Hardware/software 2D-3D backprojection on a SoPC platform

International audienceThe reduction of image reconstruction time is needed to spread the use of PET for research and routine clinical practice. In this purpose, this article presents a hardware/software architecture for the acceleration of 3D backprojection based upon an efficient 2D backprojection. This architecture has been designed in order to provide a high level of parallelism thanks to an efficient management of the memory accesses which would have been otherwise strongly slowed by the external memory. The reconstruction system is embedded in a SoPC platform (System on Programmable Chip), the new generation of reconfigurable circuit. The originality of this architecture comes from the design of a 2D Adaptative and Predictive Cache (2D-AP Cache) which has proved to be an efficient way to overcome the memory access bottleneck. Thanks to a hierarchical use of this cache, several backprojection operators can run in parallel, accelerating in this manner noteworthy the reconstruction process. This 2D reconstruction system will next be used to speed up 3D image reconstruction

A GPU-based Implementation for Improved Online Rebinning Performance in Clinical 3-D PET

Online rebinning is an important and well-established technique for reducing the time required to process Positron Emission Tomography data. However, the need for efficient data processing in a clinical setting is growing rapidly and is beginning to exceed the capability of traditional online processing methods. High-count rate applications such as Rubidium 3-D PET studies can easily saturate current online rebinning technology. Realtime processing at these high-count rates is essential to avoid significant data loss. In addition, the emergence of time-of-flight (TOF) scanners is producing very large data sets for processing. TOF applications require efficient online Rebinning methods so as to maintain high patient throughput. Currently, new hardware architectures such as Graphics Processing Units (GPUs) are available to speedup data parallel and number crunching algorithms. In comparison to the usual parallel systems, such as multiprocessor or clustered machines, GPU hardware can be much faster and above all, it is significantly cheaper. The GPUs have been primarily delivered for graphics for video games but are now being used for High Performance computing across many domains. The goal of this thesis is to investigate the suitability of the GPU for PET rebinning algorithms

Wavelet-Based Volume Rendering

Various biomedical technologies like CT, MRI and PET scanners provide detailed cross-sectional views of the human anatomy. The image information obtained from these scanning devices is typically represented as large data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes. As these data sets cannot be stored on one\u27s local hard drive, SDSC provides a large data repository to store such data sets. These data sets need to be accessed by researchers around the world to collaborate in their research. But the size of these data sets make them difficult to be transmitted over the current network. This thesis presents a 3-D Haar wavelet algorithm which enables these data sets to be transformed into smaller hierarchical representations. These transformed data sets are transmitted over the network and reconstructed to a 3-D volume on the client\u27s side through progressive refinement of the images and 3-D texture mapping techniques

dOpenCL: Towards a Uniform Programming Approach for Distributed Heterogeneous Multi-/Many-Core Systems

Modern computer systems are becoming increasingly heterogeneous by comprising multi-core CPUs, GPUs, and other accelerators. Current programming approaches for such systems usually require the application developer to use a combination of several programming models (e. g., MPI with OpenCL or CUDA) in order to exploit the full compute capability of a system. In this paper, we present dOpenCL (Distributed OpenCL) – a uniform approach to programming distributed heterogeneous systems with accelerators. dOpenCL extends the OpenCL standard, such that arbitrary computing devices installed on any node of a distributed system can be used together within a single application. dOpenCL allows moving data and program code to these devices in a transparent, portable manner. Since dOpenCL is designed as a fully-fledged implementation of the OpenCL API, it allows running existing OpenCL applications in a heterogeneous distributed environment without any modifications. We describe in detail the mechanisms that are required to implement OpenCL for distributed systems, including a device management mechanism for running multiple applications concurrently. Using three application studies, we compare the performance of dOpenCL with MPI+OpenCL and a standard OpenCL implementation

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Efficient reconfigurable architectures for 3D medical image compression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE), Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci

Optical simulation study for high resolution monolithic detector design for TB-PET

Background The main limitations in positron emission tomography (PET) are the limited sensitivity and relatively poor spatial resolution. The administered radioactive dose and scan time could be reduced by increasing system sensitivity with a total-body (TB) PET design. The second limitation, spatial resolution, mainly originates from the specific design of the detectors that are implemented. In state-of-the-art scanners, the detectors consist of pixelated crystal arrays, where each individual crystal is isolated from its neighbors with reflector material. To obtain higher spatial resolution the crystals can be made narrower which inevitably leads to more inter-crystal scatter and larger dead space between the crystals. A monolithic detector design shows superior characteristics in (i) light collection efficiency (no gaps), (ii) timing, as it significantly reduces the number of reflections and therefore the path length of each scintillation photon and (iii) spatial resolution (including better depth-of-interaction (DOI)). The aim of this work is to develop a precise simulation model based on measured crystal data and use this powerful tool to find the limits in spatial resolution for a monolithic detector for the use in TB-PET. Materials and methods A detector (Fig. 1) based on a monolithic 50x50x16 mm3 lutetium-(yttrium) oxyorthosilicate (L(Y)SO) scintillation crystal coupled to an 8x8 array of 6x6mm2 silicon photomultipliers (SiPMs) is simulated with GATE. A recently implemented reflection model for scintillation light allows simulations based on measured surface data (1). The modeled surfaces include black painted rough finishing on the crystal sides (16x50mm2) and a specular reflector attached to a polished crystal top (50x50mm2). Maximum Likelihood estimation (MLE) is used for positioning the events. Therefore, calibration data is obtained by generating 3.000 photo-electric events at given calibration positions (Fig. 1). Compton scatter is not (yet) included. In a next step, the calibration data is organized in three layers based on the exact depth coordinate in the crystal (i.e. DOI assumed to be known). For evaluating the resolution, the full width at half maximum (FWHM) is estimated at the irradiated positions of Fig. 2 as a mean of all profiles in vertical and horizontal direction. Next, uniformity is evaluated by simulating 200k events from a flood source, placed in the calibrated area. Results For the irradiation pattern in Fig. 2 the resolution in terms of FWHM when applying MLE is: 0.86±0.13mm (Fig. 3a). Nevertheless, there are major artifacts also at non-irradiated positions. By positioning the events based on three DOI-based layers it can be seen that the events closest to the photodetector introduce the largest artifacts (Fig. 3b-d). The FWHM improves for Layer 1 and 2, to 0.69±0.04mm and 0.59±0.02mm, respectively. Layer 3 introduces major artifacts to the flood map, as events are positioned at completely different locations as the initial irradiation. A FWHM estimation is thus not useful. The uniformity (Fig. 4) degrades with proximity to the photodetector. The map in Fig. 4c shows that the positioning accuracy depends not only on DOI but also the position in the plane parallel to the photodetector array. Conclusions A simulation model for a monolithic PET detector with good characteristics for TB-PET systems was developed with GATE. A first estimate of the spatial resolution and uniformity was given, pointing out the importance of depth-dependent effects. Future studies will include several steps towards more realistic simulations e.g. surface measurements of our specific crystals for the optical surface model and inclusion of the Compton effect