Software Phantom Research Articles

Preclinical validation of the advection diffusion flow estimation method using computational patient specific coronary tree phantoms.

Coronary computed tomography angiography (CCTA) does not allow the quantification of reduced blood flow due to coronary artery disease (CAD). In response, numerical methods based on the CCTA image have been developed to compute coronary blood flow and assess the impact of disease. However to compute blood flow in the coronary arteries, numerical methods require specification of boundary conditions that are difficult to estimate accurately in a patient-specific manner. We describe herein a new noninvasive flow estimation method, called Advection Diffusion Flow Estimation (ADFE), to compute coronary artery flow from CCTA to use as boundary conditions for numerical models of coronary blood flow. ADFE uses image contrast variation along the tree-like structure to estimate flow in each vessel. For validating this method we used patient specific software phantoms on which the transport of contrast was simulated. This controlled validation setting enables a direct comparison between estimated flow and actual flow and a detailed investigation of factors affecting accuracy. A total of 10 CCTA image data sets were processed to extract all necessary information for simulating contrast transport. A spectral element method solver was used for computing the ground truth simulations with high accuracy. On this data set, the ADFE method showed a high correlation coefficient of between estimated flow and the ground truth flow together with an average relative error of only . Comparing the ADFE method with the best method currently available (TAFE) for image-based blood flow estimation, which showed a correlation coefficient of and average error of , it can be concluded that the ADFE method has the potential to significantly improve the quantification of coronary artery blood flow derived from contrast gradients in CCTA images.

Which NODDI implementation is nicest for diffusion MRI?

AbstractBackgroundNeurite Orientation Dispersion and Density Imaging (NODDI) is becoming increasingly common as multiband acceleration becomes available for clinical scanning, but fitting the model can be extremely slow. Since the release of the original MATLAB implementation in 2012, three alternatives have become popular: Accelerated Microstructure Imaging via Convex Optimization (AMICO), the Microstructure Diffusion Toolbox (MDT), and the CUDA Diffusion Modelling Toolbox (cuDIMOT). They accelerate fitting through reformulation and/or using GPUs, but the implementation differences raise the question of which approach is best. To our knowledge all previous comparisons lacked knowledge of the true values and used the original implementation as the nominally correct one, with in vivo data. We remedy that using simulations with known NODDI values, in addition to in vivo scans.MethodWe created a software phantom with Neurite Density Index (NDI) values linearly increasing from 0.1 to 1.0 in x, Orientation Dispersion (ODI) from 0 to 1 in y, and Isotropic water Volume Fraction (ISOVF) from 0 to 0.9 in z. We simulated five scans using the ADNI3 advanced diffusion MRI protocol. The phantom had 20 voxels in each direction, and the fiber bundle principal direction randomly varied between the simulations. The simulations were fit by the original, AMICO, MDT, and cuDIMOT implementations, and root mean square errors were calculated as shown in Table 1. A discrepancy measure was also calculated using the brain voxels of three in vivo human scans acquired using the same protocol on a Siemens Prisma, substituting the original implementation’s values for the truth since the latter is unknown for in vivo scans.ResultcuDIMOT had the least error (for ODI < 0.8) and was fastest. The original implementation was a close second in accuracy but much slower. cuDIMOT’s ODI underestimation can be corrected with a transfer function in the noise‐free case, but that approach fails with actual scans.ConclusionThe original or cuDIMOT implementations should be used, with cuDIMOT the favorite when at least one CUDA‐capable GPU is available and the number of scans are large. Correcting cuDIMOT’s ODI underestimation using a different initialization or form of Dawson’s integral should be investigated.

Which NODDI implementation is nicest for diffusion MRI?

AbstractBackgroundNeurite Orientation Dispersion and Density Imaging (NODDI) is becoming increasingly common as multiband acceleration becomes available for clinical scanning, but fitting the model can be extremely slow. Since the release of the original MATLAB implementation in 2012, three alternatives have become popular: Accelerated Microstructure Imaging via Convex Optimization (AMICO), the Microstructure Diffusion Toolbox (MDT), and the CUDA Diffusion Modelling Toolbox (cuDIMOT). They accelerate fitting through reformulation and/or using GPUs, but the implementation differences raise the question of which approach is best. To our knowledge all previous comparisons lacked knowledge of the true values and used the original implementation as the nominally correct one, with in vivo data. We remedy that using simulations with known NODDI values, in addition to in vivo scans.MethodWe created a software phantom with Neurite Density Index (NDI) values linearly increasing from 0.1 to 1.0 in x, Orientation Dispersion (ODI) from 0 to 1 in y, and Isotropic water Volume Fraction (ISOVF) from 0 to 0.9 in z. We simulated five scans using the ADNI3 advanced diffusion MRI protocol. The phantom had 20 voxels in each direction, and the fiber bundle principal direction randomly varied between the simulations. The simulations were fit by the original, AMICO, MDT, and cuDIMOT implementations, and root mean square errors were calculated as shown in Table 1. A discrepancy measure was also calculated using the brain voxels of three in vivo human scans acquired using the same protocol on a Siemens Prisma, substituting the original implementation’s values for the truth since the latter is unknown for in vivo scans.ResultcuDIMOT had the least error (for ODI < 0.8) and was fastest. The original implementation was a close second in accuracy but much slower. cuDIMOT’s ODI underestimation can be corrected with a transfer function in the noise‐free case, but that approach fails with actual scans.ConclusionThe original or cuDIMOT implementations should be used, with cuDIMOT the favorite when at least one CUDA‐capable GPU is available and the number of scans are large. Correcting cuDIMOT’s ODI underestimation using a different initialization or form of Dawson’s integral should be investigated.

Fractal analysis of perfusion imaging in synovitis: a novel imaging biomarker for grading inflammatory activity based on assessing angiogenesis

ObjectivesThe mutual and intertwined dependence of inflammation and angiogenesis in synovitis is widely acknowledged. However, no clinically established tool for objective and quantitative assessment of angiogenesis is routinely available. This...

On the separation of susceptibility sources in quantitative susceptibility mapping: Theory and phantom validation with an in vivo application to multiple sclerosis lesions of different age

PurposeIn biological tissue, phase contrast is determined by multiple substances such as iron, myelin or calcifications. Often, these substances occur co-located within the same measurement volume. However, quantitative susceptibility mapping can solely measure the average susceptibility per voxel. To provide new insight in disease progression and mechanisms in neurological diseases, where multiple processes such as demyelination and iron accumulation occur simultaneously in the same location, a separation of susceptibility sources is desirable to disentangle the underlying susceptibility proportions. MethodsThe basic concept of separating the susceptibility effects from sources with different sign within one voxel is to include information on relaxation rate ΔR2∗ in the quantitative susceptibility mapping reconstruction pipeline. The presented reconstruction algorithm is implemented as a constrained minimization problem and solved using conjugate gradients. The algorithm is evaluated using a software phantom and validated in MRI measurements with a phantom containing mixtures of microscopic positive and negative susceptibility sources. Data from three multiple sclerosis patients are used to show in vivo feasibility. ResultsIn numerical simulations, the feasibility of disentangling susceptibility sources within the same voxel was confirmed provided the critera of the static dephasing regime were fulfilled. In phantom experiments, the magnitude decay kernel, which is an essential reconstruction parameter of the algorithm, was determined to be Dm=194.5T-1s-1ppm-1, and susceptibility sources could be separated in MRI measurement data. ConclusionsIn conclusion, in this study a detailed description of the implementation of an algorithm for the separation of positive and negative susceptibility sources within the same volume element as well as its limitations is presented and validated quantitatively in both simulation and phantom experiments for the first time. An application to multiple sclerosis lesions shows promising results for in vivo usability.

Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT).

Interventional treatments of aneurysms in the carotid artery are increasingly being supplemented with three-dimensional (3D) x-ray imaging. The 3D imaging provides additional information on device sizing and stent malapposition during the procedure. Standard 3D x-ray image acquisition is a one-size fits all model, exposing patients to additional radiation and results in images that may have cardiac-induced motion blur around the artery. Here, we investigate the potential of a novel dynamic imaging technique Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) to personalize image acquisition by adapting the gantry velocity and projection rate in real-time to changes in the patient's electrocardiogram (ECG) trace. We compared the total number of projections acquired, estimated carotid artery widths and image quality between ACROBEAT and conventional (single rotation fixed gantry velocity and acquisition rate, no ECG-gating) scans in a simulation study and a proof-of-concept physical phantom experimental study. The simulation study dataset consisted of an XCAT digital software phantom programmed with five patient-measured ECG traces and artery motion curves. The ECG traces had average heart rates of 56, 64, 76, 86, and 100bpm. To validate the concept experimentally, we designed and manufactured the physical phantom from an 8-mm diameter silicon rubber tubing cast into Phytagel. An artery motion curve and the ECG trace with an average heart rate of 56bpm was passed through the phantom. To implement ACROBEAT on the Siemens ARTIS pheno angiography system for the proof-of-concept experimental study, the Siemens Test Automation Control System was used. The total number of projections acquired and estimated carotid artery widths were compared between the ACROBEAT and conventional scans. As the ground truth was available for the simulation studies, the image quality metrics of Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM) were also utilized to assess image quality. In the simulation study, on average, ACROBEAT reduced the number of projections acquired by 63%, reduced carotid width estimation error by 65%, reduced RMSE by 11% and improved SSIM by 27% compared to conventional scans. In the proof-of-concept experimental study, ACROBEAT enabled a 60% reduction in the number of projections acquired and reduced carotid width estimation error by 69% compared to a conventional scan. A simulation and proof-of-concept experimental study was completed applying a novel dynamic imaging protocol, ACROBEAT, to imaging the carotid artery. The ACROBEAT results showed significantly improved image quality with fewer projections, offering potential applications to intracranial interventional procedures negatively affected by cardiac motion.

Automated Quantification of Diseased Thoracic Aortic Longitudinal Centerline and Surface Curvatures.

Precise description of vascular morphometry is crucial to support medical device manufacturers and clinicians for improving device development and interventional outcomes. A compact and intuitive method is presented to automatically characterize the surface geometry of tubular anatomic structures and quantify surface curvatures starting from generic stereolithographic (STL) surfaces. The method was validated with software phantoms and used to quantify the longitudinal surface curvatures of 37 human thoracic aortas with aneurysm or dissection. The quantification of surface curvatures showed good agreement with analytic solutions from the software phantoms, and demonstrated better agreement as compared to estimation methods using only centerline geometry and cross-sectional radii. For the human thoracic aortas, longitudinal inner surface curvature was significantly higher than centerline curvature (0.33 ± 0.06 versus 0.16 ± 0.02 cm-1 for mean; 1.38 ± 0.48 versus 0.45 ± 0.11 cm-1 for peak; both p < 0.001). These findings show the importance of quantifying surface curvatures in order to better describe the geometry and biomechanical behavior of the thoracic aorta, which can assist in treatment planning and supplying device manufactures with more precise boundary conditions for mechanical evaluation.

Tomographic Image Reconstruction based on Artificial Neural Network (ANN) Techniques

A new approach for tomographic image reconstruction from projections using Artificial Neural Network (ANN) techniques is presented in this work. The design of the proposed reconstruction system is based on a simple but efficient network architecture, which best utilizes all available input information. Due to the computational complexity, which grows quadratically with the image size, the training phase of the system is characterized by relatively large CPU times. The trained network, on the contrary, is able to provide all necessary information in a quick and efficient way giving results comparable to other time consuming iterative reconstruction algorithms. The performance of the network studied with a large number of software phantoms is directly compared to the well known Algebraic Reconstruction Technique (ART). For a given image and projections size, the role of the hidden layers in the network architecture is examined and the quality dependence of the reconstructed image on the size of the geometrical patterns used in the training phase is also investigated.

Towards patient connected imaging with ACROBEAT: Adaptive CaRdiac cOne BEAm computed Tomography

Robotic C-arm cone beam computed tomography (CBCT) systems are playing an increasingly pivotal role in interventional cardiac procedures and high precision radiotherapy treatments. One of the main challenges in any form of cardiac imaging is mitigating the intrinsic motion of the heart, which causes blurring and artefacts in the 3D reconstructed image. Most conventional 3D cardiac CBCT acquisition techniques attempt to combat heart motion through retrospective gating techniques, whereby acquired projections are sorted into the desired cardiac phase after the completion of the scan. However, this results in streaking artefacts and unnecessary radiation exposure to the patient. Here, we present our Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) acquisition protocol that uses the patient’s electrocardiogram (ECG) signal to adaptively regulate the gantry velocity and projection time interval in real-time. It enables prospectively gated patient connected imaging in a single sweep of the gantry. The XCAT digital software phantom was used to complete a simulation study to compare ACROBEAT to a conventional multi-sweep retrospective ECG gated acquisition, under a variety of different acquisition conditions. The effect of location and length of the acquisition window and total number of projections acquired on image quality and total scan time were examined. Overall, ACROBEAT enables up to a 5 times average improvement in the contrast-to-noise ratio, a 40% reduction in edge response width and an 80% reduction in total projections acquired compared to conventional multi-sweep retrospective ECG gated acquisition.

Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.

Method for Recovering Lost Ultrasonic Information Using the Echo-intensity Mean.

The axial resolution of a B-mode (or intensity) image is limited by the bandwidth of the pulse envelope. In this report, we investigate the source of this limitation by examining the transfer of high-resolution information from the tissue impedance variance throughout the imaging process. For that purpose, we express the mean and variance of the echo-intensity signal as a linear system to track the flow of object information along the image-formation chain. The results reveal how demodulation influences the available information by discarding high spatial-frequency information. This analysis further points to a simple way to recover lost information with only a minor addition to signal processing. Software phantoms are used to show that under ideal conditions, information from small-scale high-contrast reflectors, such as microcalcifications, can be significantly enhanced with this simple change to echo processing.

A Lagrangian cylindrical coordinate system for characterizing dynamic surface geometry of tubular anatomic structures

Vascular morphology characterization is useful for disease diagnosis, risk stratification, treatment planning, and prediction of treatment durability. To quantify the dynamic surface geometry of tubular-shaped anatomic structures, we propose a simple, rigorous Lagrangian cylindrical coordinate system to monitor well-defined surface points. Specifically, the proposed system enables quantification of surface curvature and cross-sectional eccentricity. Using idealized software phantom examples, we validate the method’s ability to accurately quantify longitudinal and circumferential surface curvature, as well as eccentricity and orientation of eccentricity. We then apply the method to several medical imaging data sets of human vascular structures to exemplify the utility of this coordinate system for analyzing morphology and dynamic geometric changes in blood vessels throughout the body.Graphical abstractPointwise longitudinal curvature of a thoracic aortic endograft surface for systole and diastole, with their absolute difference.

Evaluation of a breast software model for 2D and 3D X-ray imaging studies of the breast

IntroductionIn X-ray imaging, test objects reproducing breast anatomy characteristics are realized to optimize issues such as image processing or reconstruction, lesion detection performance, image quality and radiation induced detriment. Recently, a physical phantom with a structured background has been introduced for both 2D mammography and breast tomosynthesis. A software version of this phantom and a few related versions are now available and a comparison between these 3D software phantoms and the physical phantom will be presented. MethodsThe software breast phantom simulates a semi-cylindrical container filled with spherical beads of different diameters. Four computational breast phantoms were generated with a dedicated software application and for two of these, physical phantoms are also available and they are used for the side by side comparison. Planar projections in mammography and tomosynthesis were simulated under identical incident air kerma conditions. Tomosynthesis slices were reconstructed with an in-house developed reconstruction software. In addition to a visual comparison, parameters like fractal dimension, power law exponent β and second order statistics (skewness, kurtosis) of planar projections and tomosynthesis reconstructed images were compared. ResultsVisually, an excellent agreement between simulated and real planar and tomosynthesis images is observed. The comparison shows also an overall very good agreement between parameters evaluated from simulated and experimental images. ConclusionThe computational breast phantoms showed a close match with their physical versions. The detailed mathematical analysis of the images confirms the agreement between real and simulated 2D mammography and tomosynthesis images. The software phantom is ready for optimization purpose and extrapolation of the phantom to other breast imaging techniques.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

Evaluation of the effect of silicone breast inserts on X-ray mammography and breast tomosynthesis images: A Monte Carlo simulation study

PurposeBreast augmentation is one of the most popular cosmetic surgeries worldwide. The aim of this study is to investigate the effect of breast implant insertion on the detectability and visibility of lesions on mammography and breast tomosynthesis (BT) images. Materials and methodsThree software phantoms, composed of a homogeneous background with embedded silicone gel structures, and two types of breast abnormalities, microcalcifications (µCs) and masses, were generated. Two X-ray breast imaging modalities were simulated: mammography and BT with six incident monochromatic X-ray beams with energies in the interval between 20 and 30 keV. Projection images were generated using an in-house developed Monte Carlo simulator. The detectability of mammographic findings adjacent to the implant material and the influence of the incident beam energy and implant thickness on the feature detection were studied. ResultsIt was found that implants thicker than 26 mm for the case of mammography and 14 mm for the case of BT obscured the visibility of underlying structures. Although BT demonstrated a lack of contrast, this modality was able to visualize µCs under considerable depths of implant. Increasing the incident beam energy led to better visualization of small µCs, while in the case of breast masses, their detectability was limited. ConclusionsSilicone gel implants introduce a limitation in the image quality of mammograms resulting in low detectability of features. In addition, silicone gel implants obscure partially or totally parts of the image, depending on the size and the thickness of the implant as well the energy of the X-rays used.

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues

This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.

Optimisation of quantitative lung SPECT applied to mild COPD: a software phantom simulation study.

BackgroundThe amount of inhomogeneities in a 99mTc Technegas single-photon emission computed tomography (SPECT) lung image, caused by reduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), is correlated to disease advancement. A quantitative analysis method, the CVT method, measuring these inhomogeneities was proposed in earlier work. To detect mild COPD, which is a difficult task, optimised parameter values are needed.MethodsIn this work, the CVT method was optimised with respect to the parameter values of acquisition, reconstruction and analysis. The ordered subset expectation maximisation (OSEM) algorithm was used for reconstructing the lung SPECT images. As a first step towards clinical application of the CVT method in detecting mild COPD, this study was based on simulated SPECT images of an advanced anthropomorphic lung software phantom including respiratory and cardiac motion, where the mild COPD lung had an overall ventilation reduction of 5%.ResultsThe best separation between healthy and mild COPD lung images as determined using the CVT measure of ventilation inhomogeneity and 125 MBq 99mTc was obtained using a low-energy high-resolution collimator (LEHR) and a power 6 Butterworth post-filter with a cutoff frequency of 0.6 to 0.7 cm−1. Sixty-four reconstruction updates and a small kernel size should be used when the whole lung is analysed, and for the reduced lung a greater number of updates and a larger kernel size are needed.ConclusionsA LEHR collimator and 125 99mTc MBq together with an optimal combination of cutoff frequency, number of updates and kernel size, gave the best result. Suboptimal selections of either cutoff frequency, number of updates and kernel size will reduce the imaging system’s ability to detect mild COPD in the lung phantom.

Multi-tracer Imaging of a Compton Camera

Since a Compton camera has high detection sensitivity due to electronic collimation and a good energy resolution, it is a potential imaging system for nuclear medicine. In this study, we investigated the feasibility of a Compton camera for multi-tracer imaging and proposed a rotating Compton camera to satisfy Orlov’s condition for 3D imaging. Two software phantoms of 140 and 511 keV radiation sources were used for Monte-Carlo simulation and then the simulation data were reconstructed by listmode ordered subset expectation maximization to evaluate the capability of multi-tracer imaging in a Compton camera. And the Compton camera rotating around the object was proposed and tested with different rotation angle steps for improving the limited coverage of the fixed conventional Compton camera over the field-of-view in terms of histogram of angles in spherical coordinates. The simulation data showed the separate 140 and 511 keV images from simultaneous multi-tracer detection in both 2D and 3D imaging and the number of valid projection li nes on the conical surfaces was inversely proportional to the decrease of rotation angle. Considering computation load and proper number of projection lines on the conical surface, the rotation angle of 30 degree was sufficient for 3D imaging of the Compton camera in terms of 26 min of computation time and 5 million of detected event number and the increased detection time can be solved with multiple Compton camera system. The Compton camera proposed in this study can be effective system for multi-tracer imaging and is a potential system for development of various disease diagnosis and therapy approaches.

Explosive Breakup of a Water Droplet with a Nontransparent Solid Inclusion Heated in a High-Temperature Gaseous Medium

This paper investigates the evaporation of a droplet with a comparably sized solid nontransparent in a high-temperature (500-800 K) gas medium. Water evaporates from the free surface of the inclusion. During this process, intensive vapor formation occurs on the inner interface water droplet - solid inclusion with the subsequent explosive decay of the droplet. Experiments have been conducted using high- speed (up to 10 5 fps) video cameras and software Phantom Camera Control. The conditions of the explosive vapor formation of the heterogeneous droplet were found. The typical phase change mechanisms of the heterogeneous droplet under the conditions of intensive heat exchange were determined.

Methods for characterizing human coronary artery deformation from cardiac-gated computed tomography data.

Accurate quantification of changes in length, curvature, and bifurcation angles of coronary arteries due to cardiac motion is important for the design of coronary stents. A new method is developed to describe the dynamic characteristics of the human coronary artery. From cardiac-gated computed tomography (CT) data, 3-D surface geometry and centerline paths of the coronary arteries were constructed. For quantification of strain and twisting deformation, 3-D distortion-free vessel straightening and landmark matching algorithms were developed to compute the relative translation and rotation of distal landmarks with respect to a proximal landmark. For quantification of bending deformation, change in curvature was measured by computing a best-fit torus in the region of interest within a coronary segment. The optimal torus parameters were estimated by minimizing the standard deviation of distances from the surface mesh to the centerline of the torus. The angle between branch vessels was measured using linear fitting of centroid sets from the cross-sectional vessel lumen. The proposed methods were verified using a software phantom and applied to two patient specific CT datasets. Vascular deformations derived from these methods can provide information for designing bench-top tests for endovascular devices that better replicate the in vivo environment, thereby improving device performance prediction and leading to more durable designs.