Spectral CT of the extremities with a silicon strip photon counting detector

Sisniega, Alejandro; Zbijewski, Wojciech; Stayman, J. Webster; Xu, Jennifer; Taguchi, Katsuyuki; Siewerdsen, J. H.

doi:10.1117/12.2082092

Cited by 2 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…Analysis of image quality with stacked axial scans was shown in previous work (Zbijewski et al 2013, Sisniega et al 2015). …”

Section: Methodsmentioning

confidence: 90%

“…Previous work (Zbijewski et al 2013, Sisniega et al 2015) with the same benchtop PCD-based CT system explored the interplay between volumetric sampling and image quality for stacked circular acquisition trajectories and conventional Penalized Likelihood (PL) MBIR with quadratic penalties. In this paper, the analysis is extended to helical acquisition trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

et al. 2015

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Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm × 25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8 × higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.

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“…Analysis of image quality with stacked axial scans was shown in previous work (Zbijewski et al 2013, Sisniega et al 2015). …”

Section: Methodsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Due to the photon energy discriminating, electronic noise rejecting, and direct conversion features of photon counting detectors (PCDs), PCD-based computed tomography (PCD-CT) has the potential of improving the non-spectral imaging performance of conventional CT (da Silva et al 2019, Ferrero et al 2018, Gutjahr et al 2016, Harvey et al 2019, Marcus et al 2018, Pourmorteza et al 2016, Shikhaliev et al 2005, Shikhaliev 2008, Symons et al 2017a, Taguchi and Iwanczyk 2013, Taguchi 2017, Willemink et al 2018, Yu et al 2015, 2016 while providing perfectly registered spectral information for every clinical CT scan (Amato et al 2020, Badea et al 2019, Danielsson et al 2020, Fredette et al 2019, Lee et al 2016, Nakada et al 2015, Persson et al 2014, Roessl and Proksa 2007, Roessl et al 2011, Schmidt 2009, Shikhaliev 2008, Sisniega et al 2015, Symons et al 2017a This work aims at providing a publicly available artifact correction method for researchers and other users of PCD-CT that do not have access to the proprietary algorithms or the capability of upgrading their existing PCD hardware. Except for some low-cost acrylic and aluminum sheets, the proposed method does not require any other dedicated apparatus to work.…”

Section: Introductionmentioning

confidence: 99%

An experimental method to correct low-frequency concentric artifacts in photon counting CT

Feng¹,

Ji²,

Zhang³

et al. 2021

Phys. Med. Biol.

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Large-area photon counting detectors (PCDs) are usually built by tiling multiple semiconductor panels that often have slightly different spectral responses to input x-rays. As a result of this spectral inconsistency, experimental PCD-CT images of large, human-sized objects may show high-frequency ring artifacts and low-frequency band artifacts. Due to the much larger width of the bands compared with the rings, the concentric artifact problem in PCD-CT images of human-sized objects cannot be adequately addressed by conventional CT ring correction methods. This work presents an experimental method to correct the concentric artifacts in PCD-CT. The method is applicable to not only energy-discriminating PCDs with multiple bins but also PCDs with only a single threshold controller. Its principle is similar to the two-step beam hardening correction method, except that the proposed method uses pixel-specific polynomial functions to address the spectral inconsistency problem across the detector plane. The pixel-specific polynomial coefficients were experimentally calibrated using 15 acrylic sheets and 6 aluminum sheets of known thicknesses. The pixel-specific polynomial functions were used to convert the measured PCD-CT projection data to acrylic- and aluminum-equivalent thicknesses that are energy-independent. The proposed method was experimentally evaluated using a human cadaver head and multiple physical phantoms: two of them contain iodine and one phantom contains dual K-edge contrast materials (gadolinium and iodine). The results show that the proposed method can effectively remove the low-frequency concentric artifacts in PCD-CT images while reducing beam hardening artifacts. In contrast, the conventional CT ring correction algorithm did not adequately address the low-frequency band artifacts. Compared with the direct material decomposition-based correction method, the proposed method not only relaxes the requirement of multi-energy bins but also generates images with lower noise and fewer concentric artifacts.

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Spectral CT of the extremities with a silicon strip photon counting detector

Cited by 2 publications

References 7 publications

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

An experimental method to correct low-frequency concentric artifacts in photon counting CT

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