Evidence map›Paper›PMID 39912861›Full record

ArticleMedical physics2025

Spatial resolution improvements for photon counting detectors using coincidence counting and frequency weighted reconstruction.

Scott S Hsieh, Katsuyuki Taguchi, Shuai Leng, Cynthia H McCollough

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Article in Medical physics, 2025. 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Scott S HsiehDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Katsuyuki TaguchiDepartment of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Funding

Improving the dose efficiency of photon counting CTR01EB035553 · NIBIB · MAYO CLINIC ROCHESTER · PI Scott Sigao Hsieh · 2024 to 2026
$1.1M
Quasi-ideal photon counting x-ray CT with multi-energy inter-pixel coincidence counter (MEICC)R21EB029739 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI TAGUCHI, KATSUYUKI · 2020 to 2021
$436k
NIBIB NIH HHS R01 EB035553NIBIB NIH HHS R21 EB029739NIH HHS R01EB035553NIH HHS R21EB029739
6 · The paper itself

Abstract

backgroundPhoton counting X-ray detectors (PCDs) provide better spatial resolution than energy integrating X-ray detectors, but even higher resolution is desired in some applications. Certain charge sharing compensation techniques such as coincidence counting preferentially detect photons that arrive at the boundary between pixels, and this could be used for subpixel localization of incident photons and enhanced spatial resolution. PURPOSE: To estimate improvements to spatial resolution and detective quantum efficiency that are possible when using coincidence counting for high-resolution, non-spectral imaging.

methodsThe modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) were estimated using numerical simulations of a two-dimensional parallel-beam CT system. Coincidence counters were modeled either geometrically or with Monte Carlo simulations. The geometric model consisted of narrow coincidence counters that interlaced with wider ordinary pixels, and showed that using standard filtered backprojection decreases low-frequency DQE, but that a frequency weighting technique could be used to restore DQE. The Monte Carlo simulations were used to estimate the possible improvements that could be expected from real systems. The pixel pitch was 0.25 mm and the source apertures considered were 0, 0.125, and 0.5 mm. A numerical stent phantom was also used to illustrate possible improvements.

resultsAssuming a 0.125 mm source aperture and the Monte Carlo model, the limiting MTF (at 10%) increased from 20 to 40 lp/cm using coincidence counters. This can be explained by the increased sampling (and higher Nyquist limit) possible from coincidence counters. In the geometric model, coincidence counters are compared to conventional double sampling techniques such as in-plane flying focal spot, and the MTF at 38 lp/cm increased from 5% to 53%. Without the frequency weighting technique, low-frequency DQE was reduced by about 20%, but these losses are recovered with frequency weighting. Improvements are much more modest with the 0.25 mm source aperture because the system becomes source-limited.

conclusionsCoincidence counting could be used to increase spatial resolution in PCDs. The increases in system resolution could be large if a high-resolution X-ray source were available.

Indexed as

Image Processing, Computer-AssistedPhotonsTomography, X-Ray ComputedMonte Carlo MethodPhantoms, ImagingMonte Carlophoton counting detectorsspatial resolution

Identifiers

PMID39912861
PMCPMC12826346

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.