Abstract
Regional contribution to left ventricular (LV) ejection is of much clinical importance but its assessment is notably challenging. While deformation imaging is often used, this does not take into account loading conditions. Recently, a method for intraventricular pressure estimation was proposed, thus allowing for loading conditions to be taken into account in a non-invasive way. In this work, a method for 3D automatic myocardial performance mapping in echocardiography is proposed by performing 3D myocardial segmentation and tracking, thus giving access to local geometry and strain. This is then used to assess local LV stress-strain relationships which can be seen as a measure of local myocardial work. The proposed method was validated against F-18-fluorodeoxyglucose positron emission tomography, the reference method to clinically assess local metabolism. Averaged over all patients, the mean correlation between FDG-PET and the proposed method was 0.67 +/- 0.18. In conclusion, stress-strain loops were, for the first time, estimated from 3D echocardiography and correlated to the clinical gold standard for local metabolism, showing the future potential of real-time 3D echocardiography ( RT3DE) for the assessment of local metabolic activity of the heart.

Abstract
Phase coherence methods have been proposed to improve the delay-and-sum (DAS) beamforming in terms of contrast and spatial resolution. However, they could be equally beneficial for speckle tracking echocadiography, given the higher variance they introduce in the speckle texture. The aim of this study was to compare, in a close-to-clinical scenario, the B-mode and speckle tracking performance of the DAS beamformer and 4 phase coherence methods: generalized coherence factor, phase coherence factor, sign coherence factor and short lag spatial coherence. Both simulation and experimental imaging of a tissue mimicking phantom were used to assess classical imaging metrics, whereas in-vivo imaging was performed to evaluate myocardial visibility and tissue tracking. Results showed improved resolution and contrast from the coherence beamformers, as well as a reduction of clutter noise, especially in the near field. Similarly, apical strain curves were more reliably estimated following coherence processing. Overall, these methods seem to better derive both morphological and functional imaging, although no method outperformed in all scenarios. © 2018 IEEE.

Abstract
Aims: To determine myocardial stiffness by means of measuring the velocity of naturally occurring myocardial shear waves (SWs) at mitral valve closure (MVC) and investigate their changes with myocardial remodelling in patients with hypertensive heart disease. Methods and results: Thirty-three treated arterial hypertension (HT) patients with hypertrophic left ventricular (LV) remodelling (59 ± 14 years, 55% male) and 26 aged matched healthy controls (55±15 years, 77% male) were included. HT patients were further divided into a concentric remodelling (HT1) group (13 patients) and a concentric hypertrophy (HT2) group (20 patients). LV parasternal long-axis views were acquired with an experimental ultrasound scanner at 1266 ± 317 frames per seconds. The SW velocity induced by MVC was measured from myocardial acceleration maps. SW velocities differed significantly between HT patients and controls (5.83 ± 1.20 m/s vs. 4.04 ± 0.96 m/s; P < 0.001). In addition, the HT2 group had the highest SW velocities (P < 0.001), whereas values between controls and the HT1 group were comparable (P = 0.075). Significant positive correlations were found between SW velocity and LV remodelling (interventricular septum thickness: r = 0.786, P < 0.001; LV mass index: r = 0.761, P < 0.001). SW velocity normalized for wall stress indicated that myocardial stiffness in the HT2 group was twice as high as in controls (P < 0.001), whereas values of the HT1 group overlapped with the controls (P = 1.00). Conclusions: SW velocity as measure of myocardial stiffness is higher in HT patients compared with healthy controls, particularly in advanced hypertensive heart disease. Patients with concentric remodelling have still normal myocardial properties whereas patients with concentric hypertrophy show significant stiffening.

Abstract
The lung cancer diagnosis is based on the search of lung nodules. Besides its characterization, it is also common to register the anatomical position of these findings. Even though computed-aided diagnosis systems tend to help in these tasks, there is still lacking a complete system that can qualitatively label the nodules in lung regions. In this way, this paper proposes an automatic lung reference model to facilitate the report of nodules between computed-aided diagnosis systems and the radiologist, and among radiologists. The model was applied to 115 computed tomography scans with manually and automatically segmented lobes, and the obtained sectors' variability was evaluated. As the sectors average variability within lobes is less or equal to 0.14, the model can be a good way to promote the report of lung nodules.

Abstract
Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, searching lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer-aided detection systems can be used as second observers, they may bias radiologists and introduce significant time overheads. With this in mind, this study assesses the potential of using gaze information for integrating automatic detection systems in the clinical practice. For that purpose, 4 radiologists were asked to annotate 20 scans from a public dataset while being monitored by an eye tracker device, and an automatic lung nodule detection system was developed. Our results show that radiologists follow a similar search routine and tend to have lower fixation periods in regions where finding errors occur. The overall detection sensitivity of the specialists was 0.67 +/- 0.07, whereas the system achieved 0.69. Combining the annotations of one radiologist with the automatic system significantly improves the detection performance to similar levels of two annotators. Filtering automatic detection candidates only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening.