IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts

Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach

Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature renormalization approaches

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Towards an Accurate Tracking of Liver Tumors for Augmented Reality in Robotic Assisted Surgery

International audienceThis article introduces a method for tracking the internal structures of the liver during robot-assisted procedures. Vascular network, tumors and cut planes, computed from pre-operative data, can be overlaid onto the laparoscopic view for image-guidance, even in the case of large motion or deformation of the organ. Compared to current methods, our method is able to precisely propagate surface motion to the internal structures. This is made possible by relying on a fast yet accurate biomechanical model of the liver combined with a robust visual tracking approach designed to properly constrain the model. Augmentation results are demonstrated on in-vivo sequences of a human liver during robotic surgery, while quantitative validation is performed on an ex-vivo porcine liver experimentation. Validation results show that our approach gives an accurate surface registration with an error of less than 6mm on the position of the tumor

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

A Heterogeneous Patient-Specific Biomechanical Model of the Lung for Tumor Motion Compensation and Effective Lung Radiation Therapy Planning

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies

Spatio-Temporal Modeling Of Anatomic Motion For Radiation Therapy

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration

Heterogeneous volumetric data mapping and its medical applications

With the advance of data acquisition techniques, massive solid geometries are being collected routinely in scientific tasks, these complex and unstructured data need to be effectively correlated for various processing and analysis. Volumetric mapping solves bijective low-distortion correspondence between/among 3D geometric data, and can serve as an important preprocessing step in many tasks in compute-aided design and analysis, industrial manufacturing, medical image analysis, to name a few. This dissertation studied two important volumetric mapping problems: the mapping of heterogeneous volumes (with nonuniform inner structures/layers) and the mapping of sequential dynamic volumes. To effectively handle heterogeneous volumes, first, we studied the feature-aligned harmonic volumetric mapping. Compared to previous harmonic mapping, it supports the point, curve, and iso-surface alignment, which are important low-dimensional structures in heterogeneous volumetric data. Second, we proposed a biharmonic model for volumetric mapping. Unlike the conventional harmonic volumetric mapping that only supports positional continuity on the boundary, this new model allows us to have higher order continuity $C^1$ along the boundary surface. This suggests a potential model to solve the volumetric mapping of complex and big geometries through divide-and-conquer. We also studied the medical applications of our volumetric mapping in lung tumor respiratory motion modeling. We were building an effective digital platform for lung tumor radiotherapy based on effective volumetric CT/MRI image matching and analysis. We developed and integrated in this platform a set of geometric/image processing techniques including advanced image segmentation, finite element meshing, volumetric registration and interpolation. The lung organ/tumor and surrounding tissues are treated as a heterogeneous region and a dynamic 4D registration framework is developed for lung tumor motion modeling and tracking. Compared to the previous 3D pairwise registration, our new 4D parameterization model leads to a significantly improved registration accuracy. The constructed deforming model can hence approximate the deformation of the tissues and tumor

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group