 4. These results show that there can be significant differences in DVF
when performing a DIR compared to the respiratory motion models created from the tagged
elements’ displacement.</p><p>The thoracic motion model was then created through a
combination of the DIR-based DVF to model the deformation outside the lungs from the
registration of proton MR images, and the tagging-based DVF to model deformation inside
the lungs using the manually measured DVF from the tagging MR images.</p><p>The next
part of this dissertation focused on investigating a DIR- based lung ventilation mapping
technique using proton MR images by evaluating its correlation with hyperpolarized
Helium-3 gas ventilation MRI reference images which provide a ground-truth measure
of lung ventilation. Correlation between the reference ventilation images and ventilation
maps computed from HP gas tagging MRIs, which provide ground-truth lung deformation,
was also investigated. This is the first study, to our knowledge, to investigate three
types of ventilation maps that are all MR-based. Furthermore, all images/data used
in our evaluation are acquired during one same breath hold maneuver, eliminating the
uncertainties associated with reproducibility of the respiratory cycle, patient positioning,
and finding the spatial correspondence between the ventilation maps being evaluated.</p><p>The
results of the spatial comparison between the DIR-based and reference ventilation
images showed moderate to strong spearman correlation coefficients which are higher
than many previous ventilation evaluation studies in the literature This may also
be due to the fact that the images in this study were acquired during the same breath-hold
and therefore inherently co-registered. The tagging-based ventilation maps, which
are independent of the accuracy of any DIR algorithms, showed very similar spatial
correlations to the reference images compared to the DIR-based ventilation maps. This
proves the potential of the Jacobian ventilation calculation method which assumes
that local volume changes are an appropriate lung ventilation surrogate. As more RT
clinics incorporate MR imaging for patient simulation, and contouring for treatment
planning, this study shows the feasibility of utilizing MR images for DIR-based ventilation
calculations.</p><p>In the final part of this dissertation, we investigate lung strain
as an additional metric to assess respiratory mechanics. We evaluation the sensitivity
to deformation changes of both ventilation and strain as lung functionality metrics
by comparing both metrics’ sensitivity to changes in displacement vector fields using
Hyperpolarized He-3 Tagging MRI data. This study utilized physiologically-based respiratory
motion models from three subjects to assess the sensitivity of lung strain and lung
ventilation by introducing a number of modifications to the DVF, generating new lung
function maps, and investigating how much each of these lung function metrics were
affected. Lung strain was computed voxel-wise from the gradient of the tagging-based
displacement. Through this algorithm, we obtained a 3x3 tensor that directly measures
both the magnitude and direction of the lung deformation and then determines the three
principal strains. For the lung ventilation calculation, we used the previously described
voxel-by-voxel algorithm which was based on computing the Jacobian Determinant of
the tagging-based DVF to determine the local volume changes. These results show much
larger mean absolute percent differences between original and modified ventilation
maps compared to the principal strain maps for all tests performed in this study;
ranging from an average of 49.5 to 2743.7% for ventilation and 30.6 to 650.0% for
strain among the 3 subjects. The principal strain maps showed much smaller average
standard deviations between subjects. We found that Tagging-based ventilation maps
calculated through the Jacobian of the DVF might be more sensitive to deformation
changes compared to the lung strain maps, showing much larger mean absolute percent
differences between the original and modified maps. This could indicate two things,
while ventilation might be more sensitive to smaller deformation changes which could
be an advantage, this could also indicate that it is more sensitive to small errors
or uncertainties in the DVF which could make the calculation more unstable compared
to strain. </p><p>In conclusion, this dissertation utilized a unique hybrid MR image
acquisition method to present: the development of valuable physiologically-based respiratory
motion models and DIR validation tools using novel tagging MR images; the first all
MR-based evaluation of deformation-based ventilation mapping techniques, against reference
HP gas He-3 MR ventilation images with improved spatial correspondence; and the investigation
of lung strain as an additional metric to assess lung function.</p>
|
|
