Publications by authors named "Antonio Stanziola"

Transcranial ultrasound simulations are increasingly used to predict in situ exposure parameters for ultrasound therapies in the brain. However, there can be considerable uncertainty in estimating the acoustic medium properties of the skull and brain from computed tomography (CT) images. This paper shows how the resulting uncertainty in the simulated acoustic field can be predicted in a computationally efficient way using linear uncertainty propagation.

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A new method for solving the wave equation is presented, called the learned Born series (LBS), which is derived from a convergent Born series but its components are found through training. The LBS is shown to be significantly more accurate than the convergent Born series for the same number of iterations, in the presence of high contrast scatterers, while maintaining a comparable computational complexity. The LBS is able to generate a reasonable prediction of the global pressure field with a small number of iterations, and the errors decrease with the number of learned iterations.

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Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results.

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Model-based treatment planning for transcranial ultrasound therapy typically involves mapping the acoustic properties of the skull from an X-ray computed tomography (CT) image of the head. Here, three methods for generating pseudo-CT (pCT) images from magnetic resonance (MR) images were compared as an alternative to CT. A convolutional neural network (U-Net) was trained on paired MR-CT images to generate pCT T images from either T1-weighted or zero-echo time (ZTE) MR images (denoted tCT and zCT, respectively).

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Three-dimensional imaging is valuable to noninvasively assess angiogenesis given the complex 3-D architecture of vascular networks. The emergence of high frame rate (HFR) ultrasound, which can produce thousands of images per second, has inspired novel signal processing techniques and their applications in structural and functional imaging of blood vessels. Although highly sensitive vascular mapping has been demonstrated using ultrafast Doppler, the detectability of microvasculature from the background noise may be hindered by the low signal-to-noise ratio (SNR) particularly in the deeper region and without the use of contrast agents.

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The very high frame rate afforded by ultrafast ultrasound, combined with microbubble contrast agents, opens new opportunities for imaging tissue microvasculature. However, new imaging paradigms are required to obtain superior image quality from the large amount of acquired data while allowing real-time implementation. In this paper, we report a technique-acoustic sub-aperture processing (ASAP)-capable of generating very high contrast/signal-to-noise ratio (SNR) images of macro-and microvessels, with similar computational complexity to classical power Doppler (PD) imaging.

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We demonstrate a versatile phase-change sub-micron contrast agent providing three modes of contrast enhancement: 1) photoacoustic imaging contrast, 2) ultrasound contrast with optical activation, and 3) ultrasound contrast with acoustic activation. This agent, which we name 'Cy-droplet', has the following novel features. It comprises a highly volatile perfluorocarbon for easy versatile activation, and a near-infrared optically absorbing dye chosen to absorb light at a wavelength with good tissue penetration.

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Studies have reported that intraplaque neovascularisation (IPN) is closely correlated with plaque vulnerability. In this study, a new image processing approach, differential intensity projection (DIP), was developed to visualise and quantify IPN in contrast-enhanced non-linear ultrasound image sequences of carotid arteries. DIP used the difference between the local temporal maximum and the local temporal average signals to identify bubbles against tissue non-linear artefact and noise.

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