Two-dimensional and doppler echocardiographic evaluation in twenty-one healthy Python regius.

Echocardiographic evaluation is a diagnostic tool for the in vivo diagnosis of heart diseases. Specific and unique anatomical characteristics of the ophidian heart such as the single ventricular cavity, a tubular sinus venosus opening into the right atrium, the presence of three arterial trunks and extreme mobility in the coelomic cavity during the cardiac cycle directly affect echocardiographic examination. Twenty-one awake, healthy ball pythons (Python regius) were analysed based on guidelines for performing echocardiographic examinations.

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model.

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability.

A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology.

Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric approaches (M, M), combining mutual information (MI) and normalized gradient field filter (NGF).

T2w-MRI signal normalization affects radiomics features reproducibility.

Purpose: Despite its increasing application, radiomics has not yet demonstrated a solid reliability, due to the difficulty in replicating analyses. The extraction of radiomic features from clinical MRI (T1w/T2w) presents even more challenges because of the absence of well-defined units (e.g.

Representative 3D shape of the distal femur, modes of variation and relationship with abnormality of the trochlear region.

The anatomy of the distal femur has a predominant influence on the mechanics of both patello- and tibio-femoral joints. Especially, the morphological degeneration of the trochlear region dramatically affects the overall knee biomechanics and, from a clinical point of view, the staging of such a degeneration is fundamental to tailor the optimal therapeutic solution. The description of morphological variability and pathological inter-subject differences of the trochlea can be achieved by means of statistical shape modeling of a set of three-dimensional surfaces.

Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology.

Statistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities.

Model-Supported Radiotherapy Personalization: Test of Hyper- and Hypo-Fractionation Effects.

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study.

Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia.

Background: The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella-femoral joint. In this paper, we integrated statistical shape models (SSM), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA) networks, which exploit the parameters to discriminate among different levels of abnormalities.

Methods: Two datasets of distal femur reconstructions were obtained from CT scans, including pathologic and physiologic shapes.

Comparison between model-predicted tumor oxygenation dynamics and vascular-/flow-related Doppler indices.

Purpose: Mathematical modeling is a powerful and flexible method to investigate complex phenomena. It discloses the possibility of reproducing expensive as well as invasive experiments in a safe environment with limited costs. This makes it suitable to mimic tumor evolution and response to radiotherapy although the reliability of the results remains an issue.

Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats.

Purpose: Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g.

Mathematical modeling of tumor response to radiation: radio-sensitivity correlation with BOLD, TOLD, ΔR1 and ΔR2* investigated in large Dunning R3327-AT1 rat prostate tumors.

Tumor response to radiation therapy can vary highly across patients. Several factors, both tumor- and environment-specific, can influence the radio-sensitivity, one of the most well-known being hypoxia. In this work, we investigated possible correlations between the radio-sensitivity parameters determined by means of a simple mathematical model of tumor volume evolution, and the MRI-based indicators of oxygenation in Dunning R3327-AT1 rats.

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging.

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy).

Modeling the Interplay Between Tumor Volume Regression and Oxygenation in Uterine Cervical Cancer During Radiotherapy Treatment.

This paper describes a patient-specific mathematical model to predict the evolution of uterine cervical tumors at a macroscopic scale, during fractionated external radiotherapy. The model provides estimates of tumor regrowth and dead-cell reabsorption, incorporating the interplay between tumor regression rate and radiosensitivity, as a function of the tumor oxygenation level. Model parameters were estimated by minimizing the difference between predicted and measured tumor volumes, these latter being obtained from a set of 154 serial cone-beam computed tomography scans acquired on 16 patients along the course of the therapy.