Roshan M. D'Souza

Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT images

Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decomposition based technique. Finally the denoised image is created by median filtering of the low-rank component of the processed data. Simultaneous decomposition and alignment of the images result in better performance in comparison to simple registration-based methods that are used in the literature for noise reduction of OCT images.

Study of contact angle hysteresis using the Cellular Potts Model

We use the Cellular Potts Model (CPM) to study the contact angle (CA) hysteresis in multiphase (solid-liquid-vapour) systems. We simulate a droplet over the tilted patterned surface, and a bubble placed under the surface immersed in liquid. The difference between bubbles and droplets was discussed through their CA hysteresis. Dependency of CA hysteresis on the surface structure and other parameters was also investigated. This analysis allows decoupling of the 1D (pinning of the triple line) and 2D (adhesion hysteresis in the contact area) effects and provides new insight into the nature of CA hysteresis.

Toward optimal spatial and spectral quality in widefield infrared spectromicroscopy of IR labelled single cells

Advancements in widefield infrared spectromicroscopy have recently been demonstrated following the commissioning of IRENI (InfraRed ENvironmental Imaging), a Fourier Transform infrared (FTIR) chemical imaging beamline at the Synchrotron Radiation Center. The present study demonstrates the effects of magnification, spatial oversampling, spectral pre-processing and deconvolution, focusing on the intracellular detection and distribution of an exogenous metal tris-carbonyl derivative 1 in a single MDA-MB-231 breast cancer cell. We demonstrate here that spatial oversampling for synchrotron-based infrared imaging is critical to obtain accurate diffraction-limited images at all wavelengths simultaneously. Resolution criteria and results from raw and deconvoluted images for two Schwarzschild objectives (36×, NA 0.5 and 74×, NA 0.65) are compared to each other and to prior reports for raster-scanned, confocal microscopes. The resolution of the imaging data can be improved by deconvolving the instrumental broadening that is determined with the measured PSFs, which is implemented with GPU programming architecture for fast hyperspectral processing. High definition, rapidly acquired, FTIR chemical images of respective spectral signatures of the cell 1 and shows that 1 is localized next to the phosphate- and Amide-rich regions, in agreement with previous infrared and luminescence studies. The infrared image contrast, localization and definition are improved after applying proven spectral pre-processing (principal component analysis based noise reduction and RMie scattering correction algorithms) to individual pixel spectra in the hyperspectral cube.

Accelerating the Gillespie Exact Stochastic Simulation Algorithm using hybrid parallel execution on graphics processing units.

The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to simulate reaction kinetics in situations where the concentration of the reactant is too low to allow deterministic techniques such as differential equations. The inherent limitations of the GSSA include the time required for executing a single run and the need for multiple runs for parameter sweep exercises due to the stochastic nature of the simulation. Even very efficient variants of GSSA are prohibitively expensive to compute and perform parameter sweeps. Here we present a novel variant of the exact GSSA that is amenable to acceleration by using graphics processing units (GPUs). We parallelize the execution of a single realization across threads in a warp (fine-grained parallelism). A warp is a collection of threads that are executed synchronously on a single multi-processor. Warps executing in parallel on different multi-processors (coarse-grained parallelism) simultaneously generate multiple trajectories. Novel data-structures and algorithms reduce memory traffic, which is the bottleneck in computing the GSSA. Our benchmarks show an 8×−120× performance gain over various state-of-the-art serial algorithms when simulating different types of models.

Parallelizing the Cellular Potts Model on graphics processing units

Abstract The Cellular Potts Model (CPM) is a lattice based modeling technique used for simulating cellular structures in computational biology. The computational complexity of the model means that current serial implementations restrict the size of simulation to a level well below biological relevance. Parallelization on computing clusters enables scaling the size of the simulation but marginally addresses computational speed due to the limited memory bandwidth between nodes. In this paper we present new data-parallel algorithms and data structures for simulating the Cellular Potts Model on graphics processing units. Our implementations handle most terms in the Hamiltonian, including cell–cell adhesion constraint, cell volume constraint, cell surface area constraint, and cell haptotaxis. We use fine level checkerboards with lock mechanisms using atomic operations to enable consistent updates while maintaining a high level of parallelism. A new data-parallel memory allocation algorithm has been developed to handle cell division. Tests show that our implementation enables simulations of > 10 6 cells with lattice sizes of up to 256 3 on a single graphics card. Benchmarks show that our implementation runs ∼80× faster than serial implementations, and ∼5× faster than previous parallel implementations on computing clusters consisting of 25 nodes. The wide availability and economy of graphics cards mean that our techniques will enable simulation of realistically sized models at a fraction of the time and cost of previous implementations and are expected to greatly broaden the scope of CPM applications.

Graphics processing unit based direct simulation Monte Carlo

The direct simulation Monte Carlo (DSMC) is a computational method for fluid mechanics simulation in the regime of rarefied gas flow. It is a numerical solution of the Boltzmann equation based on an individual particle basis. Accurate simulations typically require particle numbers in the range of hundreds of thousands to millions. Such large simulations require an inordinate amount of time for processing using serial computing on central processing units (CPUs). In this paper we investigate data-parallel techniques on graphics processing units (GPUs) to execute very large scale DSMC simulations. We have designed and implemented Bird’s method on a three-dimensional simulation domain that includes complex geometry interactions. We also have tested and verified the statistical and theoretical accuracy of our implementation. Our results show substantial performance improvements (nearly two orders of magnitude) over Bird’s serial implementation without loss of accuracy.

Data-parallel techniques for simulating a mega-scale agent-based model of systemic inflammatory response syndrome on graphics processing units

Agent-based modeling is increasingly being used for computer simulation of complex biological systems. An agent-based model (ABM) is a bottom-up simulation where the bulk dynamics of the model result from the local interactions of its individual constituents or agents. However, due to emergent qualities of ABMs, bulk behaviors may be sensitive to the size of the model as determined by the population of individuals. Therefore, in certain circumstances it may be critical to closely match the simulation size with the actual system. This may be particularly true in biological systems, where multiple large-scale heterogeneous populations can range into millions or even billions of individual cells/agents. Most existing ABM simulation toolkits are designed for serial computing and canno*t effectively simulate such mega-scale systems from a run-time standpoint. In this paper, we investigate data-parallel ABM implementations on graphics processing units to address the scalability issue of ABMs. As an example, we have implemented an abstracted version of the Systemic Inflammatory Response Syndrome ABM. We also implemented a serial version to confirm statistical accuracy. Our results show that parallelization on graphics processing units offers a substantial gain in performance without a loss in accuracy.

Fast k-NNG construction with GPU-based quick multi-select

In this paper, we describe a new brute force algorithm for building the -Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best algorithm. There are two main parts to the algorithm: the first part is finding the distances between the input vectors, which may be formulated as a matrix multiplication problem; the second is the selection of the k-NNs for each of the query vectors. For the second part, we describe a novel graphics processing unit (GPU)-based multi-select algorithm based on quick sort. Our optimization makes clever use of warp voting functions available on the latest GPUs along with user-controlled cache. Benchmarks show significant improvement over state-of-the-art implementations of the k-NN search on GPUs.

PI-FLAME

Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems, such as the human immune system. Previously, Folcik et al. (The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 2007; 4: 39) developed a Basic Immune Simulator (BIS) and implemented it using the Recursive Porous Agent Simulation Toolkit (RePast) ABM simulation framework. However, frameworks such as RePast are designed to execute serially on central processing units and therefore cannot efficiently handle large model sizes. In this paper, we report on our implementation of the BIS using FLAME GPU, a parallel computing ABM simulator designed to execute on graphics processing units. To benchmark our implementation, we simulate the response of the immune system to a viral infection of generic tissue cells. We compared our results with those obtained from the original RePast implementation for statistical accuracy. We observe that our implementation has a 13× performance advantage over the original RePast implementation.

Data-parallel algorithms for large-scale real-time simulation of the cellular potts model on graphics processing units

In the following paper we present techniques for data-parallel execution of the Cellular Potts Model (CPM) on Graphics Processing Units (GPUs). We have developed data-structures and algorithms that are optimized to use available hardware resources on the GPU. To the best of our knowledge, this is the first attempt at using data-parallel techniques for simulating the CPM. We benchmarked this implementation against other parallel CPM implementations using traditional CPU clusters. Experimental results demonstrate that this implementation solves many of the drawbacks of traditional CPU clusters, and results in a performance gain of up to 30x, without sacrificing the integrity of the original model.