Registration of pre-operative lung cancer PET/CT scans with post-operative histopathology images

Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice.Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

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

AUGMENTED REALITY AND INTRAOPERATIVE C-ARM CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED ROBOTIC SURGERY

Minimally-invasive robotic-assisted surgery is a rapidly-growing alternative to traditionally open and laparoscopic procedures; nevertheless, challenges remain. Standard of care derives surgical strategies from preoperative volumetric data (i.e., computed tomography (CT) and magnetic resonance (MR) images) that benefit from the ability of multiple modalities to delineate different anatomical boundaries. However, preoperative images may not reflect a possibly highly deformed perioperative setup or intraoperative deformation. Additionally, in current clinical practice, the correspondence of preoperative plans to the surgical scene is conducted as a mental exercise; thus, the accuracy of this practice is highly dependent on the surgeon’s experience and therefore subject to inconsistencies. In order to address these fundamental limitations in minimally-invasive robotic surgery, this dissertation combines a high-end robotic C-arm imaging system and a modern robotic surgical platform as an integrated intraoperative image-guided system. We performed deformable registration of preoperative plans to a perioperative cone-beam computed tomography (CBCT), acquired after the patient is positioned for intervention. From the registered surgical plans, we overlaid critical information onto the primary intraoperative visual source, the robotic endoscope, by using augmented reality. Guidance afforded by this system not only uses augmented reality to fuse virtual medical information, but also provides tool localization and other dynamic intraoperative updated behavior in order to present enhanced depth feedback and information to the surgeon. These techniques in guided robotic surgery required a streamlined approach to creating intuitive and effective human-machine interferences, especially in visualization. Our software design principles create an inherently information-driven modular architecture incorporating robotics and intraoperative imaging through augmented reality. The system's performance is evaluated using phantoms and preclinical in-vivo experiments for multiple applications, including transoral robotic surgery, robot-assisted thoracic interventions, and cocheostomy for cochlear implantation. The resulting functionality, proposed architecture, and implemented methodologies can be further generalized to other C-arm-based image guidance for additional extensions in robotic surgery

Development and in vitro characterization of three dimensional biodegradable scaffolds for peripheral nerve tissue engineering

Tissue engineering emerges nowadays to seek new solutions to damaged tissues and/or organs by replacing or repairing them with engineered constructs or scaffolds. In nerve tissue engineering, scaffolds for the repair of peripheral nerve injuries should act to support and promote axon growth following implantation. It is believed that substantial progress can be made by creating scaffolds from biomaterials, with growth-promoting molecules and spatially-controlled microstructure. To this end, this research aims to develop three dimensional (3D) scaffolds for peripheral nerve tissue regeneration by focusing on studies on the axon guidance, development and characterization of a novel 3D scaffold, and visualization of scaffolds by means of synchrotron-based diffraction enhanced imaging (DEI). Axon guidance is one of crucial considerations in developing of nerve scaffolds for nerve regeneration. In order to study the axon guidance mechanism, a two dimensional (2D) grid micropatterns were created by dispensing chitosan or laminin-blended chitosan substrate strands oriented in orthogonal directions; and then used in the in vitro dorsal root ganglion (DRG) neuron culture experiments. The results show the effect of the micropatterns on neurite directional growth can preferentially grow upon and follow the laminin-blended chitosan pathways. A novel 3D scaffold was developed for potential applications to peripheral nerve tissue engineering applications. The scaffolds were fabricated from poly L-lactide (PLLA) mixed with chitosan microspheres (CMs) by using a rapid freeze prototyping (RFP) technique, allowing for controllable scaffold microstructure and bioactivities protein release. The scaffold characterization shows that (1) the mechanical properties of the scaffolds depend on the ratio of CMs to PLLA as well as the cryogenic temperature and (2) the protein release can be controlled by adjusting the crosslink degree of the CMs and prolonged after the CMs were embedded into the PLLA scaffolds. Also, the degradation properties of the scaffolds were investigated with the results showing that the addition of CMs to PLLA can decrease the degradation rate as compared to pure PLLA scaffolds. This allows for another means to control the degradation rate. Visualization of polymer scaffolds in soft tissues is challenging, yet essential, to the success of tissue engineering applications. The x-ray diffraction enhanced imaging (DEI) method was explored for the visualization of the PLLA/CMs scaffolds embedded in soft tissues. Among various methods examined, including conventional radiography and in-line phase contrast imaging techniques, the DEI was the only technique able to visualize the scaffolds embedded in unstained muscle tissue as well as the microstructure of muscle tissue. Also, it has been shown that the DEI has the capacity to image the scaffolds in thicker tissue, and reduce the radiation doses to tissues as compared to conventional radiography. The methods and results developed/obtained in this study represent a substantial progress in the development and characterization of 3D scaffolds. This progress forms a basis for the future tests on the scaffolds as applied for peripheral nerve injuries

Synchrotron-based characterization of mechanobiological effects on the nanoscale in musculoskeletal tissues

Collagen is the main organic building block of musculoskeletal tissues. Despite collagen being their smallest load bearing unit, these tissues differ significantly in mechanical function and properties. A major factor behind these differences is their hierarchical organization, from the collagen molecule up to the organ scale. It is thus of high importance to understand the characteristics of each level, as well as how they interact and relate to each other. With such knowledge, improved prevention and rehabilitation of musculoskeletal pathologies may be achieved.Both mineralized and soft collagenous tissues respond to their mechanical loading environment according to specific mechanobiological principles. During prenatal development, immobilization can cause dramatic effects on the developing skeleton, causing the newly formed bones to be smaller, deformed and more prone to fracture. But how immobilization affects the deposition, structure and composition of the developing bones is still unclear. In tendons, both insufficient and excessive mechanical loading increases the risk of injury. After rupture, reduced mechanical loading results in altered collagen structure and cell activity, thus influencing the mechanical properties of the healing tendon. How the loading environment affects the structure of intact and ruptured tendons is still debated.The work presented in this thesis aims to thoroughly characterize the mechanobiological effects on the mineralization process in developing bones as well as the collagen structure and multiscale mechanical response of intact and healing tendons. This is achieved through a multimodal approach including a range of high-resolution synchrotron- and lab-based techniques, in combination with mechanical testing.In the first part of the thesis, humeri from “muscle-less” embryonic mice and their healthy littermates at development stages from start of mineralization to shortly before birth were investigated. The multimodal approach revealed a highly localized spatial pattern of Zinc during normal development to sites of ongoing mineralization, accompanied by larger mineral particles. Healthy bones also showed signs of remodeling at later time points. In the absence of skeletal muscle, it was revealed that the developing bones exhibited a delayed but increased mineral deposition and growth, with no signs of remodeling.In the second part of the thesis, intact Achilles tendons from rats subjected to either full in vivo loading through free cage activity or unloading by Botox injections combined with cast immobilization were investigated. It was shown that the nanoscale fibrils in the Achilles tendon respond to the applied tissue loads and exhibit viscoelastic responses. It was revealed that in vivo unloading results in a more disorganized microstructure and an impaired viscoelastic response. Unloading also altered the nanoscale fibril mechanical response, possibly through alterations in the strain partitioning between hierarchical levels.In the third part of the thesis, Achilles tendons were transected and allowed to heal while subjected to either full in vivo loading, reduced loading through Botox injections or unloading. In vivo unloading during the early healing process resulted in a delayed and more disorganized collagen structure and a larger presence of adipose tissue. Unloading also delayed the remodeling of the stumps as well as callus maturation. Additionally, the nanoscale fibril mechanical response was altered, with unloaded tendons exhibiting a low degree of fibril recruitment as well as a decreased ability for fibril extension.The work in this thesis further illustrates the important role of the mechanical environment on the nanostructure of musculoskeletal tissues. It also highlights the power of combining high-resolution tissue characterization techniques into a multimodal and multiscale approach, allowing us to study the effects on several hierarchical length scales simultaneously and as a result be able to elucidate the intricate connection between hierarchical scales

Accelerating functional gene discovery in osteoarthritis.

Osteoarthritis causes debilitating pain and disability, resulting in a considerable socioeconomic burden, yet no drugs are available that prevent disease onset or progression. Here, we develop, validate and use rapid-throughput imaging techniques to identify abnormal joint phenotypes in randomly selected mutant mice generated by the International Knockout Mouse Consortium. We identify 14 genes with functional involvement in osteoarthritis pathogenesis, including the homeobox gene Pitx1, and functionally characterize 6 candidate human osteoarthritis genes in mouse models. We demonstrate sensitivity of the methods by identifying age-related degenerative joint damage in wild-type mice. Finally, we phenotype previously generated mutant mice with an osteoarthritis-associated polymorphism in the Dio2 gene by CRISPR/Cas9 genome editing and demonstrate a protective role in disease onset with public health implications. We hope this expanding resource of mutant mice will accelerate functional gene discovery in osteoarthritis and offer drug discovery opportunities for this common, incapacitating chronic disease