Holger Scherl

Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA)

The Common Unified Device Architecture (CUDA) is a fundamentally new programming approach making use of the unified shader design of the most current Graphics Processing Units (CPUs) from NVIDIA. The programming interface allows to implement an algorithm using standard C language and a few extensions without any knowledge about graphics programming using OpenGL, DirectX, and shading languages. We apply this revolutionary new technology to the FDK method, which solves the three-dimensional reconstruction task in cone-beam CT. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. Todays CPUs with their high level of parallelism are cost-efficient processors for performing the FDK reconstruction according to medical requirements. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CUDA architecture. Our implementation approach further allows to do an on-the-fly- reconstruction, which means that the reconstruction is completed right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device. Finally, we compare our results to our highly optimized FDK implementation on the Cell Broadband Engine Architecture (CBEA), both with respect to reconstruction speed and implementation effort.

Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets

We present a new level-set based method to segment and quantify stenosed internal carotid arteries (ICAs) in 3D contrast-enhanced computed tomography angiography (CTA). Within these data sets it is a difficult task to evaluate the degree of stenoses deterministically even for the experienced physician because the actual vessel lumen is hardly distinguishable from calcified plaque and there is no sharp border between lumen and arterial wall. According to our knowledge no commercially available software package allows the detection of the boundary between lumen and plaque components. Therefore in the clinical environment physicians have to perform the evaluation manually. This approach suffers from both intra- and inter-observer variability. The limitation of the manual approach requires the development of a semi-automatic method that is able to achieve deterministic segmentation results of the internal carotid artery via level-set techniques. With the new method different kinds of plaques were almost completely excluded from the segmented regions. For an objective evaluation we also studied the methods performance with four different phantom data sets for which the ground truth of the degree of stenosis was known a priori. Finally, we applied the method to 10 ICAs and compared the obtained segmentations with manual measurements of three physicians.

Implementation of the FDK algorithm for cone-beam CT on the cell broadband engine architecture

In most of todays commercially available cone-beam CT scanners, the well known FDK method is used for solving the 3D reconstruction task. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. The brand-new Cell Broadband Engine Architecture (CBEA) with its high level of parallelism is a cost-efficient processor for performing the FDK reconstruction according to the medical requirements. The programming scheme, however, is quite different to any standard personal computer hardware. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CBEA. Our software framework allows to compute the filtering and back-projection in parallel, making it possible to do an on-the-fly-reconstruction. The achieved results demonstrate that a complete FDK reconstruction is computed with the CBEA in less than seven seconds for a standard clinical scenario. Given the fact that scan times are usually much higher, we conclude that reconstruction is finished right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device.

Design and implementation of the software architecture for a 3-D reconstruction system in medical imaging

The design and implementation of the reconstruction system in medical X-ray imaging is a challenging issue due to its immense computational demands. In order to ensure an efficient clinical workflow it is inevitable to meet high performance requirements. Hence, the usage of hardware acceleration is mandatory. The software architecture of the reconstruction system is required to be modular in a sense that different accelerator hardware platforms are supported and it must be possible to implement different parts of the algorithm using different acceleration architectures and techniques. This paper introduces and discusses the design of a software architecture for an image reconstruction system that meets the aforementioned requirements. We implemented a multi-threaded software framework that combines two software design patterns: the pipeline and the master/worker pattern. This enables us to take advantage of the parallelism in off-the-shelf accelerator hardware such as multi-core systems, the Cell processor, and graphics accelerators in a very flexible and reusable way.

Beam Hardening Correction with an Iterative Scheme Using an Exact Backward Projector and a Polychromatic Forward Projector

In computed tomography (CT), reconstructions from cone-beam (CB) data acquired with a polychromatic X-ray device show so called beam hardening artifacts. Beam hardening artifacts are highly undesirable for a medical diagnosis, because details in the reconstructed image are severely disturbed or completely lost. In this work, we demonstrate the significant reduction of beam hardening artifacts by using an iterative reconstruction scheme which consists of a backward and a forward projector. For the backward projector, an exact reconstruction approach was used. The forward projector was extended by a polychromatic model to mimic a realistic X-ray device. The presented experiments use simulated CB data to restrict the evaluation to beam hardening artifacts. The discussion is focused on CB data acquired along a helical trajectory.