The conventional applicability of biomaterials in the field of bone tissue engineering takes into consideration several key parameters to achieve desired results for prospective translational use. Hence, several engineering strategies have been developed to model in the regenerative parameters of different forms of biomaterials, including bioactive glass and β-tricalcium phosphate. This review examines the different ways these two materials are transformed and assembled with other regenerative factors to improve their application for bone tissue engineering. We discuss the role of the engineering strategy used and the regenerative responses and mechanisms associated with them.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Assessment of trauma-induced hemorrhage with ultrasound is particularly challenging outside of the clinic, where its detection is crucial. The current clinical standard for hematoma detection – the focused assessment with sonography of trauma (FAST) exam – does not aim to detect ongoing blood loss, and thus is unable to detect injuries of increasing severity. To enhance detection of active bleeding, we propose the use of ultrasound contrast agents (UCAs), together with a novel flow phantom and contrast-sensitive processing techniques, to facilitate efficient, practical characterization of internal bleeding. Within a the custom phantom, UCAs and processing techniques enabled a significant enhancement of the hemorrhage visualization (mean increase in generalized contrast-to-noise ratio of 17 %) compared to the contrast-free case over a range of flow rates up to 40 ml/min. Moreover, we have shown that the use of UCAs improves the probability of detection: the area under the receiver operating characteristic curve for a flow rate of 40 ml/min was 0.99, compared to 0.72 without contrast. We also demonstrate how additional processing of the spatial and temporal information further localizes the bleeding site. UCAs also enhanced Doppler signals over the non-contrast case. These results show that specialized nonlinear processing (NLP) pipelines together with UCAs may offer an efficient means to improve substantially the detection of slower hemorrhages and increase survival rates for trauma-induced injury in pre-hospital settings.

Assessing the localization accuracy of electric and magnetic source imaging (ESI/MSI) for deep brain sources using a 3D-printed head phantom. We developed a realistic pediatric head phantom preserving brain, skull, and scalp properties with implanted sources in clinically relevant deep brain locations. Localization accuracy of ESI/MSI was assessed across varying noise levels using dipole fitting and dynamic statistical parametric mapping (dSPM). The phantom generated realistic MEG and EEG data resembling actual epilepsy patient recordings. MSI showed superior accuracy to ESI for the deep tangential insular source (dipole: ∼17 vs. ∼33 mm; dSPM: ∼24 vs. ∼32 mm). While ESI-ECD localized some radial sources well (e.g. ∼9 mm for brainstem), its dSPM struggled to localize deep sources (e.g. insula and amygdala). Both modalities found the radial thalamus source most challenging to localize. MSI outperformed ESI for localizing deep tangential sources; yet, both techniques struggled to localize deep radial sources. For point-like sources, dipole fitting delivered the highest accuracy (∼9 mm, ESI for brainstem), whereas averaged dSPM was superior for sources with distributed-source behavior (∼13 mm, MSI for orbital gyrus). 3D Printed realistic head phantoms can aid assessing the accuracy of ESI/MSI and selecting appropriate methods for different clinical scenarios.

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.