Real-time 3D cone beam reconstruction (original) (raw)
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Evaluation of Parallel Approaches for 3D Cone-Beam Reconstruction
The problem of reconstructing a 3D image from a set of its 2D conic projections from di erent angles of view arises in medical imaging for 3D tomography. 3D tomography requires voluminous data and long computation times. In this study, we present the implementation of some 3D reconstruction methods (Feldkamp algorithm, Bloc ART and SIRT algorithm), using di erents approachs on SIMD 1 and MIMD 2 computers. We evaluate the theoretical complexities and present experimental computation times for the di erent implementations. The scalability of the implementations is very good because we obtain an e ciency equal to 3.7 for an T3D implementation.
A fast and high-quality cone beam reconstruction pipeline using the GPU
Medical Imaging 2007: Physics of Medical Imaging, 2007
Cone beam scanners have evolved rapidly in the past years. Increasing sampling resolution of the projection images and the desire to reconstruct high resolution output volumes increases both the memory consumption and the processing time considerably. In order to keep the processing time down new strategies for memory management are required as well as new algorithmic implementations of the reconstruction pipeline. In this paper, we present a fast and high-quality cone beam reconstruction pipeline using the Graphics Processing Unit (GPU). This pipeline includes the backprojection process and also pre-filtering and post-filtering stages. In particular, we focus on a subset of five stages, but more stages can be integrated easily. In the pre-filtering stage, we first reduce the amount of noise in the acquired projection images by a non-linear curvature-based smoothing algorithm. Then, we apply a high-pass filter as required by the inverse Radon transform. Next, the backprojection pass reconstructs a raw 3D volume. In post-processing, we first filter the volume by a ring artifact removal. Then, we remove cupping artifacts by our novel uniformity correction algorithm. We present the algorithm in detail. In order to execute the pipeline as quickly as possible we take advantage of GPUs that have proven to be very fast parallel processors for numerical problems. Unfortunately, both the projection images and the reconstruction volume are too large to fit into 512 MB of GPU memory. Therefore, we present an efficient memory management strategy that minimizes the bus transfer between main memory and GPU memory. Our results show a 4 times performance gain over a highly optimized CPU implementation using SSE2/3 commands. At the same time, the image quality is comparable to the CPU results with an average per pixel difference of 10 −5 .
Accelerated cone-beam backprojection using GPU-CPU hardware
Proceedings of the 9th …, 2007
The three-dimensional image reconstruction process used in interventional CT imaging is computationally demanding. Implementation on general-purpose computational platforms requires substantial processing time, which is undesirable during time-critical surgical and minimally invasive procedures. Central and Graphics Processing Units (CPUs and GPUs, respectively) have been studied as a platform to accelerate 3-D imaging. GPU devices offer a programmable hardware architecture, suitable for pipelining and high levels of parallel processing to increase computational throughput, as well as the benefits of being off-the-shelf and effectively scalable solutions. The focus of this paper is on the backprojection step of the image reconstruction process, since it is the most computationally intensive part. Using the modified Feldkamp-Davis-Kress (FDK) cone-beam algorithm, our feasibility studies indicate the entire 512 3 image reconstruction on a mobile X-ray C-arm can be accelerated to real time (i.e. completed immediately after an exposure scan of 15-30 seconds duration).
Parallel image reconstruction on MIMD computers for three-dimensional cone-beam tomography
Parallel Computing, 1998
This paper deals with the parallel implementation of reconstruction methods in three-dimensional (3D) cone-beam tomography. Indeed, 3D reconstruction of realistic size volumes requires huge computer resources due to the voluminous data to handle, and the long processing times. Parallel computing, on MIMD computers, seems to be a good approach to manage this problem. In this study, we present dierent solutions for the parallelization of three conventional reconstruction methods, usable on a class of parallel computers. Depending on the method, we consider two approaches referred as a local and a global approach. Performance results obtained on ®ve dierent MIMD computers are reported. Two 3D images reconstructed from physical data acquired with the``Morphometer'' are presented.
Cone based 3D reconstruction: a FDK-SART comparison for limited number of projections
2001
3D Cone Beam Computed Tomography is a highly investigated field, nowadays. Both analytical and iterative methods for tomographic reconstruction have been proposed. This paper compares two such CBCT image reconstruction algorithms, the FDK and the SART, in terms of their performances when a limited number of projections is used. Increased angular step between projections, allows dose reduction and shorter acquisition time. Although more computationally demanding, given the constant development of computer power, SART could be a method of interest for cone beam reconstruction using a limited number of projections.
Evaluation of state-of-the-art hardware architectures for fast cone-beam CT reconstruction
Parallel Computing, 2012
We present an evaluation of state-of-the-art computer hardware architectures for implementing the FDK method, which solves the 3-D image reconstruction task in cone-beam computed tomography (CT). The computational complexity of the FDK method prohibits its use for many clinical applications unless appropriate hardware acceleration is employed. Today's most powerful hardware architectures for high-performance computing applications are based on standard multi-core processors, off-the-shelf graphics boards, the Cell Broadband Engine Architecture (CBEA), or customized accelerator platforms (e.g., FPGA-based computer components).
Cutting Voxel Projector a New Approach to Construct 3D Cone Beam CT Operator
ArXiv, 2021
In this paper, we introduce a new class of projectors for 3D cone beam tomographic reconstruction. We find analytical formulas for the relationship between the voxel volume projected onto a given detector pixel and its contribution to the extinction value detected on that pixel. Using this approach, we construct a near-exact projector and backprojector that can be used especially for algebraic reconstruction techniques. We have implemented this cutting voxel projector and a less accurate, speed-optimized version of it together with two established projectors, a ray tracing projector based on Siddon’s algorithm and a TT footprint projector. We show that the cutting voxel projector achieves, especially for large cone beam angles, noticeably higher accuracy than the TT projector. Moreover, our implementation of the relaxed version of the cutting voxel projector is significantly faster than current footprint projector implementations. We further show that Siddon’s algorithm with compara...
The Generalized Back Projection Theorem for Cone Beam Reconstruction
IEEE Transactions on Nuclear Science, 2000
The use of cone beam scanners raises the problem of three dimensional reconstruction from divergent projections. After a survey on bidimensional analytical reconstruction methods we examine their application to the 3D problem. Finally, it is shown that the back projection theorem can be generalized to cone beam projections. This allows to state a new inversion formula suitable for both the 4 rr parallel and divergent geometries. It leads to the generalization of the "rho-f iltered back projection " algorithm which is outlined.
Medical Imaging 2016: Physics of Medical Imaging, 2016
Modern image reconstruction algorithms rely on projection and back-projection operations to refine an image estimate in iterative image reconstruction. A widely-used state-of-the-art technique is distance-driven projection and backprojection. While the distance-driven technique yields superior image quality in iterative algorithms, it is a computationally demanding process. This has a detrimental effect on the relevance of the algorithms in clinical settings. A few methods have been proposed for enhancing the distance-driven technique in order to take advantage of modern computer hardware. This paper explores a two-dimensional extension of the branchless method proposed by Samit Basu and Bruno De Man. The extension of the branchless method is named "pre-integration" because it gets a performance boost by integrating the data before the projection and back-projection operations. It was written with NVIDIA's CUDA platform and carefully designed for massively parallel GPUs. The performance and the image quality of the preintegration method were analyzed. Both projection and back-projection are significantly faster with pre-integration. The image quality was analyzed using cone beam image reconstruction algorithms within Jeffrey Fessler's Image Reconstruction Toolbox. Images produced from regularized, iterative image reconstruction algorithms using the preintegration method show no significant impacts to image quality.