Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

Agent-Based Modelling of Radiation-Induced Lung Injuries

Radiotherapy (RT), which nowadays is integrated in more than 50% of the therapies of new cancer patients, involves the use of ionizing radiation (such as photon beams and ions) as a tool to sterilize cancers. However, the lethal doses to be delivered to the tumours are limited by normal tissue complications. Consequently, constraints must be set on the radiation dose and irradiated volume in order to maintain acceptable toxicity levels. An important role in this context is played by computational models that ultimately provide valuable insights useful for tuning the RT parameters. Their use in the biomedical framework has a well-defined pattern: a theoretical model is initially built on the basis of the available in-vitro and/or in-vivo data and implemented in-silico; the model is then altered until a good match between its output and laboratory data is observed and finally used for predictions in the clinical setting. As yet, however, the tolerance doses for the organs at risk are derived from clinical experience and used as inputs for phenomenological Normal-Tissue Complication Probability (NTCP) models that lack a mechanistic description of the underlying phenomena. This thesis describes the implementation of an Agent-Based Model (ABM) that simulates the onset of Radiation-Induced Lung Injuries (RILI) (namely pneumonitis and fibrosis), complications that can occur in the lungs of patients irradiated in the thoracic region. Although relatively common, the risk factors and progression of the RILI, which eventually lead to respiratory failure and death, haven’t been fully elucidated. Here, the capability of the innovative AB modelling approach to improve patient-specific NTCP estimates while attempting to provide insights on the development of RILI is investigated. With respect to the existing dose-volume histogram-based and tissue-architecture approaches, ABMs can take into account not only the patient-specific geometry and tissue-level parameters, but also spatial information on the dose distribution. As a first step, a 3D model of idiopathic pulmonary fibrosis, which resembles the Radiation-Induced Lung Fibrosis (RILF), was implemented using BioDynaMo, an AB simulation framework. The model, whose agents simulate a partial pulmonary acinus, can replicate previous experimental results and assess the appropriateness of the approach for the purpose. The model was subsequently rescaled to represent an alveolar segment at the cell scale that can be damaged locally by external sources. As a surrogate measure of the RILF severity, the RILF Severity Index (RSI) was introduced, derived by combining the loss in the alveolar volume with the increase in the average concentration of the ExtraCellular Matrix (ECM). The RSI showed qualitative agreement with a similar index obtained using data from computational tomographies and the ECM patterns matched clinical findings. Finally, a pipeline was established that links TOPAS-nBio, a particle transport simulator for biological applications, with BioDynaMo. The alveolar segment structure was rebuilt using TOPAS-nBio and the delivery of realistic dose distributions at the cell scale was simulated. The output was then used as an input for the AB model and the effect of different fractionation schemes and radiation qualities on the outcome explored. In accordance with previous studies, a 5-fractions treatment resulted in a lower RSI with respect to the delivery of the same dose in a single fraction and an increased sensitivity to peaked protons dose distributions with respect to flatter ones from photons irradiation was observed. Overall, the results presented in this thesis prove the capability of the AB models to recapitulate some main radiobiological processes and advise for their potential complementary role in NTCP estimates

Biological imaging for adaptive bladder radiotherapy

Outcomes for muscle-invasive bladder cancer (MIBC) have changed little over recent decades, with long term survival remaining around 50 % [1]. Standard radiotherapy treatment for MIBC involves resection of tumour followed by uniform radiotherapy to the whole bladder, as the residual tumour is not readily visible on conventional computed tomography (CT) imaging. This thesis investigates the use of diffusion-weighted MRI (DW-MRI) to enable a dose-escalated radiotherapy treatment, aiming to improve local control of MIBC. Geometrical distortion in DW-MRI was investigated via bladder-mimicking phantoms, and positional differences were quantified between DW-MRI, standard T2-weighted MRI, and CT. Deformable registration within a commercial radiotherapy treatment planning system was tested to see whether it could mitigate distortion. All markers were located within a maximum discrepancy of 5 mm (mean 3 mm). Open-source software designed to correct geometric distortion in DW-MRI was tested and produced improved results with a maximum of 1.8 mm (mean 1.3 mm). Tumours were simulated in 6 locations within the bladder on a CT dataset of a previously-treated MIBC patient. Expansions producing planning target volumes incorporated the findings from the phantom investigations, aiming to mimic the use of DW-MRI registered to CT. Escalated dose distributions were compared against the standard, using established dose-constraints for nearby sensitive organs. Maximum dose-escalations to 70 - 78 Gy were possible depending on tumour location. Poisson-based tumour control probability (TCP) models were fitted to MIBC trials data for radiotherapy-only and radio-chemotherapy treatments, and used to calculate TCP for all dose distributions. TCP increased 9.0 - 19.2 % depending on tumour location and model used. The feasibility of delivery of the dose distributions was assessed via dose-accumulation using cone beam CT. This work showed that the use of DW-MRI for planning and pre-treatment imaging of MIBC patients could isotoxically improve local control of MIBC

Improving Image Reconstruction for Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has been developed to reduce the issue of overlapping tissue in conventional 2-D mammography for breast cancer screening and diagnosis. In the DBT procedure, the patient’s breast is compressed with a paddle and a sequence of x-ray projections is taken within a small angular range. Tomographic reconstruction algorithms are then applied to these projections, generating tomosynthesized image slices of the breast, such that radiologists can read the breast slice by slice. Studies have shown that DBT can reduce both false-negative diagnoses of breast cancer and false-positive recalls compared to mammography alone. This dissertation focuses on improving image quality for DBT reconstruction. Chapter I briefly introduces the concept of DBT and the inspiration of my study. Chapter II covers the background of my research including the concept of image reconstruction, the geometry of our experimental DBT system and figures of merit for image quality. Chapter III introduces our study of the segmented separable footprint (SG) projector. By taking into account the finite size of detector element, the SG projector improves the accuracy of forward projections in iterative image reconstruction. Due to the more efficient access to memory, the SG projector is also faster than the traditional ray-tracing (RT) projector. We applied the SG projector to regular and subpixel reconstructions and demonstrated its effectiveness. Chapter IV introduces a new DBT reconstruction method with detector blur and correlated noise modeling, called the SQS-DBCN algorithm. The SQS-DBCN algorithm is able to significantly enhance microcalcifications (MC) in DBT while preserving the appearance of the soft tissue and mass margin. Comparisons between the SQS-DBCN algorithm and several modified versions of the SQS-DBCN algorithm indicate the importance of modeling different components of the system physics at the same time. Chapter V investigates truncated projection artifact (TPA) removal algorithms. Among the three algorithms we proposed, the pre-reconstruction-based projection view (PV) extrapolation method provides the best performance. Possible improvements of the other two TPA removal algorithms have been discussed. Chapter VI of this dissertation examines the effect of source blur on DBT reconstruction. Our analytical calculation demonstrates that the point spread function (PSF) of source blur is highly shift-variant. We used CatSim to simulate digital phantoms. Analysis on the reconstructed images demonstrates that a typical finite-sized focal spot (~ 0.3 mm) will not affect the image quality if the x-ray tube is stationary during the data acquisition. For DBT systems with continuous-motion data acquisition, the motion of the x-ray tube is the main cause of the effective source blur and will cause loss in the contrast of objects. Therefore modeling the source blur for these DBT systems could potentially improve the reconstructed image quality. The final chapter of this dissertation discusses a few future studies that are inspired by my PhD research.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144059/1/jiabei_1.pd