Biomedical subjects

Roland Proksa

Publications and source records attributed to Roland Proksa.

4 recordsLinked to original sources

PubMed2006

Circular CT in combination with a helical segment.

The combination of circular CT with a helical trajectory segment results in a mathematically complete data set. We present a reconstruction algorithm which is mathematically exact and of the filtered back-projection type. The algorithm ensures that only Radon planes which are not covered along the circle are taken into account, when data from the helical segment are back-projected. Therefore, the helical segment contributes only to low-frequency parts of trans-axial slices. Data along the helix can be obtained with a very low acquisition dose.

Algorithms↗

PubMed2005

EnPiT: filtered back-projection algorithm for helical CT using an n-Pi acquisition.

In this paper, we formulate a reconstruction algorithm for an n-Pi acquisition, where n can be any positive odd integer. The algorithm is a generalization of the method presented in (Bontus et al. 2003). It is based on the results obtained by Katsevich (2004). For the algorithm, different sets of filter-lines have to be defined. We describe the variation of these lines along the detector in some detail, before we discuss, how the method gives all Radon-plane contributions the correct weighting. The different sets of filter-lines are all contained within the n-Pi window, such that a practical realization is possible. Reconstruction results, which we present in the final section, show convincing image quality.

Algorithms↗

PubMed2003

Real-time adaptive filtering for projection reconstruction MR fluoroscopy.

Magnetic resonance (MR) imaging has faced a dramatic increase in real-time capabilities over the last years. However, the application of fast pulse sequences still suffers from low signal-to-noise ratios (SNRs), which can be the limiting factor for the actual acquisition speed. In MR fluoroscopy, filtering along the time and/or spatial domain can be applied to increase the image quality. In this paper, a projection-based noise filter is presented that significantly enhances the SNR in projection reconstruction (PR) fluoroscopy without apparent loss of resolution in the reconstructed images. In contrast to an imaged-based approach, this method allows a very efficient computational implementation. The filter algorithm was implemented on a digital signal processor and was applied to real-time processing during PR fluoroscopy. A quantitative analysis of the improvement in SNR and results for different fluoroscopic MR applications are given. Apart from MR fluoroscopy, the proposed technique has the potential to be applied to low dose computed tomography fluoroscopy.

Algorithms↗

PubMed2003

A quasiexact reconstruction algorithm for helical CT using a 3-Pi acquisition.

Recently, an exact reconstruction method for helical CT was published by A. Katsevich. The algorithm is of the filtered backprojection type and is, therefore, computationally efficient. Moreover, during backprojection, only data are used which correspond to an illumination interval of 180 degrees as seen from the object-point. We propose a new reconstruction method, which is applicable to data obtained with a 3-Pi acquisition [IEEE Trans. Med. Imaging 19, 848-863 (2000)]. The method uses the same filter types as the Katsevich algorithm, but the directions and the number of the filter lines are chosen differently. For the derivation of the new algorithm, we analyze the relationship of the Katsevich method and radon inversion. A certain radon plane can intersect with the backprojection interval related to a 3-Pi acquisition either once, three, or five times. In analogy to the definition of quasiexactness introduced by Kudo et al. for a 1-Pi acquisition, we use the term quasiexactness for algorithms on a 3-Pi acquisition, if radon planes with one or three intersections within the backprojection interval are treated correctly. Using the results on the relationship with radon inversion, we can prove that our algorithm is quasiexact in this sense. We use simulation results in order to demonstrate that the algorithm yields excellent image quality.

Algorithms↗