Evidence map›Paper›PMID 39735346›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2024

Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT.

J Carlos Rodriguez Luna, Mini Das

Abstract read
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Article in Journal of medical imaging (Bellingham, Wash.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

J Carlos Rodriguez LunaUniversity of Houston, Department of Physics, Houston, Texas, United States.
Mini DasUniversity of Houston, Department of Physics, Houston, Texas, United States.

Funding

Development of Spectral Phase Contrast Micro-CTR01EB029761 · NIBIB · UNIVERSITY OF HOUSTON · PI Mini Das · 2020 to 2026
$3.2M
NIBIB NIH HHS R01 EB029761
6 · The paper itself

Abstract

Purpose: Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits. Approach: We begin with an empirical spectral correction method executed in the tomographic domain that accounts for distortions in estimated spectral attenuation for each voxel. This is followed by our proposed iterative clustering material decomposition (ICMD) where clustering of voxels is used to reduce the number of basis materials to be resolved for each cluster. Using a larger number of energy bins for the clustering step shows distinct advantages in excellent classification to a larger number of clusters with accurate cluster centers when compared with the National Institute of Standards and Technology attenuation values. The decomposition step is applied to each cluster separately where each cluster has fewer basis materials compared with the entire volume. This is shown to reduce the need for the number of energy bins required in each decomposition step for the clusters. This approach significantly increases the total number of materials that can be decomposed within the volume with high accuracy and with excellent noise properties. Results: Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a Conclusions: This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.

Indexed as

clustering analysismaterial decompositionmicro-computed tomographyphoton counting detectorssignal-to-thickness calibrationsoft tissue classificationspectral correction

Identifiers

PMID39735346
PMCPMC11676343

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