In Vivo Time- Resolved Microtomography Reveals the Mechanics of the Blowfly Flight Motor

Dipteran flies are amongst the smallest and most agile of flying animals. Their wings are driven indirectly by large power muscles, which cause cyclical deformations of the thorax that are amplified through the intricate wing hinge. Asymmetric flight manoeuvres are controlled by 13 pairs of steering muscles acting directly on the wing articulations. Collectively the steering muscles account for <3% of total flight muscle mass, raising the question of how they can modulate the vastly greater output of the power muscles during manoeuvres. Here we present the results of a synchrotron-based study performing micrometre-resolution, time-resolved microtomography on the 145 Hz wingbeat of blowflies. These data represent the first four-dimensional visualizations of an organism's internal movements on sub-millisecond and micrometre scales. This technique allows us to visualize and measure the three-dimensional movements of five of the largest steering muscles, and to place these in the context of the deforming thoracic mechanism that the muscles actuate. Our visualizations show that the steering muscles operate through a diverse range of nonlinear mechanisms, revealing several unexpected features that could not have been identified using any other technique. The tendons of some steering muscles buckle on every wingbeat to accommodate high amplitude movements of the wing hinge. Other steering muscles absorb kinetic energy from an oscillating control linkage, which rotates at low wingbeat amplitude but translates at high wingbeat amplitude. Kinetic energy is distributed differently in these two modes of oscillation, which may play a role in asymmetric power management during flight control. Structural flexibility is known to be important to the aerodynamic efficiency of insect wings, and to the function of their indirect power muscles. We show that it is integral also to the operation of the steering muscles, and so to the functional flexibility of the insect flight motor

Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees

<p>Abstract</p> <p>Background</p> <p>The detailed interpretation of mass phenomena such as human escape panic or swarm behaviour in birds, fish and insects requires detailed analysis of the 3D movements of individual participants. Here, we describe the adaptation of a 3D stereoscopic imaging method to measure the positional coordinates of individual agents in densely packed clusters. The method was applied to study behavioural aspects of shimmering in Giant honeybees, a collective defence behaviour that deters predatory wasps by visual cues, whereby individual bees flip their abdomen upwards in a split second, producing Mexican wave-like patterns.</p> <p>Results</p> <p>Stereoscopic imaging provided non-invasive, automated, simultaneous, <it>in-situ </it>3D measurements of hundreds of bees on the nest surface regarding their thoracic position and orientation of the body length axis. <it>Segmentation </it>was the basis for the <it>stereo matching</it>, which defined correspondences of individual bees in pairs of stereo images. Stereo-matched "agent bees" were re-identified in subsequent frames by the <it>tracking </it>procedure and <it>triangulated </it>into real-world coordinates. These algorithms were required to calculate the three spatial motion components (dx: horizontal, dy: vertical and dz: towards and from the comb) of individual bees over time.</p> <p>Conclusions</p> <p>The method enables the assessment of the 3D positions of individual Giant honeybees, which is not possible with single-view cameras. The method can be applied to distinguish at the individual bee level active movements of the thoraces produced by abdominal flipping from passive motions generated by the moving bee curtain. The data provide evidence that the z-deflections of thoraces are potential cues for colony-intrinsic communication. The method helps to understand the phenomenon of collective decision-making through mechanoceptive synchronization and to associate shimmering with the principles of wave propagation. With further, minor modifications, the method could be used to study aspects of other mass phenomena that involve active and passive movements of individual agents in densely packed clusters.</p

Image databases in medical applications

The number of medical images acquired yearly in hospitals increases all the time. These imaging data contain lots of information on the characteristics of anatomical structures and on their variations. This information can be utilized in numerous medical applications. In deformable model-based segmentation and registration methods, the information in the image databases can be used to give a priori information on the shape of the object studied and the gray-level values in the image, and on their variations. On the other hand, by studying the variations of the object of interest in different populations, the effects of, for example, aging, gender, and diseases on anatomical structures can be detected. In the work described in this Thesis, methods that utilize image databases in medical applications were studied. Methods were developed and compared for deformable model-based segmentation and registration. Model selection procedure, mean models, and combination of classifiers were studied for the construction of a good a priori model. Statistical and probabilistic shape models were generated to constrain the deformations in segmentation and registration so that only the shapes typical to the object studied were accepted. In the shape analysis of the striatum, both volume and local shape changes were studied. The effects of aging and gender, and also the asymmetries were examined. The results proved that the segmentation and registration accuracy of deformable model-based methods can be improved by utilizing the information in image databases. The databases used were relatively small. Therefore, the statistical and probabilistic methods were not able to model all the population-specific variation. On the other hand, the simpler methods, the model selection procedure, mean models, and combination of classifiers, gave good results also with the small image databases. Two main applications were the reconstruction of 3-D geometry from incomplete data and the segmentation of heart ventricles and atria from short- and long-axis magnetic resonance images. In both applications, the methods studied provided promising results. The shape analysis of the striatum showed that the volume of the striatum decreases in aging. Also, the shape of the striatum changes locally. Asymmetries in the shape were found, too, but any gender-related local shape differences were not found.reviewe

Objective Assessment of Image Quality: Extension of Numerical Observer Models to Multidimensional Medical Imaging Studies

Encompassing with fields on engineering and medical image quality, this dissertation proposes a novel framework for diagnostic performance evaluation based on objective image-quality assessment, an important step in the development of new imaging devices, acquisitions, or image-processing techniques being used for clinicians and researchers. The objective of this dissertation is to develop computational modeling tools that allow comprehensive evaluation of task-based assessment including clinical interpretation of images regardless of image dimensionality. Because of advances in the development of medical imaging devices, several techniques have improved image quality where the format domain of the outcome images becomes multidimensional (e.g., 3D+time or 4D). To evaluate the performance of new imaging devices or to optimize various design parameters and algorithms, the quality measurement should be performed using an appropriate image-quality figure-of-merit (FOM). Classical FOM such as bias and variance, or mean-square error, have been broadly used in the past. Unfortunately, they do not reflect the fact that the average performance of the principal agent in medical decision-making is frequently a human observer, nor are they aware of the specific diagnostic task. The standard goal for image quality assessment is a task-based approach in which one evaluates human observer performance of a specified diagnostic task (e.g. detection of the presence of lesions). However, having a human observer performs the tasks is costly and time-consuming. To facilitate practical task-based assessment of image quality, a numerical observer is required as a surrogate for human observers. Previously, numerical observers for the detection task have been studied both in research and industry; however, little research effort has been devoted toward development of one utilized for multidimensional imaging studies (e.g., 4D). Limiting the numerical observer tools that accommodate all information embedded in a series of images, the performance assessment of a particular new technique that generates multidimensional data is complex and limited. Consequently, key questions remain unanswered about how much the image quality improved using these new multidimensional images on a specific clinical task. To address this gap, this dissertation proposes a new numerical-observer methodology to assess the improvement achieved from newly developed imaging technologies. This numerical observer approach can be generalized to exploit pertinent statistical information in multidimensional images and accurately predict the performance of a human observer over the complexity of the image domains. Part I of this dissertation aims to develop a numerical observer that accommodates multidimensional images to process correlated signal components and appropriately incorporate them into an absolute FOM. Part II of this dissertation aims to apply the model developed in Part I to selected clinical applications with multidimensional images including: 1) respiratory-gated positron emission tomography (PET) in lung cancer (3D+t), 2) kinetic parametric PET in head-and-neck cancer (3D+k), and 3) spectral computed tomography (CT) in atherosclerotic plaque (3D+e). The author compares the task-based performance of the proposed approach to that of conventional methods, evaluated based on a broadly-used signal-known-exactly /background-known-exactly paradigm, which is in the context of the specified properties of a target object (e.g., a lesion) on highly realistic and clinical backgrounds. A realistic target object is generated with specific properties and applied to a set of images to create pathological scenarios for the performance evaluation, e.g., lesions in the lungs or plaques in the artery. The regions of interest (ROIs) of the target objects are formed over an ensemble of data measurements under identical conditions and evaluated for the inclusion of useful information from different complex domains (i.e., 3D+t, 3D+k, 3D+e). This work provides an image-quality assessment metric with no dimensional limitation that could help substantially improve assessment of performance achieved from new developments in imaging that make use of high dimensional data

Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes

Pulmonary x-ray computed tomographic (CT) and magnetic resonance imaging (MRI) research and development has been motivated, in part, by the quest to subphenotype common chronic lung diseases such as chronic obstructive pulmonary disease (COPD). For thoracic CT and MRI, the main COPD research tools, disease biomarkers are being validated that go beyond anatomy and structure to include pulmonary functional measurements such as regional ventilation, perfusion, and inflammation. In addition, there has also been a drive to improve spatial and contrast resolution while at the same time reducing or eliminating radiation exposure. Therefore, this review focuses on our evolving understanding of patient-relevant and clinically important COPD endpoints and how current and emerging MRI and CT tools and measurements may be exploited for their identification, quantification, and utilization. Since reviews of the imaging physics of pulmonary CT and MRI and reviews of other COPD imaging methods were previously published and well-summarized, we focus on the current clinical challenges in COPD and the potential of newly emerging MR and CT imaging measurements to address them. Here we summarize MRI and CT imaging methods and their clinical translation for generating reproducible and sensitive measurements of COPD related to pulmonary ventilation and perfusion as well as parenchyma morphology. The key clinical problems in COPD provide an important framework in which pulmonary imaging needs to rapidly move in order to address the staggering burden, costs, as well as the mortality and morbidity associated with COPD

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