1,916 research outputs found
An optimized ultrasound detector for photoacoustic breast tomography
Photoacoustic imaging has proven to be able to detect vascularization-driven
optical absorption contrast associated with tumors. In order to detect breast
tumors located a few centimeter deep in tissue, a sensitive ultrasound detector
is of crucial importance for photoacoustic mammography. Further, because the
expected photoacoustic frequency bandwidth (a few MHz to tens of kHz) is
inversely proportional to the dimensions of light absorbing structures (0.5 to
10+ mm), proper choices of materials and their geometries, and proper
considerations in design have to be made for optimal photoacoustic detectors.
In this study, we design and evaluate a specialized ultrasound detector for
photoacoustic mammography. Based on the required detector sensitivity and its
frequency response, a selection of active material and matching layers and
their geometries is made leading to a functional detector models. By iteration
between simulation of detector performances, fabrication and experimental
characterization of functional models an optimized implementation is made and
evaluated. The experimental results of the designed first and second functional
detectors matched with the simulations. In subsequent bare piezoelectric
samples the effect of lateral resonances was addressed and their influence
minimized by sub-dicing the samples. Consequently, using simulations, the final
optimized detector could be designed, with a center frequency of 1 MHz and a -6
dB bandwidth of ~80%. The minimum detectable pressure was measured to be 0.5
Pa, which will facilitate deeper imaging compared to the currrent systems. The
detector should be capable of detecting vascularized tumors with resolution of
1-2 mm. Further improvements by proper electrical grounding and shielding and
implementation of this design into an arrayed detector will pave the way for
clinical applications of photoacoustic mammography.Comment: Accepted for publication in Medical Physics (American Association of
Physicists in Medicine
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Tumor growth prediction, a highly challenging task, has long been viewed as a
mathematical modeling problem, where the tumor growth pattern is personalized
based on imaging and clinical data of a target patient. Though mathematical
models yield promising results, their prediction accuracy may be limited by the
absence of population trend data and personalized clinical characteristics. In
this paper, we propose a statistical group learning approach to predict the
tumor growth pattern that incorporates both the population trend and
personalized data, in order to discover high-level features from multimodal
imaging data. A deep convolutional neural network approach is developed to
model the voxel-wise spatio-temporal tumor progression. The deep features are
combined with the time intervals and the clinical factors to feed a process of
feature selection. Our predictive model is pretrained on a group data set and
personalized on the target patient data to estimate the future spatio-temporal
progression of the patient's tumor. Multimodal imaging data at multiple time
points are used in the learning, personalization and inference stages. Our
method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on
a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD
13.9% +- 9.8% obtained by a previous state-of-the-art model-based method
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis
Nonlinear optimization for a tumor invasion PDE model
In this work, we introduce a methodology to approximate unknown parameters that appear on a non-linear reaction–diffusion model of tumor invasion. These equations consider that tumor-induced alteration of micro-environmental pH furnishes a mechanism for cancer invasion. A coupled system reaction–diffusion explaining this model is given by three partial differential equations for the non-dimensional spatial distribution and temporal evolution of the density of normal tissue, the neoplastic tissue growth and the excess concentration of H ++ ions. The tumor model parameters have a corresponding biological meaning: the reabsorption rate, the destructive influence of H ++ ions in the healthy tissue, the growth rate of tumor tissue and the diffusion coefficient. We propose to solve the direct problem using the Finite Element Method (FEM) and minimize an appropriate functional including both the real data (obtained via in-vitro experiments and fluorescence ratio imaging microscopy) and the numerical solution. The gradient of the functional is computed by the adjoint method.Fil: Quiroga, Andrés Agustin Ignacio. Comision Nacional de Energia Atomica. Gerencia de Area de Aplicaciones de la Tecnología Nuclear. Gerencia de Investigación Aplicada. Grupo de Mecanica Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, German Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: Fernández Ferreyra, Damián Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Turner, Cristina Vilma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentin
Two-dimensional Tissue Image Reconstruction Based on Magnetic Field Data
This paper introduces new possibilities within two-dimensional reconstruction of internal conductivity distribution. In addition to the electric field inside the given object, the injected current causes a magnetic field which can be measured either outside the object by means of a Hall probe or inside the object through magnetic resonance imaging. The Magnetic Resonance method, together with Electrical impedance tomography (MREIT), is well known as a bio-imaging modality providing cross-sectional conductivity images with a good spatial resolution from the measurements of internal magnetic flux density produced by externally injected currents. A new algorithm for the conductivity reconstruction, which utilizes the internal current information with respect to corresponding boundary conditions and the external magnetic field, was developed. A series of computer simulations has been conducted to assess the performance of the proposed algorithm within the process of estimating electrical conductivity changes in the lungs, heart, and brain tissues captured in two-dimensional piecewise homogeneous chest and head models. The reconstructed conductivity distribution using the proposed method is compared with that using a conventional method based on Electrical Impedance Tomography (EIT). The acquired experience is discussed and the direction of further research is proposed
Serial FEM/XFEM-Based Update of Preoperative Brain Images Using Intraoperative MRI
Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images
Phenomenological model of diffuse global and regional atrophy using finite-element methods
The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and
Grid simulation services for the medical community
The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services
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