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Evaluation of a multicore-optimized implementation for tomographic reconstruction.

ABSTRACT: Tomography allows elucidation of the three-dimensional structure of an object from a set of projection images. In life sciences, electron microscope tomography is providing invaluable information about the cell structure at a resolution of a few nanometres. Here, large images are required to combine wide fields of view with high resolution requirements. The computational complexity of the algorithms along with the large image size then turns tomographic reconstruction into a computationally demanding problem. Traditionally, high-performance computing techniques have been applied to cope with such demands on supercomputers, distributed systems and computer clusters. In the last few years, the trend has turned towards graphics processing units (GPUs). Here we present a detailed description and a thorough evaluation of an alternative approach that relies on exploitation of the power available in modern multicore computers. The combination of single-core code optimization, vector processing, multithreading and efficient disk I/O operations succeeds in providing fast tomographic reconstructions on standard computers. The approach turns out to be competitive with the fastest GPU-based solutions thus far.

SUBMITTER: Agulleiro JI

PROVIDER: S-EPMC3491071 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Evaluation of a multicore-optimized implementation for tomographic reconstruction.

Agulleiro Jose-Ignacio JI Fernández José Jesús JJ

PloS one 20121106 11

Tomography allows elucidation of the three-dimensional structure of an object from a set of projection images. In life sciences, electron microscope tomography is providing invaluable information about the cell structure at a resolution of a few nanometres. Here, large images are required to combine wide fields of view with high resolution requirements. The computational complexity of the algorithms along with the large image size then turns tomographic reconstruction into a computationally dema ...[more]

PMID: 23139768

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Project description:For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated.Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space.The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary.In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique.

Dataset Information

Evaluation of a multicore-optimized implementation for tomographic reconstruction.

Publications

Evaluation of a multicore-optimized implementation for tomographic reconstruction.

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