Assessment of metal implant induced artefacts using photon counting spectral CT

Amma, Maya R; Butler, Anthony; Raja, Aamir; Bamford, Benjamin; Butler, P. H.; Walker, E. Peter; Matanaghi, Aysouda; Adebileje, Sikiru A; Anderson, Nigel G.; Anjomrouz, Marzieh; Asghariomabad, Fatemeh; Atharifard, Ali; Bell, Stephen T.; Bheesette, S.; Chernoglazov, Alexander I.; Dalefield, Tara; Ruiter, Niels J A de; Duncan, Neryda; Doesburg, R.; Gieseg, Steven P.; Goulter, Brian P.; Alexander, Steven D.; Gurney, Sam; Healy, Joseph L.; Hilton, Peter; Dahal, Shishir; Carbonez, Pierre; Damet, J.; Chambers, Claire; Kanithi, Praveenkumar; Kirkbride, Tracy; Lansley, Stuart P.; Lowe, Chiara; Mandalika, V. B. H.; Marfo, Emmanuel; Moghiseh, Mahdieh; Palmer, David N.; Panta, Raj K.; Prebble, Hannah M.; Ramyar, Mohsen; Renaud, Peter; Schleich, Nanette; Searle, Emily; Shamshad, Muhammed; Sheeja, Jereena S; Uddin, Rayhan; Broeke, Lieza Vanden; Vivek, V.; Wijesooriya, Manoj; Walsh, Michael F.; Baer, Kenzie; Tredinnick, Seamus; Woodfield, Tim B. F.

doi:10.1117/12.2531003

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The 100−110 keV bin was used as the high‐energy bin for the experimental verification of TRMAR. This energy range was chosen not only due to it containing energies above the SPR threshold dictated by the Fastcat simulations, but also because previous work has demonstrated the benefits of smaller bins for MAR using PCDs 42 . The 30−120 keV energy range used for the metal‐free, uncorrected, and NMAR images, as well as the base of the TRMAR images.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, a number of recent studies have investigated how photon‐counting detectors (PCDs), used in place of the conventional energy‐integrating detectors (EIDs), could improve aspects of MAR, 39–41 including better assessment of bone health immediately around metal implants 42 as well as better detection of implant failure when compared to current imaging methods 43 . PCDs are direct‐conversion detectors that can distinguish the energy of each incident x‐ray and bin them into a number of predetermined energy ranges.…”

Section: Introductionmentioning

confidence: 99%

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Metal artifact correction in photon‐counting detector computed tomography: metal trace replacement using high‐energy data

et al. 2022

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Background: Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. Metal artifact reduction (MAR) methods continue to be proposed and photon-counting detectors (PCDs) have recently been the subject of research toward this purpose. PCDs offer the ability to distinguish the energy of incident x-rays and sort them in a set number of energy bins. High-energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening. Purpose: High-energy reconstructions using PCD-CT have their drawbacks, such as reduced image contrast and increased noise. Here, we demonstrate a MAR algorithm, trace replacement MAR (TRMAR), in which the data corrupted by metal artifacts in full energy spectrum projections are corrected using the high-energy data captured during the same scan. The resulting reconstructions offer similar MAR to that seen in high-energy reconstructions, but with improved image quality. Methods: Experimental data were collected using a bench-top PCD-CT system with a cadmium zinc telluride PCD. Simulations were performed to determine the optimal high-energy threshold and to test TRMAR in simulations using the XCAT phantom and a biological sample. For experiments a 100-mm diameter cylindrical phantom containing vials of water, two screws, various densities of Ca(ClO 4 ) 2 , and a spatial resolution phantom was imaged with and without the screws. The screws were segmented in the initial reconstruction and forward projected to identify them in the sinogram space in order to perform TRMAR.The resulting reconstructions were compared to the control and to reconstructions corrected using normalized metal artifact reduction (NMAR). Additionally, a beef short rib was imaged with and without metal to provide a more realistic phantom. Results: XCAT simulations showed a reduction in the streak artifact from −978 HU in uncorrected images to −10 HU with TRMAR. The magnitude of the metal artifact in uncorrected images of the 100-mm phantom was −442 HU, compared to the desired −81 HU with no metal. TRMAR reduced the magnitude of the artifact to −142 HU, with NMAR reducing the magnitude to −96 HU. Relative image noise was reduced from 176% in the high-energy image to 56% using TRMAR. Density quantification was better with NMAR, with the Ca(ClO 4 ) 2 vial affected most by metal artifacts showing 0.8% error compared to 2.1% with TRMAR. Small features were preserved to a greater extent with TRMAR, with the limiting spatial frequency at 20% of the MTF fully maintained at 1.31 lp/mm, 380

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metal artifact correction in photon‐counting detector computed tomography: metal trace replacement using high‐energy data

et al. 2022

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An Overview of X-ray Photon Counting Spectral Imaging (x-CSI) with a Focus on Gold Nanoparticle Quantification in Oncology

Scienti

Darambara

2021

J. Imaging

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This review article offers an overview of the differences between traditional energy integrating (EI) X-ray imaging and the new technique of X-ray photon counting spectral imaging (x-CSI). The review is motivated by the need to image gold nanoparticles (AuNP) in vivo if they are to be used clinically to deliver a radiotherapy dose-enhancing effect (RDEE). The aim of this work is to familiarise the reader with x-CSI as a technique and to draw attention to how this technique will need to develop to be of clinical use for the described oncological applications. This article covers the conceptual differences between x-CSI and EI approaches, the advantages of x-CSI, constraints on x-CSI system design, and the achievements of x-CSI in AuNP quantification. The results of the review show there are still approximately two orders of magnitude between the AuNP concentrations used in RDEE applications and the demonstrated detection limits of x-CSI. Two approaches to overcome this were suggested: changing AuNP design or changing x-CSI system design. Optimal system parameters for AuNP detection and general spectral performance as determined by simulation studies were different to those used in the current x-CSI systems, indicating potential gains that may be made with this approach.

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X-ray Photon-Counting Data Correction through Deep Learning

M¹,

Rundle²,

Wang³

2020

Preprint

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X-ray photon-counting detectors (PCDs) are drawing an increasing attention in recent years due to their low noise and energy discrimination capabilities. The energy/spectral dimension associated with PCDs potentially brings great benefits such as for material decomposition, beam hardening and metal artifact reduction, as well as low-dose CT imaging. However, X-ray PCDs are currently limited by several technical issues, particularly charge splitting (including charge sharing and K-shell fluorescence re-absorption or escaping) and pulse pile-up effects which distort the energy spectrum and compromise the data quality. Correction of raw PCD measurements with hardware improvement and analytic modeling is rather expensive and complicated. Hence, here we proposed a deep neural network based PCD data correction approach which directly maps imperfect data to the ideal data in the supervised learning mode. In this work, we first establish a complete simulation model incorporating the charge splitting and pulse pile-up effects. The simulated PCD data and the ground truth counterparts are then fed to a specially designed deep adversarial network for PCD data correction. Next, the trained network is used to correct separately generated PCD data. The test results demonstrate that the trained network successfully recovers the ideal spectrum from the distorted measurement within ±6% relative error. Significant data and image fidelity improvements are clearly observed in both projection and reconstruction domains.

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Assessment of metal implant induced artefacts using photon counting spectral CT

Cited by 3 publications

References 6 publications

Metal artifact correction in photon‐counting detector computed tomography: metal trace replacement using high‐energy data

Metal artifact correction in photon‐counting detector computed tomography: metal trace replacement using high‐energy data

An Overview of X-ray Photon Counting Spectral Imaging (x-CSI) with a Focus on Gold Nanoparticle Quantification in Oncology

X-ray Photon-Counting Data Correction through Deep Learning

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