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Purpose: Micron-sized perfluorocarbon droplet adjuvants to focused ultrasound therapies allow lower applied power, circumvent unwanted prefocal heating, and enhance thermal dose in highly perfused tissues. The heat enhancement has been shown to saturate at increasing concentrations. Experiments were performed to empirically model the saturating heating effects during focused ultrasound. Materials and methods: The measurements were made at varying concentrations using magnetic resonance thermometry and focused ultrasound by circulating droplets of mean diameter 1.9 to 2.3 µm through a perfused phantom. A simulation was performed to estimate the interaction radius size, empirically. Results: The interaction radius, representing the radius of a sphere encompassing 90% of the probability for the transformation of acoustic energy into heat deposition around a single droplet, was determined experimentally from ultrasonic absorption coefficient measurements The simulations suggest the interaction radius was approximately 12.5-fold larger than the geometrical radius of droplets, corresponding to an interaction volume on the order of 2000 larger than the geometrical volume. Conclusions: The results provide information regarding the dose–response relationship from the droplets, a measure with 15% precision of their interaction radii with focused ultrasound, and subsequent insights into the underlying physical heating mechanism.

Additional file 1: Figure S1. Contour of the temporal lag (units: s) between sonication command and local extrema of temperature at the location of the focal point, as a function of cortical bone radius and focal point offset with respect to the center of breakthrough. Dotted lines are numerically calculated values and continuous lines are the fitted contours using the Eq. (12).

Visual behavior recognition is currently a highly active research area. This is due both to the scientific challenge posed by the complexity of the task, and to the growing interest in its applications, such as automated visual surveillance, human-computer interaction, medical diagnosis or video indexing/retrieval. A large number of different approaches have been developed, whose complexity and underlying models depend on the goals of the particular application which is targeted. The general trend followed by these approaches is the separation of the behavior recognition task into two sequential processes. The first one is a feature extraction process, where features which are considered relevant for the recognition task are extracted from the input image sequence. The second one is the actual recognition process, where the extracted features are classified in terms of the pre-defined behavior classes. One problematic issue of such a two-pass procedure is that the recognition process is highly dependent on the feature extraction process, and does not have the possibility to influence it. Consequently, a failure of the feature extraction process may impair correct recognition. The focus of our thesis is on the recognition of single object behavior from monocular image sequences. We propose a general framework where feature extraction and behavior recognition are performed jointly, thereby allowing the two tasks to mutually improve their results through collaboration and sharing of existing knowledge. The intended collaboration is achieved by introducing a probabilistic temporal model based on a Hidden Markov Model (HMM). In our formulation, behavior is decomposed into a sequence of simple actions and each action is associated with a different probability of observing a particular set of object attributes within the image at a given time. Moreover, our model includes a probabilistic formulation of attribute (feature) extraction in terms of image segmentation. Contrary to existing approaches, segmentation is achieved by taking into account the relative probabilities of each action, which are provided by the underlying HMM. In this context, we solve the joint problem of attribute extraction and behavior recognition by developing a variation of the Viterbi decoding algorithm, adapted to our model. Within the algorithm derivation, we translate the probabilistic attribute extraction formulation into a variational segmentation model. The proposed model is defined as a combination of typical image- and contour-dependent energy terms with a term which encapsulates prior information, offered by the collaborating recognition process. This prior information is introduced by means of a competition between multiple prior terms, corresponding to the different action classes which may have generated the current image. As a result of our algorithm, the recognized behavior is represented as a succession of action classes corresponding to the images in the given sequence. Furthermore, we develop an extension of our general framework, that allows us to deal with a common situation encountered in applications. Namely, we treat the case where behavior is specified in terms of a discrete set of behavior types, made up of different successions of actions, which belong to a shared set of action classes. Therefore, the recognition of behavior requires the estimation of the most probable behavior type and of the corresponding most probable succession of action classes which explains the observed image sequence. To this end, we modify our initial model and develop a corresponding Viterbi decoding algorithm. Both our initial framework and its extension are defined in general terms, involving several free parameters which can be chosen so as to obtain suitable implementations for the targeted applications. In this thesis, we demonstrate the viability of the proposed framework by developing particular implementations for two applications. Both applications belong to the field of gesture recognition and concern finger-counting and finger-spelling. For the finger-counting application, we use our original framework, whereas for the finger-spelling application, we use its proposed extension. For both applications, we instantiate the free parameters of the respective frameworks with particular models and quantities. Then, we explain the training of the obtained models from specific training data. Finally, we present the results obtained by testing our trained models on new image sequences. The test results show the robustness of our models in difficult cases, including noisy images, occlusions of the gesturing hand and cluttered background. For the finger-spelling application, a comparison with the traditional sequential approach to image segmentation and behavior recognition illustrates the superiority of our collaborative model.

Background High intensity focused ultrasound (HIFU) is clinically accepted for the treatment of solid tumors but remains challenging in highly perfused tissue due to the heat sink effect. Endovascular liquid-core sonosensitizers have been previously suggested to enhance the thermal energy deposition at the focal area and to lower the near-/far-field heating. We are investigating the therapeutic potential of PFOB-FTAC micro-droplets in a perfused tissue-mimicking model and postmortem excised organs. Method A custom-made in vitro perfused tissue-mimicking model, freshly excised pig kidneys (n = 3) and liver (n = 1) were perfused and subjected to focused ultrasound generated by an MR-compatible HIFU transducer. PFOB-FTAC sonosensitizers were injected in the perfusion fluid up to 0.235% v/v ratio. Targeting and on-line PRFS thermometry were performed on a 3 T MR scanner. Assessment of the fluid perfusion was performed with pulsed color Doppler in vitro and with dynamic contrast-enhanced (DCE)-MRI in excised organs. Results Our in vitro model of perfused tissue demonstrated re-usability. Sonosensitizer concentration and perfusion rate were tunable in situ. Differential heating under equivalent HIFU sonications demonstrated a dramatic improvement in the thermal deposition due to the sonosensitizers activity. Typically, the energy deposition was multiplied by a factor between 2.5 and 3 in perfused organs after the administration of micro-droplets, while DCE-MRI indicated an effective perfusion. Conclusion The current PFOB-FTAC micro-droplet sonosensitizers provided a large and sustained enhancement of the HIFU thermal deposition at the focal area, suggesting solutions for less technological constraints, lower risk for the near-/far- field heating. We also report a suitable experimental model for other MRgHIFU studies

Premature birth and the early exposure to environmental sensory information influence the cascade of histogenetic events that occur during the development of human telencephalon. The prenatal establishment of the genetically driven number and size of the elements that constitute the neuronal architecture of the telencephalon (e.g. number of axonal fibres and neurons) is the basis for the lifelong neuronal reorganisation (e.g. synapse pruning, dendrite growth, axonal retraction and myelinisation). Our hypothesis was driven by the experimental data and data from literature according to which the results of early alteration of histogenetic prenatal events can be seen in childhood too. Thus, we have tested the effect of the premature birth and birth weight on the regional size of cerebral cortex (cortical thickness and cortical surface) at school age.

The focus of this paper is on the recognition of single object behavior from monocular image sequences. The general literature trend is to perform behavior recognition separately after an initial phase of feature/attribute extraction. We propose a framework where behavior recognition is performed jointly with attribute extraction, allowing the two tasks to mutually improve their results. To this end, we express the joint recognition / extraction problem in terms of a probabilistic temporal model, allowing its resolution via a variation of the Viterbi decoding algorithm, adapted to our model. Within the algorithm derivation, we translate probabilistic attribute extraction into a variational segmentation scheme. We demonstrate the viability of the proposed framework through a particular implementation for finger-spelling recognition. The obtained results illustrate the superiority of our collaborative model with respect to the traditional approach, where attribute extraction and behavior recognition are performed sequentially.

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, the behavior inference process offers dynamic probabilistic priors to guide segmentation. At the same time, segmentation supplies its results to the inference process, ensuring that they are consistent both with prior knowledge and with new image information. The prior models are learned from training data and they adapt dynamically, based on newly analyzed images. We demonstrate the effectiveness of our framework via particular implementations that we have employed in the resolution of two hand gesture recognition applications. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior inference in challenging conditions involving complex backgrounds and occlusions of the target object.

ObjectivePrematurity is associated with a high risk of long-term behavioral problems. This study aimed to assess the prognostic utility of volumetric brain data at term-equivalent-age (TEA), clinical perinatal factors, and parental social economic risk in the prediction of the behavioral outcome at 5 years in a cohort of very preterm infants (VPT