Abbottthomassen3903

Pulse wave imaging (PWI) is an ultrasound imaging modality that estimates the wall stiffness of an imaged arterial segment by tracking the pulse wave propagation. The aim of the present study is to integrate PWI with vector flow imaging, enabling simultaneous and co-localized mapping of vessel wall mechanical properties and 2-D flow patterns. Two vector flow imaging techniques were implemented using the PWI acquisition sequence 1) multiangle vector Doppler and 2) a cross-correlation-based vector flow imaging (CC VFI) method. The two vector flow imaging techniques were evaluated in vitro using a vessel phantom with an embedded plaque, along with spatially registered fluid structure interaction (FSI) simulations with the same geometry and inlet flow as the phantom setup. The flow magnitude and vector direction obtained through simulations and phantom experiments were compared in a prestenotic and stenotic segment of the phantom and at five different time frames. In most comparisons, CC VFI provided significantly lower bias or precision than the vector Doppler method ( ) indicating better performance. In addition, the proposed technique was applied to the carotid arteries of nonatherosclerotic subjects of different ages to investigate the relationship between PWI-derived compliance of the arterial wall and flow velocity in vivo. Spearman's rank-order test revealed positive correlation between compliance and peak flow velocity magnitude ( rs = 0.90 and ), while significantly lower compliance ( ) and lower peak flow velocity magnitude ( ) were determined in older (54-73 y.o.) compared with young (24-32 y.o.) subjects. Finally, initial feasibility was shown in an atherosclerotic common carotid artery in vivo. The proposed imaging modality successfully provided information on blood flow patterns and arterial wall stiffness and is expected to provide additional insight in studying carotid artery biomechanics, as well as aid in carotid artery disease diagnosis and monitoring.Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object properties from training images. However, an analysis of the prior information learned by these deep networks and their ability to generalize to data that may lie outside the training distribution is still being explored. An inaccurate prior might lead to false structures being hallucinated in the reconstructed image and that is a cause for serious concern in medical imaging. In this work, we propose to illustrate the effect of the prior imposed by a reconstruction method by decomposing the image estimate into generalized measurement and null components. The concept of a hallucination map is introduced for the general purpose of understanding the effect of the prior in regularized reconstruction methods. Numerical studies are conducted corresponding to a stylized tomographic imaging modality. The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.X-ray computed tomography (CT) uses different filter kernels to highlight different structures. Since the raw sinogram data is usually removed after the reconstruction, in case there are additional need for other types of kernel images that were not previously generated, the patient may need to be scanned again. Accordingly, there exists increasing demand for post-hoc image domain conversion from one kernel to another without sacrificing the image quality. In this paper, we propose a novel unsupervised continuous kernel conversion method using cycle-consistent generative adversarial network (cycleGAN) with adaptive instance normalization (AdaIN). Even without paired training data, not only can our network translate the images between two different kernels, but it can also convert images along the interpolation path between the two kernel domains. We also show that the quality of generated images can be further improved if intermediate kernel domain images are available. Experimental results confirm that our method not only enables accurate kernel conversion that is comparable to supervised learning methods, but also generates intermediate kernel images in the unseen domain that are useful for hypopharyngeal cancer diagnosis.

To implement a skull-conformal phased array for ultrasound-guided transcranial focused ultrasound therapy with improved patient comfort.

Using patient-specific computed tomography and MRI neuroimaging data, tightly-conforming helmet scaffolds were designed computationally. Bucladesine The helmet scaffolds were designed to hold reusable transducer modules at near-normal incidence in an optimal configuration for the treatment location(s) of interest. Numerical simulations of trans-skull ultrasound propagation were performed to evaluate different conformal array designs and to compare with hemispherical arrays similar to those employed clinically. A 4096-element phased array was constructed by 3D printing a helmet scaffold optimised for an ex vivo human skullcap, and its performance was evaluated via benchtop and in vivo experiments.

Acoustic field measurements confirmed the system's ability to focus through human skull bone using simulation-based transcranial aberration corrections. Preliminary in vivo testing demonstrated safe trans-human skull blood-brain barrier (BBB) opening in rodents.

Patient-specific conformal ultrasound phased arrays appear to be a feasible and safe approach for conducting transcranial BBB opening procedures.

Skull-conformal phased arrays stand to improve patient comfort and have the potential to accelerate the adoption of transcranial FUS therapy by improving access to the technology.

Skull-conformal phased arrays stand to improve patient comfort and have the potential to accelerate the adoption of transcranial FUS therapy by improving access to the technology.Neural circuits develop through a plastic phase orchestrated by genetic programs and environmental signals. We have identified a leucine-rich-repeat domain transmembrane protein PAN-1 as a factor required for synaptic rewiring in C. elegans. PAN-1 localizes on cell membrane and binds with MYRF, a membrane-bound transcription factor indispensable for promoting synaptic rewiring. Full-length MYRF was known to undergo self-cleavage on ER membrane and release its transcriptional N-terminal fragment in cultured cells. We surprisingly find that MYRF trafficking to cell membrane before cleavage is pivotal for C. elegans development and the timing of N-MYRF release coincides with the onset of synaptic rewiring. On cell membrane PAN-1 and MYRF interact with each other via their extracellular regions. Loss of PAN-1 abolishes MYRF cell membrane localization, consequently blocking myrf-dependent neuronal rewiring process. Thus, through interactions with a cooperating factor on the cell membrane, MYRF may link cell surface activities to transcriptional cascades required for development.