Search PubMed⌕ Search

PubMed · 10565510

Subsecond multi-slice computed tomography: basics and applications.

Abstract

The recent advent of multislice-scanning is the first real quantum leap in computed tomography since the introduction of spiral CT in the early 90s. We discuss basic theoretical considerations important for the design of multislice scanners. Then, specific issues, like the design of the detector and spiral interpolation schemes are addressed briefly for the SOMATOM PLUS 4 Volume Zoom. The theoretical concepts are validated with phantom measurements. We finally show the large potential of the new technology for clinical applications. The concurrent acquisition of multiple slices results in a dramatic reduction of scan time for a given scan technique. This allows scanning volumes previously inaccessible. Similarly, given volumes can be scanned at narrower collimation, i.e. higher axial resolution in a given time. From data acquired at narrow collimation, both high-resolution studies and standard images can be reconstructed in the so-called Combi-Mode. This on the one hand reduces dose exposure to the patient because repeated scanning of a patient is no longer required. On the other hand, standard reconstructions benefit from narrow collimation as Partial Volume Artifacts are drastically suppressed. The rotational speed of 0.5 s of the SOMATOM PLUS 4 Volume Zoom furthermore opens up a whole range of new applications in cardiac CT. For the first time, virtually motion-free images can be acquired even for large volumes in a single breathhold by the combination of fast rotation and ECG triggering, respectively gating. We explain the underlying concepts and present initial results. The paper concludes with a brief discussion of the impact of the new technique on image display and postprocessing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K Klingenbeck-Regn, S Schaller, T Flohr, B Ohnesorge, A F Kopp, U Baum. 1999. Subsecond multi-slice computed tomography: basics and applications.. https://doi.org/10.1016/s0720-048x(99)00086-8

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

KEEP EXPLORING

Related citations

Striping artifact removal in VisiumHD data through nuclear counts modeling.

MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.

Artifacts↗

Enhanced bio-manufacturing through advanced multivariate statistical technologies.

The paper describes the interrogation of data, from a reaction vessel producing an active pharmaceutical ingredient (API), using advanced multivariate statistical techniques. Due to the limited number of batches available, data augmentation was used to increase the number of batches thereby enabling the extraction of more subtle process behaviour from the data. A second methodology investigated was that of multi-group modelling. This allowed between cluster variability to be removed, thus allowing attention to focus on within process variability. The paper describes how the different approaches enabled the realisation of a better understanding of the factors causing the onset of an impurity formation to be obtained as well demonstrating the power of multivariate statistical data analysis techniques to provide an enhanced understanding of the process.

Artifacts↗