24 research outputs found
Highly parallel Monte-Carlo simulations of the acousto-optic effect in heterogeneous turbid media
The development of a highly parallel simulation of the acousto-optic effect is detailed. The simulation supports optically heterogeneous simulation domains under insonification by arbitrary monochromatic ultrasound fields. An adjoint method for acousto-optics is proposed to permit point-source/point-detector simulations. The flexibility and efficiency of this simulation code is demonstrated in the development of spatial absorption sensitivity maps which are in broad agreement with current experimental investigations. The simulation code has the potential to provide guidance in the feasibility and optimization of future studies of the acousto-optic technique, and its speed may permit its use as part of an iterative inversion model
Next-generation acceleration and code optimization for light transport in turbid media using GPUs
A highly optimized Monte Carlo (MC) code package for simulating light
transport is developed on the latest graphics processing unit (GPU) built
for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical
optics, the MC method is the gold standard approach for simulating light
transport in biological tissue, both due to its accuracy and its flexibility
in modelling realistic, heterogeneous tissue geometry in 3-D. However, the
widespread use of MC simulations in inverse problems, such as treatment
planning for PDT, is limited by their long computation time. Despite its
parallel nature, optimizing MC code on the GPU has been shown to be a
challenge, particularly when the sharing of simulation result matrices among
many parallel threads demands the frequent use of atomic instructions to
access the slow GPU global memory. This paper proposes an optimization
scheme that utilizes the fast shared memory to resolve the performance
bottleneck caused by atomic access, and discusses numerous other
optimization techniques needed to harness the full potential of the GPU.
Using these techniques, a widely accepted MC code package in biophotonics,
called MCML, was successfully accelerated on a Fermi GPU by approximately
600x compared to a state-of-the-art Intel Core i7 CPU. A skin model
consisting of 7 layers was used as the standard simulation geometry. To
demonstrate the possibility of GPU cluster computing, the same GPU code was
executed on four GPUs, showing a linear improvement in performance with an
increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML,
is compatible with a wide range of graphics cards and is released as an
open-source software in two versions: an optimized version tuned for high
performance and a simplified version for beginners (http://code.google.com/p/gpumcml)
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In-silico investigation towards the non-invasive optical detection of blood lactate
This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood
System Optimization and Iterative Image Reconstruction in Photoacoustic Computed Tomography for Breast Imaging
Photoacoustic computed tomography(PACT), also known as optoacoustic tomography (OAT), is an emerging imaging technique that has developed rapidly in recent years. The combination of the high optical contrast and the high acoustic resolution of this hybrid imaging technique makes it a promising candidate for human breast imaging, where conventional imaging techniques including X-ray mammography, B-mode ultrasound, and MRI suffer from low contrast, low specificity for certain breast types, and additional risks related to ionizing radiation. Though significant works have been done to push the frontier of PACT breast imaging, it is still challenging to successfully build a PACT breast imaging system and apply it to wide clinical use because of various practical reasons. First, computer simulation studies are often conducted to guide imaging system designs, but the numerical phantoms employed in most previous works consist of simple geometries and do not reflect the true anatomical structures within the breast. Therefore the effectiveness of such simulation-guided PACT system in clinical experiments will be compromised. Second, it is challenging to design a system to simultaneously illuminate the entire breast with limited laser power. Some heuristic designs have been proposed where the illumination is non-stationary during the imaging procedure, but the impact of employing such a design has not been carefully studied. Third, current PACT imaging systems are often optimized with respect to physical measures such as resolution or signal-to-noise ratio (SNR). It would be desirable to establish an assessing framework where the detectability of breast tumor can be directly quantified, therefore the images produced by such optimized imaging systems are not only visually appealing, but most informative in terms of the tumor detection task. Fourth, when imaging a large three-dimensional (3D) object such as the breast, iterative reconstruction algorithms are often utilized to alleviate the need to collect densely sampled measurement data hence a long scanning time. However, the heavy computation burden associated with iterative algorithms largely hinders its application in PACT breast imaging. This dissertation is dedicated to address these aforementioned problems in PACT breast imaging. A method that generates anatomically realistic numerical breast phantoms is first proposed to facilitate computer simulation studies in PACT. The non-stationary illumination designs for PACT breast imaging are then systematically investigated in terms of its impact on reconstructed images. We then apply signal detection theory to assess different system designs to demonstrate how an objective, task-based measure can be established for PACT breast imaging. To address the
slow computation time of iterative algorithms for PACT imaging, we propose an acceleration method that employs an approximated but much faster adjoint operator during iterations, which can reduce the computation time by a factor of six without significantly compromising image quality. Finally, some clinical results are presented to demonstrate that the PACT breast imaging can resolve most major and fine vascular structures within the breast, along with some pathological biomarkers that may indicate tumor development
Adaptive proton radiation therapy via fast Monte Carlo dose calculation to correct for inter- and intra-fraction motion and geometry changes
Intensity modulated proton therapy (IMPT) plans precisely balance thousands of proton beamlets, giving high dose to the tumor while trying to spare healthy tissues. However, plan quality is affected by factors including: 1) dose calculation inaccuracies, 2) underestimation of the biological effect of the dose in sensitive areas and geometrical changes like 3) patient movement or 4) changes in posture and anatomy. All these factors are addressed in the projects here presented.
Project 1, in collaboration, introduces an upgraded version of a Monte Carlo package for graphics processing units (GPU-MC) to provide fast and accurate dose calculations. This package is extended to serve as the unique dose calculation engine in the following projects.
Project 2, in collaboration, presents a prioritized optimization method to reduce the potential biological effect of the radiation in organs at risk near the tumor.
Project 3 compares computationally efficient strategies to take into account the patient respiratory motion by defining planning target volumes based on a 4DCT of the patient. Density overwrites considering water-equivalent-path-length to voxels across the 4DCT
targets works best.
Project 4 demonstrates an online algorithm that maintains IMPT plan quality through treatment, adapting it to the daily patient posture and anatomy using GPU-MC calculations
Simulated Annealing
The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine
Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science
This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine
Uncertainty quantification and numerical methods in charged particle radiation therapy
Radiation therapy is applied in approximately 50% of all cancer treatments. To eliminate the tumor
without damaging organs in the vicinity, optimized treatment plans are determined. This requires the
calculation of three-dimensional dose distributions in a heterogeneous volume with a spatial resolution of 2-3mm. Current planning techniques use multiple beams with optimized directions and energies to
achieve the best possible dose distribution. Each dose calculation however requires the discretization of
the six-dimensional phase space of the linear Boltzmann transport equation describing complex particle
dynamics. Despite the complexity of the problem, dose calculation errors of less than 2% are clinically
recommended and computation times cannot exceed a few minutes. Additionally, the treatment reality often differs from the computed plan due to various uncertainties, for example in patient
positioning, the acquired CT image or the delineation of tumor and organs at risk. Therefore, it is essential to include uncertainties in the planning process to determine a robust treatment plan. This entails a realistic mathematical model of uncertainties, quantification of their effect on the dose distribution using appropriate propagation methods as well as a robust or probabilistic optimization of treatment parameters to account for these effects.
Fast and accurate calculations of the dose distribution including predictions of uncertainties in the computed dose are thus crucial for the determination of robust treatment plans in radiation therapy. Monte Carlo methods are often used to solve transport problems, especially for applications that require high accuracy. In these cases, common non-intrusive uncertainty propagation strategies that involve repeated simulations of the problem at different points in the parameter space quickly become infeasible due to their long run-times. Quicker deterministic dose calculation methods allow for better
incorporation of uncertainties, but often use strong simplifications or admit non-physical solutions and
therefore cannot provide the required accuracy.
This work is concerned with finding efficient mathematical solutions for three aspects of (robust) radiation therapy planning: 1. Efficient particle transport and dose calculations, 2. uncertainty modeling and propagation for radiation therapy, and 3. robust optimization of the treatment set-up