Biomedical subjects

Samuel Matej

Publications and source records attributed to Samuel Matej.

5 recordsLinked to original sources

PubMed2006

PET energy-based scatter estimation and image reconstruction with energy-dependent corrections.

In this paper we propose a comprehensive energy-based scatter correction approach for positron emission tomography (PET). We take advantage of the marked difference between the energy spectra of the unscattered and scattered photons, and use the detailed energy information that comes with the list-mode data for the estimation of the scattered events distribution in the data space. Also, inside the maximum-likelihood expectation maximization (ML-EM) image reconstruction algorithm, we introduce energy-dependent factors that individualize the correction terms for each event, given its position and energy information. The central piece of our approach is the two-dimensional detector energy response model represented as a linear combination of four components, each one representing a particular state a PET event can be found in: both photons unscattered, the second scattered while the first not, the first photon scattered while the second not and both photons scattered. For a set of events collected in the vicinity of a point in the projection space, the coefficient of each component is determined by applying a statistical estimator. As a result we obtain the number of scattered events that are in the given set. The model also gives us the variation of scatter fraction with the photon pair energies for that particular position in the data space. A simulation study that demonstrates the proposed methods is presented.

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PubMed2006

Fourier-based reconstruction for fully 3-D PET: optimization of interpolation parameters.

Fourier-based approaches for three-dimensional (3-D) reconstruction are based on the relationship between the 3-D Fourier transform (FT) of the volume and the two-dimensional (2-D) FT of a parallel-ray projection of the volume. The critical step in the Fourier-based methods is the estimation of the samples of the 3-D transform of the image from the samples of the 2-D transforms of the projections on the planes through the origin of Fourier space, and vice versa for forward-projection (reprojection). The Fourier-based approaches have the potential for very fast reconstruction, but their straightforward implementation might lead to unsatisfactory results if careful attention is not paid to interpolation and weighting functions. In our previous work, we have investigated optimal interpolation parameters for the Fourier-based forward and back-projectors for iterative image reconstruction. The optimized interpolation kernels were shown to provide excellent quality comparable to the ideal sinc interpolator. This work presents an optimization of interpolation parameters of the 3-D direct Fourier method with Fourier reprojection (3D-FRP) for fully 3-D positron emission tomography (PET) data with incomplete oblique projections. The reprojection step is needed for the estimation (from an initial image) of the missing portions of the oblique data. In the 3D-FRP implementation, we use the gridding interpolation strategy, combined with proper weighting approaches in the transform and image domains. We have found that while the 3-D reprojection step requires similar optimal interpolation parameters as found in our previous studies on Fourier-based iterative approaches, the optimal interpolation parameters for the main 3D-FRP reconstruction stage are quite different. Our experimental results confirm that for the optimal interpolation parameters a very good image accuracy can be achieved even without any extra spectral oversampling, which is a common practice to decrease errors caused by interpolation in Fourier reconstruction.

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PubMed2004

Clinical validation of fully 3-D versus 2.5-D RAMLA reconstruction on the Philips-ADAC CPET PET scanner.

INTRODUCTION: Fully three-dimensional (3-D) reconstruction algorithms have the potential to better utilize PET data acquired in true 3-D acquisition modes, as compared to 2-D approaches involving data reduction through rebinning procedures followed by a 2-D or 2.5-D reconstruction. Our previous studies, involving simulated and real phantom data from PET scanners working purely in 3-D acquisition mode, showed that the fully 3-D reconstruction approaches bring appreciable improvement on physical measures and visual image quality as well. Reconstruction procedures utilized in those studies were based upon the row action maximum likelihood algorithm (RAMLA) implemented on our clinical scanners. The purpose of this study was to investigate if the fully 3-D RAMLA reconstruction software brings an appreciable improvement in clinical image quality and reader confidence. MATERIALS AND METHODS: Ninety patient scans acquired on the Philips CPET scanner at our institution were reconstructed using 2.5-D RAMLA and 3-D RAMLA with differing reconstruction parameters. These scans were blindly presented to four experienced PET scan readers who graded various aspects of image quality. RESULTS: Our study concluded that 3-D RAMLA on the CPET scanner reduces artifacts and image noise and improves clinical confidence in interpreting PET images. CONCLUSION: This study led to the routine use of 3-D RAMLA reconstruction on the CPET scanner at our institution.

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PubMed2004

3-d reconstruction of 2-D crystals in real space.

A new algorithm for three-dimensional reconstruction of two-dimensional crystals from projections is presented, and its applicability to biological macromolecules imaged using transmission electron microscopy (TEM) is investigated. Its main departures from the traditional approach is that it works in real space, rather than in Fourier space, and it is iterative. This has the advantage of making it convenient to introduce additional constraints (such as the support of the function to be reconstructed, which may be known from alternative measurements) and has the potential of more accurately modeling the TEM image formation process. Phantom experiments indicate the superiority of the new approach even without the introduction of constraints in addition to the projection data.

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PubMed2004

Iterative tomographic image reconstruction using Fourier-based forward and back-projectors.

Iterative image reconstruction algorithms play an increasingly important role in modern tomographic systems, especially in emission tomography. With the fast increase of the sizes of the tomographic data, reduction of the computation demands of the reconstruction algorithms is of great importance. Fourier-based forward and back-projection methods have the potential to considerably reduce the computation time in iterative reconstruction. Additional substantial speed-up of those approaches can be obtained utilizing powerful and cheap off-the-shelf fast Fourier transform (FFT) processing hardware. The Fourier reconstruction approaches are based on the relationship between the Fourier transform of the image and Fourier transformation of the parallel-ray projections. The critical two steps are the estimations of the samples of the projection transform, on the central section through the origin of Fourier space, from the samples of the transform of the image, and vice versa for back-projection. Interpolation errors are a limitation of Fourier-based reconstruction methods. We have applied min-max optimized Kaiser-Bessel interpolation within the nonuniform FFT (NUFFT) framework and devised ways of incorporation of resolution models into the Fourier-based iterative approaches. Numerical and computer simulation results show that the min-max NUFFT approach provides substantially lower approximation errors in tomographic forward and back-projection than conventional interpolation methods. Our studies have further confirmed that Fourier-based projectors using the NUFFT approach provide accurate approximations to their space-based counterparts but with about ten times faster computation, and that they are viable candidates for fast iterative image reconstruction.

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