A Free-Breathing Lung Motion Model

Lung cancer has been the leading cause of cancer deaths for decades in the United States. Although radiotherapy is one of the most effective treatments, side effects from error in delivery of radiation due to organ motion during breathing remain a significant issue. To compensate the breathing motion during the treatment, a free breathing lung motion model, x= x0+αv+βf, was developed and discussed, where x is the position of a piece of tissue located at reference position x0. α is a parameter which characterizes the motion due to local air filling: motion as a function of tidal volume) and β is the parameter that accounts for the motion due to the imbalance of dynamical stress distributions during inspiration and exhalation which cause lung motion hysteresis: motion as a function of airflow). The parameters α and β together provide a quantitative characterization of breathing motion that inherently includes the complex hysteresis interplay. The theoretical foundation of the model was built by investigating the stress distribution inside of a lung and the biomechanical properties of the lung tissues. Accuracy of the model was investigated by using 49 free-breathing patient data sets. Applications of the model in localizing lung cancer, monitoring radiation damage and suppressing artifacts in free-breathing PET images were also discussed. This work supported in part by NIHR01CA096679 and NIHR01CA11671

3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT

Inverse-Consistent Determination of Young\u27s Modulus of Human Lung

Human lung undergoes respiration-induced deformation due to sequential inhalation and exhalation. Accurate determination of lung deformation is crucial for tumor localization and targeted radiotherapy in patients with lung cancer. Numerical modeling of human lung dynamics based on underlying physics and physiology enables simulation and virtual visualization of lung deformation. Dynamical modeling is numerically complicated by the lack of information on lung elastic behavior, structural heterogeneity as well as boundary constrains. This study integrates physics-based modeling and image-based data acquisition to develop the patient-specific biomechanical model and consequently establish the first consistent Young\u27s modulus (YM) of human lung. This dissertation has four major components: (i) develop biomechanical model for computation of the flow and deformation characteristics that can utilize subject-specific, spatially-dependent lung material property; (ii) develop a fusion algorithm to integrate deformation results from a deformable image registration (DIR) and physics-based modeling using the theory of Tikhonov regularization; (iii) utilize fusion algorithm to establish unique and consistent patient specific Young\u27s modulus and; (iv) validate biomechanical model utilizing established patient-specific elastic property with imaging data. The simulation is performed on three dimensional lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of human subjects. The heterogeneous Young\u27s modulus is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The biomechanical model adequately predicts the spatio-temporal lung deformation, consistent with data obtained from imaging. The accuracy of the numerical solution is enhanced through fusion with the imaging data beyond the classical comparison of the two sets of data. Finally, the fused displacement results are used to establish unique and consistent patient-specific elastic property of the lung

A robust immersed boundary method for flow in complex geometries: study of aerosol deposition in the human extrathoracic airways

The flow and the transport of particles in the human respiratory system dictate the effectiveness of therapeutic aerosols used in inhaled drug delivery. The aerosol particles are generally inhaled through the mouth, passing by the throat before reaching the targeted areas in the lungs. Therefore, knowledge of the particle deposition in the mouth-throat region is critical in the design of effective inhalation devices for optimum delivery to the lungs. Numerical simulations offer a non-invasive and cost-effective alternative to in vivo and in vitro tests. However, accurate prediction remains a challenge for numerical models due to the complexity of the flow in the extrathoracic airways. A robust immersed boundary method for flow in complex geometries is proposed. This greatly simplifies the task of grid generation and eliminates the problems associated with grid quality that exist for boundary-fitted grid techniques. The proposed method is an extension to the momentum forcing approach onto curvilinear coordinates and applies an iterative procedure to compute the forcing term implicitly, which stabilizes the scheme for higher Reynolds numbers. The use of a curvilinear grid minimizes the number of unused cells outside the geometry and increases the efficiency of the numerical scheme. The method is validated against numerical and experimental data in the literature for a number of test cases on both Cartesian and curvilinear grids. The results show good agreement with previous studies. Direct numerical simulations were performed in a number of realistic mouth and throat geometries obtained from MRI scans. A Lagrangian particle tracking scheme was employed to advance the particles dynamically, and total and regional deposition efficiencies were determined and compared to in vitro data. The effect of inflow turbulence and intersubject variation on deposition was studied. Geometric variation has a large impact on total deposition whereas the effect of inflow turbulence is confined to oral deposition

Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung

In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that would otherwise be computationally prohibitive without leveraging multi-resolution techniques. Specifically, the first part of this work proposes a classification framework that is being developed in collaboration with NorthEast Structural Genomics Consoritum (NESG) to assist in the automated screening of protein crystal images. Several groups have previously proposed automated algorithms to expedite such analysis. However, none of the classifiers described in the literature are sufficiently accurate or fast enough to be practical in a structural genomics production pipeline. The second part of this work proposes a 3D image registration algorithm to register regions of emphysema as quantified by densitometry on lung CT with MR lung volumes. The ability to register quantitatively-determined regions of emphysema with perfusion MRI will allow for further exploration of the pathophysiology of Chronic Obstructive Pulmonary Disorder (COPD). The registration method involves the registration of CT volumes at different levels of inspiration (total lung capacity to functional residual capacity [FRC]) followed by another registration between FRC-CT and FRC-MR volume pairs

Energy failure following traumatic brain injury: Potential mechanisms and impact of normobaric hyperoxia

Cerebral ischaemia is a frequent finding in post mortem studies following traumatic brain injury (TBI), but clinical studies using 15oxygen positron emission tomography (15O PET) suggest that classical ischaemia is uncommon beyond the first 24 hours after injury. Evidence of metabolic failure in the absence of classical ischaemia may represent ongoing neuronal dysfunction and progressive neuronal loss. Any therapeutic intervention that mitigates such metabolic derangements before they result in irreversible neuronal injury may improve tissue fate and improve the functional outcome for patients. Energy failure was spatially defined, characterised, and mapped using 15O and 18Fluoromisinidazole ([18F] FMISO) positron emission tomography. This enabled differentiation of classical ischaemia, diffusion hypoxia, and established infarction, and provided data on the dominant local mechanism at any given time after TBI. My thesis also aimed to examine the utility of diffusion tensor imaging and whole-brain proton MR spectroscopy (WB 1H MRS) as imaging biomarkers to investigate normobaric hyperoxia as a therapeutic option following traumatic brain injury (TBI). Using ([18F] FMISO PET evidence of tissue hypoxia consistent with microvascular ischaemia was found across the injured brain. The impact of normobaric hyperoxia (NBH) was examined in a clinical TBI cohort using diffusion tensor imaging and WB 1H MRS. Some evidence of benefit was found within the perilesional brain, but further studies should examine the value of a longer period of exposure to NBH and whether this has implications for functional outcome.AAGBI, MRC, Wellcome trus

Regional aerosol deposition in the human airways: the SimInhale benchmark case and a critical assessment of in silico methods

Regional deposition effects are important in the pulmonary delivery of drugs intended for the topical treatment of respiratory ailments. They also play a critical role in the systemic delivery of drugs with limited lung bioavailability. In recent years, significant improvements in the quality of pulmonary imaging have taken place, however the resolution of current imaging modalities remains inadequate for quantifying regional deposition. Computational Fluid-Particle Dynamics (CFPD) can fill this gap by providing detailed information about regional deposition in the extrathoracic and conducting airways. It is therefore not surprising that the last 15 years have seen an exponential growth in the application of CFPD methods in this area. Survey of the recent literature however, reveals a wide variability in the range of modelling approaches used and in the assumptions made about important physical processes taking place during aerosol inhalation. The purpose of this work is to provide a concise critical review of the computational approaches used to date, and to present a benchmark case for validation of future studies in the upper airways. In the spirit of providing the wider community with a reference for quality assurance of CFPD studies, in vitro deposition measurements have been conducted in a human-based model of the upper airways, and several groups within MP1404 SimInhale have computed the same case using a variety of simulation and discretization approaches. Here, we report the results of this collaborative effort and provide a critical discussion of the performance of the various simulation methods. The benchmark case, in vitro deposition data and in silico results will be published online and made available to the wider community. Particle image velocimetry measurements of the flow, as well as additional numerical results from the community, will be appended to the online database as they become available in the future