Investigation of Deformable Image Registration Based Lung Ventilation Mapping for Radiation Therapy Using a Hybrid Hyperpolarized Gas MRI Technique

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2020

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Abstract

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.

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).

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.

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.

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.

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.

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.

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.

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.

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Duarte, Isabella (2020). Investigation of Deformable Image Registration Based Lung Ventilation Mapping for Radiation Therapy Using a Hybrid Hyperpolarized Gas MRI Technique. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/21011.

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