Numerical Phantom Research Articles

Spatiotemporal image reconstruction for photoacoustic computed tomography of tumor vascular perfusion in murine models

Monitoring critical physiological processes in murine models like tumor vascular perfusion and its response to prospective anti-cancer treatments is a significant potential use case of dynamic photoacoustic computed tomography (PACT). Previously reported studies of dynamic PACT are based on a frame-by-frame image reconstruction (FBFIR) procedure in which full-view measurement data are assumed to be rapidly acquired. However, many commercial three-dimensional PACT imagers acquire measurements at each tomographic view rotating the object in discrete steps. The time to collect the full-view data is limited by the rotation speed of the object holder and the laser repetition rate. Therefore, FBFIR techniques are not applicable, and there is a critical need for accurate and efficient spatiotemporal image reconstruction (STIR) techniques that can account for spatiotemporal redundancies in the object’s features. To address this, we propose a low-rank matrix estimation-based STIR technique in which the sought-after dynamic image is approximated using a semiseparable approximation in space and time. To validate the proposed method, we also develop a virtual imaging framework for PACT employing dynamic numerical mouse phantoms with a physiologically realistic respiratory motion and perfusion model. The studies demonstrated that the proposed method accurately recovers the tumor vascular perfusion and object motion.

Computationally efficient deep learning-based image reconstruction for sound speed estimation in ultrasound computed tomography

Ultrasound computed tomography (USCT) has great potential for breast cancer imaging due to its ability to quantify acoustic tissue properties such as the speed of sound (SOS). Full-waveform inversion (FWI)-based image reconstruction methods can produce high spatial resolution SOS images, but these methods are computationally demanding, especially when formulated in the time-domain. Deep learning (DL)-based methods can be computationally efficient and provide high-quality object estimates as studied in various medical image reconstruction problems. However, there remains a further need to develop and assess the DL-based image reconstruction methods for USCT for use in medical imaging applications. Here, we present a supervised DL approach for USCT image reconstruction that is formulated as an image-to-image translation problem. Specifically, a low-resolution SOS image obtained via traveltime tomography and a high-resolution reflection tomography image are used as inputs to a U-Net, which is trained to estimate a high-resolution SOS map. These input images are complementary, providing both quantitative information about SOS values and information about tissue boundaries. Numerical studies that employ realistic numerical breast phantoms demonstrated that the proposed method produced high-quality SOS estimates based on image quality metrics such as NMSE, SSIM, and PSNR.

Virtual imaging trials to investigate impact of skin color on three-dimensional optoacoustic tomography of the breast

Optoacoustic tomography (OAT), also known as photoacoustic computed tomography, is being actively developed for breast imaging applications. The endogenous optical contrast in OAT images is associated with oxygen saturation and concentrations of chromophores, such as hemoglobin, melanin, fat, and water, within the tissue. In OAT breast imaging, near-infrared light propagates through the skin, where the optical energy is absorbed primarily by melanin. The photoacoustic effect results in the generation of a pressure wavefield, and the propagated pressure wavefield is measured by ultrasonic transducers located on a measurement aperture surrounding the breast. Thus, the melanin concentration influences lesion contrast in OAT images. However, the extent to which skin color affects lesion detectability in OAT breast imaging remains unexplored. To address this, we generate realistic optoacoustic 3D numerical breast phantoms containing a lesion at different locations (three depths and two polar angles) with five skin colors and virtually acquire optoacoustic data employing them. To assess the skin color impact on lesion detectability, we quantify numerical observer performance for a signal-known-exactly and background-known-exactly detection task. The results confirm that the signal-to-noise ratio of the test statistic is degraded in darker skin, and the extent depends on lesion locations and the light delivery system design.

Three-dimensional time-domain full-waveform inversion for ring-array-based ultrasound computed tomography

Ultrasound computed tomography (USCT) is a promising imaging technique for breast cancer diagnosis. Full-waveform inversion (FWI)-based image reconstruction methods based on the acoustic wave equation can produce quantitatively accurate images of acoustic properties with higher spatial resolution compared to ray-based methods. One common USCT design employs a circular ring-array of elevation-focused ultrasonic transducers that can be translated orthogonally to the imaging plane to achieve volumetric scanning. Currently, slice-by-slice (SBS) reconstruction methods are often used in this setting. Such methods use a 2D wave physic model to reconstruct a cross-sectional image for each position of the ring-array and vertically stack them together to render the 3D volume. However, this approach neglects the 3D wave propagation physics and transducer’s focusing properties, leading to out-of-plane scattering artifacts and degraded spatial resolution. To address this, a 3D time-domain FWI method that utilizes measurement data acquired at multiple adjacent ring-array locations is proposed. The reconstruction method was investigated using virtual imaging studies of ring-array-based USCT that employed realistic 3D numerical breast phantoms. The results demonstrated improved spatial resolution (both in-plane and off-plane) and reduced image artifacts compared to the SBS approach. Additionally, the impact of the number of ring-array locations on image quality is assessed.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments. This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( ), peak signal-to-noise ratio ( ), and Pearson correlation ( ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy

Objective. Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of tumor probes in small animals. However, traditional deep learning reconstruction methods that aim to minimize the mean squared error (MSE) and iterative regularization algorithms that rely on optimal parameters are typically influenced by strong noise, resulting in poor FMT reconstruction robustness. Approach. In this letter, we propose an adaptive adversarial learning strategy (3D-UR-WGAN) to achieve robust FMT reconstructions. Unlike the pixel-based MSE criterion in traditional CNNs or the regularization strategy in iterative solving schemes, the reconstruction strategy can greatly facilitate the performance of the network models through alternating loop training of the generator and the discriminator. Second, the reconstruction strategy combines the adversarial loss in GANs with the L1 loss to significantly enhance the robustness and preserve image details and textual information. Main results. Both numerical simulations and physical phantom experiments demonstrate that the 3D-UR-WGAN method can adaptively eliminate the effects of different noise levels on the reconstruction results, resulting in robust reconstructed images with reduced artifacts and enhanced image contrast. Compared with the state-of-the-art methods, the proposed method achieves better reconstruction performance in terms of target shape recovery and localization accuracy. Significance. This adaptive adversarial learning reconstruction strategy can provide a possible paradigm for robust reconstruction in complex environments, and also has great potential to provide an alternative solution for solving the problem of poor robustness encountered in other optical imaging modalities such as diffuse optical tomography, bioluminescence imaging, and Cherenkov luminescence imaging.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.

Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

Joint Estimation of Metal Density and Attenuation Maps with Pencil Beam XFET.

X-ray fluorescence emission tomography (XFET) is an emerging imaging modality that images the spatial distribution of metal without requiring biochemical modification or radioactivity. This work investigates the joint estimation of metal and attenuation maps with a pencil-beam XFET system that allows for direct metal measurement in the absence of attenuation. Using singular value decomposition on a simplified imaging model, we show that reconstructing metal and attenuation voxels far from the detector is an ill-conditioned problem. Using simulated data, we develop and compare two image reconstruction methods for joint estimation. The first method alternates between updating the attenuation map with a separable paraboloidal surrogates algorithm and updating the metal map with a closed-form solution. The second method performs simultaneous joint estimation with conjugate gradients based on a linearized imaging model. The alternating approach outperforms the linearized approach for iron and gold numerical phantom reconstructions. Reconstructing an (8 cm)3 object containing gold concentrations of 5 mg/cm3 and an unknown beam attenuation map using the alternating approach yields an accurate gold map (NRMSE = 0.19) and attenuation map (NRMSE = 0.14). This simulation demonstrates an accurate joint reconstruction of metal and attenuation maps, from emission data, without previous knowledge of any attenuation map.

MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation

BackgroundStandardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. To this end, we propose MRXCAT2.0 to generate synthetic data, covering healthy and pathological function, using a biophysical model. We exemplify the approach by generating cardiovascular magnetic resonance (CMR) images of healthy, infarcted, dilated and hypertrophic left-ventricular (LV) function. MethodIn MRXCAT2.0, the XCAT torso phantom is coupled with a statistical shape model, describing population (patho)physiological variability, and a biophysical model, providing known and detailed functional ground truth of LV morphology and function. CMR balanced steady-state free precession images are generated using MRXCAT2.0 while realistic image appearance is ensured by assigning texturized tissue properties to the phantom labels. FindingPaired CMR image and ground truth data of LV function were generated with a range of LV masses (85–140 g), ejection fractions (34–51%) and peak radial and circumferential strains (0.45 to 0.95 and − 0.18 to − 0.13, respectively). These ranges cover healthy and pathological cases, including infarction, dilated and hypertrophic cardiomyopathy. The generation of the anatomy takes a few seconds and it improves on current state-of-the-art models where the pathological representation is not explicitly addressed. For the full simulation framework, the biophysical models require approximately two hours, while image generation requires a few minutes per slice. ConclusionMRXCAT2.0 offers synthesis of realistic images embedding population-based anatomical and functional variability and associated ground truth parameters to facilitate a standardized assessment of CMR acquisition, reconstruction and processing methods.

Deformable cardiac surface tracking by adaptive estimation algorithms

This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step. For the real cardiac MRI datasets, the average root-mean-square tracking errors of 2.61 mm and 3.42 mm are reported respectively for the fixed and varying image slice planes. This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations via a low-order probabilistic model with the future goal of utilizing this method for the targeted interventional cardiac procedures under MR image guidance. For the real cardiac MRI datasets, the presented method was able to track the points-of-interests located on different sections of the cardiac surface within a precision of 3 pixels. The analyses show that the use of deformable cardiac surface tracking algorithm can pave the way for performing precise targeted intracardiac ablation procedures under MRI guidance. The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.

Effects of spatial and temporal resolution on cardiovascular magnetic resonance feature tracking measurements using a simple realistic numerical phantom.

To develop a single-slice numerical phantom with known myocardial motion, at several temporal and in-plane spatial resolutions, for testing and comparison of Cardiovascular Magnetic Resonance (CMR) feature tracking (FT) software. The phantom was developed based on CMR acquisitions of one volunteer (acquired cine, tagging cine, T1 map, T2 map, proton density weighted image). The numerical MRI simulator JEMRIS was used, and the phantom was generated at several in-plane spatial resolutions (1.4 × 1.4 mm2 to 3.0 × 3.0 mm2) and temporal resolutions (20 to 40 cardiac phases). Two feature tracking software packages were tested: Medical Image Tracking Toolbox (MITT) and two versions of cvi42 (v5.3.8 and v5.13.7). The effect of resolution on strain results was investigated with reference to ground-truth radial and circumferential strain. Peak radial strain was consistently undermeasured more for cvi42 v5.13.7 than for v5.3.8. Increased pixel size produced a trend of increased difference from ground-truth peak strain, with the largest changes for cvi42 obtained using v5.13.7 between 1.4 × 1.4 mm2 and 3.0 × 3.0 mm2, at 9.17 percentage points (radial) and 8.42 percentage points (circumferential). The results corroborate the presence of intervendor differences in feature tracking results and show the magnitude of strain differences between software versions. This study shows how temporal and in-plane spatial resolution can affect feature tracking with reference to the ground-truth strain of a numerical phantom. Results reaffirm the need for numerical phantom development for the validation and testing of FT software.

Demonstration of accurate multi-pool chemical exchange saturation transfer MRI quantification – Quasi-steady-state reconstruction empowered quantitative CEST analysis

Chemical exchange saturation transfer (CEST) MRI is sensitive to dilute labile protons and microenvironment properties, yet CEST quantification has been challenging. This difficulty is because the CEST measurement depends not only on the underlying CEST system but also on the scan protocols, including RF saturation amplitude, duration, and repetition time. In addition, T1 normalization is not straightforward under non-equilibrium conditions. Recently, a quasi-steady-state (QUASS) algorithm was established to reconstruct the desired equilibrium state from experimental measurements. Our study aimed to determine the accuracy of spinlock-model-based multi-pool CEST quantification using numerical simulations and phantom experiments. In short, CEST Z-spectra were simulated for a representative 3-pool model, and CEST amplitudes were solved with spinlock model-based multi-pool fitting and assessed as a function of RF saturation time (Ts), repetition time (TR), and T1. Although the apparent CEST signals showed significant T1 dependence, such relationships were not observed following QUASS reconstruction. To test the accuracy of T1 correction, a multi-vial phantom of nicotinamide and creatine was doped with manganese chloride, resulting in T1 ranging from 1 s to beyond 2 s. The multi-labile signals determined from the routine measurements showed significant dependence on Ts, TR, and T1. In contrast, CEST signals from the QUASS reconstruction showed consistent quantification independent of such variables. To summarize, our study demonstrated that accurate CEST quantification is feasible in multi-pool CEST systems with the spinlock-model-based fitting of QUASS CEST MRI.

Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.

As an emerging imaging technique, Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of quantum emission fields within tissue using X-ray excitation for deep penetrance. However, its reconstruction is an ill-posed and under-conditioned inverse problem because of the diffuse optical emission signal. Deep learning based image reconstruction has shown very good potential for solving these types of problems, however they suffer from a lack of ground-truth image data to confirm when used with experimental data. To overcome this, a self-supervised network cascaded by a 3D reconstruction network and the forward model, termed Selfrec-Net, was proposed to perform CELST reconstruction. Under this framework, the boundary measurements are input to the network to reconstruct the distribution of the quantum field and the predicted measurements are subsequently obtained by feeding the reconstructed result to the forward model. The network was trained by minimizing the loss between the input measurements and the predicted measurements rather than the reconstructed distributions and the corresponding ground truths. Comparative experiments were carried out on both numerical simulations and physical phantoms. For singular luminescent targets, the results demonstrate the effectiveness and robustness of the proposed network, and comparable performance can be attained to a state-of-the-art deep supervised learning algorithm, where the accuracy of the emission yield and localization of the objects was far superior to iterative reconstruction methods. Reconstruction of multiple objects is still reasonable with high localization accuracy, although with limits to the emission yield accuracy as the distribution becomes more complex. Overall though the reconstruction of Selfrec-Net provides a self-supervised way to recover the location and emission yield of molecular distributions in murine model tissues.

Region Expansion of Background Field Removal with Local Spherical Harmonics Approximation for Whole-brain Quantitative Susceptibility Mapping.

Quantitative susceptibility mapping (QSM) is useful for obtaining biological information. To calculate susceptibility distribution, it is necessary to calculate the local field caused by the differences of susceptibility between the tissues. The local field can be obtained by removing a background field from a total field acquired by MR phase image. Conventional approaches based on spherical mean value (SMV) filtering, which are widely used for background field calculations, fail to calculate the background field of the brain surface region corresponding to the radius of the SMV kernel, and consequently cannot calculate the QSM of the brain surface region. Accordingly, a new method calculating the local field by expansively removing the background field is proposed for whole brain QSM. The proposed method consists of two steps. First, the background field of the brain surface is calculated from the total field using a locally polynomial approximation of spherical harmonics. Second, the whole brain local field is calculated by SMV filtering with a constraint term of the background field of the brain surface. The parameters of the approximation were optimized to reduce calculation errors through simulations using both a numerical phantom and a measured human brain. Performance of the proposed method with the optimized parameters was quantitatively and visually compared with conventional methods in an experiment of five healthy volunteers. The proposed method showed the accurate local field over the expanded brain region in the simulation studies. It also showed consistent QSM with conventional methods inside of the brain surface and showed clear vein structures on the brain surface. The proposed method enables accurate calculation of whole brain QSM without eroding the brain surface region while maintaining same values inside of the brain surface as the conventional methods.

Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS

We report a new workflow for background-oriented schlieren (BOS), termed “physics-informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisfy the measurement data and governing equations. For the high-speed, approximately inviscid flows of interest in this work, we specify a physics loss based on the Euler and irrotationality equations. BOS is a quantitative fluid visualization technique that is commonly used to characterize compressible flow. Images of a background pattern, positioned behind the measurement volume, are processed with computer vision and tomography algorithms to determine the density field. Crucially, BOS features a series of ill-posed inverse problems that require supplemental information (i.e., in addition to the images) to accurately reconstruct the flow. Current methods for BOS rely upon interpolation of the images or a penalty term to promote a globally- or piecewise-smooth solution. However, these algorithms are invariably incompatible with the flow physics, leading to errors in the density field. Physics-informed BOS directly reconstructs all the flow fields using a PINN that includes the BOS measurement model and governing equations. This procedure improves the accuracy of density estimates and also yields velocity and pressure data, which were not previously available. We demonstrate our approach by reconstructing synthetic data that corresponds to analytical and numerical phantoms as well as a single pair of experimental measurements. Our physics-informed reconstructions are significantly more accurate than conventional BOS estimates. Furthermore, to the best of our knowledge, this work represents the first use of a PINN to reconstruct a supersonic flow from experimental data of any kind.

Spectral CT Image-Domain Material Decomposition via Sparsity Residual Prior and Dictionary Learning

The spectral computed tomography (CT) system based on a photon-counting detector (PCD) can quantitatively analyze the material composition of the inspected object by material decomposition. Nonetheless, the raw projection of spectral CT is frequently disturbed by noise and artifacts, resulting in poor quality material decomposition images. Recently, a generalized dictionary learning based image-domain material decomposition (GDLIMD) to obtain high-quality material images. DL has great advantages in noise suppression and artifacts, while its protection of the fine structure and edge information is insufficient. To address this limitation, we proposed a sparsity residual prior and dictionary learning (SRPDL) algorithm for spectral CT image-domain material decomposition. The SRPDL method retains the noise-resistance performance of dictionary learning (DL) while introducing the pixel-value-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{0} $ </tex-math></inline-formula> norm constraint to guide the material decomposition process by using the structural redundancy information between the prior image and the material images, which further improves structure protection and reduces material misclassification. We conducted numerical simulations, physical phantom, and preclinical experiments to validate and evaluate the SRPDL method. The results demonstrate that the proposed SRPDL method obtained better material decomposition accuracy than the state-of-the-art methods in noise reduction and edge protection.

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l -frame raw US images with l being significantly smaller than L . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows ∼40 -fold reduction in data acquisition time and ∼61 -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time ( ∼0.33 s) and ultrafast data processing speed ( ∼0.60 s) that makes it promising to observe rapid biological activities in vivo.

Numerical Modeling and Computer Simulation of a Meander Line Antenna for Alzheimer's Disease Treatment, a Feasibility Study.

Alzheimer's disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions; research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 - 0.9 W/kg decrease the level of amyloid-β peptides (Aβ), which is the most likely etiology of AD. This study emphasizes uniform E/H field and SAR distribution with adequate penetration depth penetration through multiple human head layers driven with low input power for safety treatments. In this work, we performed numerical modeling and computer simulations of a portable Meander Line antenna (MLA) to achieve the required EMF parameters to treat AD. The MLA device features a low cost, small size, wide bandwidth, and the ability to integrate into a portable system. This study utilized a High-Frequency Simulation System (HFSS) in the design of the MLA with the desired characteristics suited for AD treatment in humans. The team designed a 24-turn antenna with a 60 cm length and 25 cm width and achieved the required resonant frequency of 64 MHz. Here we used two numerical human head phantoms to test the antenna, the MIDA and spherical head phantom with six and seven tissue layers, respectively. The antenna was fed from a 50-Watt input source to obtain the SAR of 0.6 W/kg requirement in the center of the simulated brain tissue layer. We found that the E/H field and SAR distribution produced was not homogeneous; there were areas of high SAR values close to the antenna transmitter, also areas of low SAR value far away from the antenna. This paper details the antenna parameters, the scattering parameters response, the efficiency response, and the E and H field distribution; we presented the computer simulation results and discussed future work for a practical model.

Design of stationary computed tomography baggage scanner with π-angle sparsity and compressed-sensing reconstruction: simulation study

Continuing our research on X-ray R&D for aviation security, we designed a novel stationary computed tomography (SCT) baggage scanner with π-angle sparsity, comprising several dozen pairs of nanotube-based X-ray source and linear array-type detector placed within a scan angle of 180° at an equiangular distance. Simultaneously, we developed a reconstruction method for the specific configuration adopting an iterative algorithm based on the popular compressed-sensing (CS) scheme. To validate the efficacy of the proposed SCT design, we conducted a series of simulations using a numerical baggage phantom before its practical implementation. The results showed that the proposed SCT design and corresponding reconstruction method significantly reduced the streak artifacts appearing in the filtered-backprojection (FBP) reconstruction, thereby considerably improving the image quality. The contrast-to-noise ratio evaluated under a test condition of 180°/P20/CS was approximately 16.2, approximately 4.7 and 1.1 times larger than those for 360°/P20/FBP and 360°/P20/CS, respectively. The proposed SCT design enables saving scan time, improving image quality, and restricting radiation exposure zone.

Neonatal Brain Temperature Monitoring Based on Broadband Near-Infrared Spectroscopy.

We present here the initial development of a novel algorithm based on broadband near-infrared spectroscopy (bNIRS) data to estimate the changes in brain temperature (BT) in neonates. We first explored the validity of the methodology on a simple numerical phantom and reported good agreements between the theoretical and retrieved values of BT and hemodynamic parameters changes, which are the parameters usually targeted by bNIRS. However, we noted an underestimation of the absolute values of temperature and haemoglobins' concentration changes when large variations of tissue saturation were induced, probably due to a crosstalk between the species in this specific case. We then tested this methodology on data acquired on 2 piglets during a protocol that induces seizures. We showed that despite a decrease in rectal temperature (RT) over time (-0.1048°C 1.5h after seizure induction, 95% CI: -0.1035 to -0.1061°C), BT was raising (0.3122°C 1.5h after seizure induction, 95% CI: 0.3207 to 0.3237°C). We also noted that the piglet displaying the largest decrease in RT also displays the highest increase in BT, which could be a marker of the severity of the seizure induced brain injury. These initial results are encouraging and show that having access to the changes in BT non-invasively could help to better understand the impact of BT on injury severity and to improve the current cooling methodologies in the neonatal neurocritical care following neonatal encephalopathy.