Ultrasound Transient Shear Wave Elasticity Imaging for Tendon Tissue

Degeneration of tendon tissue is a common cause of tendon dysfunction with the symptoms of repeated episodes of pain and palpable increase of tendon thickness. Tendon mechanical properties are directly related to its physiological composition and the structural organization of the interior collagen fibers which could be altered by tendon degeneration due to overuse or injury. Thus, measuring mechanical properties of tendon tissue may represent a quantitative measurement of pain, reduced function, and tissue health. Ultrasound elasticity imaging has been developed in the last two decades and has proved to be a promising tool for tissue elasticity imaging. To date, however, well established protocols of tendinopathy elasticity imaging for diagnosing tendon degeneration in early stages or late stages do not exist. This thesis describes the re-creation of one dynamic ultrasound elasticity imaging method and the development of an ultrasound transient shear wave elasticity imaging platform for tendon and other musculoskeletal tissue imaging. An experimental mechanical stage with proper supporting systems and accurate translating stages was designed and made. A variety of high-quality tissue-mimicking phantoms were made to simulate homogeneous and heterogeneous soft tissues as well as tendon tissues. A series of data acquisition and data processing programs were developed to collect the displacement data from the phantom and calculate the shear modulus and Young’s modulus of the target. The imaging platform was found to be capable of conducting comparative measurements of the elastic parameters of the phantoms and quantitatively mapping elasticity onto ultrasound B-Mode images. This suggests the system has great potential for not only benefiting individuals with tendinopathy with an earlier detection, intervention and better rehabilitation, but also for providing a medical tool for quantification of musculoskeletal tissue dysfunction in other regions of the body such as the shoulder, elbow and knee.

Model complexity affects transient population dynamics following a dispersal event: A case study with pea aphids

Stage-structured population models predict transient population dynamics if the population deviates from the stable stage distribution. Ecologists’ interest in transient dynamics is growing because populations regularly deviate from the stable stage distribution, which can lead to transient dynamics that differ significantly from the stable stage dynamics. Because the structure of a population matrix (i.e., the number of life-history stages) can influence the predicted scale of the deviation, we explored the effect of matrix size on predicted transient dynamics and the resulting amplification of population size. First, we experimentally measured the transition rates between the different life-history stages and the adult fecundity and survival of the aphid, Acythosiphon pisum. Second, we used these data to parameterize models with different numbers of stages. Third, we compared model predictions with empirically measured transient population growth following the introduction of a single adult aphid. We find that the models with the largest number of life-history stages predicted the largest transient population growth rates, but in all models there was a considerable discrepancy between predicted and empirically measured transient peaks and a dramatic underestimation of final population sizes. For instance, the mean population size after 20 days was 2394 aphids compared to the highest predicted population size of 531 aphids; the predicted asymptotic growth rate (λmax) was consistent with the experiments. Possible explanations for this discrepancy are discussed. Includes 4 supplemental files.