Abstract
Over the past years, volumetric cardiac imaging has matured to a modality that can be used in daily routine clinical practice. However, despite the evolution of volumetric ultrasound systems with remarkable improvement in image quality, spatio-temporal resolution of the 3D data set remains limited and inferior to what can be obtained in 2D. Further development of volumetric ultrasound is thus required. However, the development of new beam forming techniques for 3D ultrasound (US) imaging requires an open, flexible and fully programmable US platform. To date, such systems are scarce and required (custom-made) dedicated electronics. Therefore, the aim of this report is to present a novel High channel Density Programmable ULtrasound System based on consumer Electronics (HD-PULSE). © 2015 IEEE.

Abstract
Fast volumetric cardiac imaging requires to reduce the number of transmit events within a single volume. One way of achieving this is by limiting the field-of-view (FOV) of the recording to the anatomically relevant domain only (e.g. the myocardium when investigating cardiac mechanics). Although fully automatic solutions towards myocardial segmentation exist, translating that information in a fast ultrasound scan sequence is not trivial. The aim of this study was therefore to develop a methodology to automatically define the FOV from a volumetric dataset in the context of anatomical scanning. Hereto, a method is proposed where the anatomical relevant space is automatically identified as follows. First, the left ventricular myocardium is localized in the volumetric ultrasound recording using a fully automatic real-time segmentation framework (i.e. BEAS). Then, the extracted meshes are employed to define a binary mask identifying myocardial voxels only. Later, using these binary images, the percentage of pixels along a given image line that belong to the myocardium is calculated. Finally, a spatially continuous FOV that covers 'T' percentage of the myocardium is found by means of a ring-shaped template matching, giving as a result the opening angle and 'thickness' for a conical scan. This approach was tested on 27 volumetric ultrasound datasets, a T = 85% was used. The mean initial opening angle for a conical scan was of 19.67±8.53° while the mean 'thickness' of the cone was 19.01±3.35°. Therefore, a reduction of 48.99% in the number of transmit events was achieved, resulting in a frame rate gain factor of 1.96. As a conclusion, anatomical scanning in combination with new scanning sequences techniques can increase frame rate significantly while keeping information of the relevant structures for functional imaging. © 2015 IEEE.

Abstract
Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

Abstract
During social communication, words and sentences play a critical role in the expression of emotional meaning. The Minho Affective Sentences (MAS) were developed to respond to the lack of a standardized sentence battery with normative affective ratings: 192 neutral, positive, and negative declarative sentences were strictly controlled for psycholinguistic variables such as numbers of words and letters and per-million word frequency. The sentences were designed to represent examples of each of the five basic emotions (anger, sadness, disgust, fear, and happiness) and of neutral situations. These sentences were presented to 536 participants who rated the stimuli using both dimensional and categorical measures of emotions. Sex differences were also explored. Additionally, we probed how personality, empathy, and mood from a subset of 40 participants modulated the affective ratings. Our results confirmed that the MAS affective norms are valid measures to guide the selection of stimuli for experimental studies of emotion. The combination of dimensional and categorical ratings provided a more fine-grained characterization of the affective properties of the sentences. Moreover, the affective ratings of positive and negative sentences were not only modulated by participants' sex, but also by individual differences in empathy and mood state. Together, our results indicate that, in their quest to reveal the neurofunctional underpinnings of verbal emotional processing, researchers should consider not only the role of sex, but also of interindividual differences in empathy and mood states, in responses to the emotional meaning of sentences.

Abstract
In a dynamically changing social environment, humans have to face the challenge of prioritizing stimuli that compete for attention. In the context of social communication, the voice is the most important sound category. However, the existing studies do not directly address whether and how the salience of an unexpected vocal change in an auditory sequence influences the orientation of attention. In this study, frequent tones were interspersed with task-relevant infrequent tones and task-irrelevant infrequent vocal sounds (neutral, happy and angry vocalizations). Eighteen healthy college students were asked to count infrequent tones. A combined event-related potential (ERP) and EEG time-frequency approach was used, with the focus on the P3 component and on the early auditory evoked gamma band response, respectively. A spatial-temporal principal component analysis was used to disentangle potentially overlapping ERP components. Although no condition differences were observed in the 210-310 ms window, larger positive responses were observed for emotional than neutral vocalizations in the 310-410 ms window. Furthermore, the phase synchronization of the early auditory evoked gamma oscillation was enhanced for happy vocalizations. These findings support the idea that the brain prioritizes the processing of emotional stimuli, by devoting more attentional resources to salient social signals even when they are not task-relevant.

Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.