Ayman M. Eldeib

High performance multi-dimensional (2D/3D) FFT-Shift implementation on Graphics Processing Units (GPUs)

Frequency domain analysis is one of the most common analysis techniques in signal and image processing. Fast Fourier Transform (FFT) is a well know tool used to perform such analysis by obtaining the frequency spectrum for time- or spatial-domain signals and vice versa. FFT-Shift is a subsequent operation used to handle the resulting arrays from this stage as it centers the DC component of the resulting array at the origin of the spectrum. The modern Graphics Processing Units (GPUs) can be easily exploited to efficiently execute this operation considering the Compute Unified Device Architecture (CUDA) technology that was released by NVIDIA. In this work, we present an efficient high performance implementation for two- and three-dimensional FFT-Shift on the GPU exploiting its highly parallel architecture relying on the CUDA platform. We use Fourier volume rendering as an example to demonstrate the significance of this proposed implementation. It achieves a speedup of 65X for the 2D case & 219X for the 3D case.

Identification of rheumatoid arthritis biomarkers based on single nucleotide polymorphisms and haplotype blocks: A systematic review and meta-analysis

Graphical abstract

Genetic Case-Control Study for Eight Polymorphisms Associated with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socio-economic impact. The aim of the current study was to investigate eight candidate RA susceptibility loci to identify the associated variants in Egyptian population. Eight single nucleotide polymorphisms (SNPs) (MTHFR—C677T and A1298C, TGFβ1 T869C, TNFB A252G, and VDR—ApaI, BsmI, FokI, and TaqI) were tested by genotyping patients with RA (n = 105) and unrelated controls (n = 80). Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium (LD) between the VDR SNPs was measured to detect any indirect association. By comparing RA patients with controls (TNFB, BsmI, and TaqI), SNPs were associated with RA using all models. MTHFR C677T was associated with RA using all models except the recessive model. TGFβ1 and MTHFR A1298C were associated with RA using the dominant and the co-dominant models. The recessive model represented the association for ApaI variant. There were no significant differences for FokI and the presence of RA disease by the used models examination. For LD results, There was a high D′ value between BsmI and FokI (D′ = 0.91), but the r2 value between them was poor. All the studied SNPs may contribute to the susceptibility of RA disease in Egyptian population except for FokI SNP.

High performance GPU-Based fourier volume rendering

Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result of its 𝒪(N 2log⁡N) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are 𝒪(N 3) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.

Constructing a functional Fourier volume rendering pipeline on heterogeneous platforms

Volume rendering became a crucial and significant tool in the medical field. This was due to the rapid evolution of imaging modalities such as Magnetic Resonance Imaging (MRI) machines, Computed Tomography (CT) scanners. Volume rendering techniques vary from category to another depending basically on the employed modality and eventually on the target application. Although spatial-domain volume rendering techniques have gained a wide acceptance during the past decades, however, they suffer from performance bottlenecks due to their O(N3) complexity for a volume of size N3. Fourier Volume Rendering (FVR) is an alternative technique that works on the k-space representation of the volume with reduced complexity of O(N2logN) for generating projection images of the spatial volume that look like X-ray ones. In this work, a functional hybrid implementation of the FVR pipeline on Central Processing Units (CPUs) and Graphics Processing Units (GPUs) is presented.

GPU acceleration for digitally reconstructed radiographs using bindless texture objects and CUDA/OpenGL interoperability.

This paper features an advanced implementation of the X-ray rendering algorithm that harnesses the giant computing power of the current commodity graphics processors to accelerate the generation of high resolution digitally reconstructed radiographs (DRRs). The presented pipeline exploits the latest features of NVIDIA Graphics Processing Unit (GPU) architectures, mainly bindless texture objects and dynamic parallelism. The rendering throughput is substantially improved by exploiting the interoperability mechanisms between CUDA and OpenGL. The benchmarks of our optimized rendering pipeline reflect its capability of generating DRRs with resolutions of 20482 and 40962 at interactive and semi interactive frame-rates using an NVIDIA GeForce 970 GTX device.

Accelerating DRR generation using Fourier slice theorem on the GPU.

Digitally Reconstructed Radiographs (DRRs) play a vital role in medical imaging procedures and radiotherapy applications. They allow the continuous monitoring of patient positioning during image guided therapies using multi-dimensional image registration. Conventional generation of DRRs using spatial domain algorithms such as ray casting is associated with computational complexity of O(N3). Fourier slice theorem is an alternative approach for generating the DRRs in the k-space with reduced time complexity. In this work, we present a high performance, scalable, and optimized DRR generation pipeline on the Graphics Processing Unit (GPU). The strong scaling performance of the presented pipeline is investigated and demonstrated using two contemporary GPUs. Our pipeline is capable of generating DRRs for 5123 volumes in less than a milli-second.

Offline large scale fourier volume rendering on low-end hardware

The resolution of medical data sets acquired by state-of-the-art imaging modalities is growing rapidly. Providing high-end workstations to visualize such data sets might not be affordable in some cases. To resolve this issue, there should be alternative convenient software solutions to handle huge data sets either by out-of-core or offline rendering applications. This presented work features an offline rendering pipeline that is capable of rendering digital x-ray radiographs of very large scale volumetric data sets, which cannot fit into the memory of the running platform relying on spatial-domain decomposition and projection-slice theorem.

MATLAB-based fourier volume rendering framework

Volume rendering plays a significant role in medical imaging. It allows exploring the internal structures of volumetric data acquired by the different imaging modalities. This exploration allows accurate diagnosis and consequently effective treatment. Various volume rendering techniques exists. However, compared to other techniques, Fourier volume rendering has gained a wide acceptance by the radiologists for several reasons. This technique generates attenuation renderings similar to x-ray radiographs that are well interpreted by the physicians. Additionally, it works with time complexity of O(N2logN), and thus, it delivers interactive frame rates for large scale medical volumes in comparison with spatial-domain rendering techniques. The complexity associated with developing a basic rendering pipeline for this technique hinders medical imaging scientists from focusing their research on investigating new algorithms for improving the reconstruction quality of the resulting digital radiographs. In this paper, we present a flexible, extensible and semi-interactive high-level MATLAB-based framework for the Fourier volume rendering pipeline.

Motor imagery based brain computer interface using transform domain features

Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.