PubMed Health⌕ Search

PubMed · 9829239

Digital x-ray imaging using amorphous selenium: reduction of aliasing.

Abstract

Alias reduction is analyzed with the concept of an equivalent presampling filter, and a mathematical approach is established to find the equivalent presampling filter corresponding to specific digital image processing algorithms. The effects of different sampling period T and sampling aperture tau on aliasing artifacts and on the resultant detective quantum efficiency (DQE) for a self-scanned, flat-panel, amorphous selenium detector are obtained. Different effective apertures can be obtained from the same detector by averaging signals over adjacent pixels. It is shown that adding outputs from M adjacent pixels is equivalent to introducing an equivalent presampling filter with special properties. Appropriate selection of the averaging parameters (M and weights) is shown to reduce the aliasing artifact in the resultant image. The effect of incomplete charge collection due to geometrical effects (fill factor) is examined. It is shown that a large fill factor is desirable for aliasing reduction. The relationship between a digital filter applied to the sampled signal and its equivalent presampling analog filter is also established. Analytical formulas for the sampled spectrum of white signal and for the sampled power spectrum of white noise are obtained for aperture functions with a spatially uniform response. These formulas take into accounts aliasing artifacts, signal correlation and aperture function response, and demonstrate the dependence of sampled spectra on T and tau. With these formulas the detective quantum efficiency DQE is derived. It is shown that the resultant DQE depends only on the fill factor and the size of readout electrode tau 0, but is completely independent of the degree or type of pixel averaging. That is, even though the pixel averaging method reduces aliasing it leaves DQE (omega) unchanged. When significant amplifier noise is present the DQE obtained with the pixel averaging method can be better than those obtained with an analog presampling filter. Finally, it is pointed out that the requirement of reducing aliasing artifacts conflicts with other requirements for a detector such as maximizing modulation transfer function (MTF). A careful and practical compromise has to be made by a detector designer in choosing the extent to which the aliasing artifacts are eliminated.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W G Ji, W Zhao, J A Rowlands. 1998. Digital x-ray imaging using amorphous selenium: reduction of aliasing.. https://doi.org/10.1118/1.598411

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗