Imaging phantom

About: Imaging phantom is a research topic. Over the lifetime, 28170 publications have been published within this topic receiving 510003 citations. The topic is also known as: phantom.

Stability of phantom wormholes

Abstract: It has recently been shown that traversable wormholes may be supported by phantom energy. In this work phantom wormhole geometries are modeled by matching an interior traversable wormhole solution, governed by the equation of state $p=\ensuremath{\omega}\ensuremath{\rho}$ with $\ensuremath{\omega}l\ensuremath{-}1$, to an exterior vacuum spacetime at a finite junction interface. The stability analysis of these phantom wormholes to linearized spherically symmetric perturbations about static equilibrium solutions is carried out. A master equation dictating the stability regions is deduced, and by separating the cases of a positive and a negative surface energy density, it is found that the respective stable equilibrium configurations may be increased by strategically varying the wormhole throat radius. The first model considered, in the absence of a thin shell, is that of an asymptotically flat phantom wormhole spacetime. The second model constructed is that of an isotropic pressure phantom wormhole, which is of particular interest, as the notion of phantom energy is that of a spatially homogeneous cosmic fluid, although it may be extended to inhomogeneous spherically symmetric spacetimes.

Tissue-mimicking phantoms for photoacoustic and ultrasonic imaging

Abstract: In both photoacoustic (PA) and ultrasonic (US) imaging, overall image quality is influenced by the optical and acoustical properties of the medium. Consequently, with the increased use of combined PA and US (PAUS) imaging in preclinical and clinical applications, the ability to provide phantoms that are capable of mimicking desired properties of soft tissues is critical. To this end, gelatin-based phantoms were constructed with various additives to provide realistic acoustic and optical properties. Forty-micron, spherical silica particles were used to induce acoustic scattering, Intralipid® 20% IV fat emulsion was employed to enhance optical scattering and ultrasonic attenuation, while India Ink, Direct Red 81, and Evans blue dyes were utilized to achieve optical absorption typical of soft tissues. The following parameters were then measured in each phantom formulation: speed of sound, acoustic attenuation (from 6 to 22 MHz), acoustic backscatter coefficient (from 6 to 22 MHz), optical absorption (from 400 nm to 1300 nm), and optical scattering (from 400 nm to 1300 nm). Results from these measurements were then compared to similar measurements, which are offered by the literature, for various soft tissue types. Based on these comparisons, it was shown that a reasonably accurate tissue-mimicking phantom could be constructed using a gelatin base with the aforementioned additives. Thus, it is possible to construct a phantom that mimics specific tissue acoustical and/or optical properties for the purpose of PAUS imaging studies.

Step-and-shoot data acquisition and reconstruction for cardiac x-ray computed tomography

Abstract: Coronary artery imaging with x-ray computed tomography (CT) is one of the most recent advancements in CT clinical applications. Although existing ''state-of-the-art'' clinical protocols today utilize helical data acquisition, it suffers from the lack of ability to handle irregular heart rate and relatively high x-ray dose to patients. In this paper, we propose a step-and-shoot data acquisition protocol that significantly overcomes these shortcomings. The key to the proposed protocol is the large volume coverage (40 mm) enabled by the cone beam CT scanner, which allows the coverage of the entire heart in 3 to 4 steps. In addition, we propose a gated complementary reconstruction algorithm that overcomes the longitudinal truncation problem resulting from the cone beam geometry. Computer simulations, phantom experiments, and clinical studies were conducted to validate our approach.

Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo

Abstract: We propose a Doppler optical micro-angiography (DOMAG) method to image flow velocities of the blood flowing in functional vessels within microcirculatory tissue beds in vivo. The method takes the advantages of recently developed optical micro-angiography (OMAG) technology, in which the endogenous optical signals backscattered from the moving blood cells are isolated from those originated from the tissue background, i.e., the tissue microstructures. The phase difference between adjacent A scans of OMAG flow signals is used to evaluate the flow velocity, similar to phase-resolved Doppler optical coherence tomography (PRDOCT). To meet the requirement of correlation between adjacent A scans in using the phase resolved technique to evaluate flow velocity, an ideal tissue-sample background (i.e., optically homogeneous tissue sample) is digitally reconstructed to replace the signals that represent the heterogeneous features of the static sample that are rejected in the OMAG flow images. Because of the ideal optical-homogeneous sample, DOMAG is free from the characteristic texture pattern noise due to the heterogeneous property of sample, leading to dramatic improvement of the imaging performance. A series of phantom flow experiments are performed to evaluate quantitatively the improved imaging performance. We then conduct in vivo experiments on a mouse brain to demonstrate that DOMAG is capable of quantifying the flow velocities within cerebrovascular network, down to capillary level resolution. Finally, we compare the in vivo imaging performance of DOMAG with that of PRDOCT, and show that DOMAG delivers at least 15-fold increase over the PRDOCT method in terms of the lower limit of flow velocity that can be detected.

Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases

Abstract: Simulations provide a way of generating data where ground truth is known, enabling quantitative testing of image processing methods. In this paper, we present the construction of 20 realistic digital brain phantoms that can be used to simulate medical imaging data. The phantoms are made from 20 normal adults to take into account intersubject anatomical variabilities. Each digital brain phantom was created by registering and averaging four T1, T2, and proton density (PD)-weighted magnetic resonance imaging (MRI) scans from each subject. A fuzzy minimum distance classification was used to classify voxel intensities from T1, T2, and PD average volumes into grey-matter, white matter, cerebro-spinal fluid, and fat. Automatically generated mask volumes were required to separate brain from nonbrain structures and ten fuzzy tissue volumes were created: grey matter, white matter, cerebro-spinal fluid, skull, marrow within the bone, dura, fat, tissue around the fat, muscles, and skin/muscles. A fuzzy vessel class was also obtained from the segmentation of the magnetic resonance angiography scan of the subject. These eleven fuzzy volumes that describe the spatial distribution of anatomical tissues define the digital phantom, where voxel intensity is proportional to the fraction of tissue within the voxel. These fuzzy volumes can be used to drive simulators for different modalities including MRI, PET, or SPECT. These phantoms were used to construct 20 simulated T1-weighted MR scans. To evaluate the realism of these simulations, we propose two approaches to compare them to real data acquired with the same acquisition parameters. The first approach consists of comparing the intensities within the segmented classes in both real and simulated data. In the second approach, a whole brain voxel-wise comparison between simulations and real T1-weighted data is performed. The first comparison underlines that segmented classes appear to properly represent the anatomy on average, and that inside these classes, the simulated and real intensity values are quite similar. The second comparison enables the study of the regional variations with no a priori class. The experiments demonstrate that these variations are small when real data are corrected for intensity nonuniformity