A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon‐counting x‐ray detector for CT

Cammin, J.; Xu, Jennifer; Barber, William C.; Iwanczyk, Jan S.; Hartsough, Neal E.; Taguchi, Katsuyuki

doi:10.1118/1.4866890

Cited by 75 publications

(75 citation statements)

References 37 publications

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“…Various empirical methods have been proposed to compensate for PCXD spectral distortions [13, 14]. Corrections undo the distortion process, while compensation is used to offset the effect.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based method for spectral distortion correction in photon counting x-ray CT

et al. 2016

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Purpose Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables both 4 energy bins acquisition, as well as full-spectrum mode in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical effects in the detector and can be very noisy due to photon starvation in narrow energy bins. To address spectral distortions, we propose and demonstrate a novel artificial neural network (ANN)-based spectral distortion correction mechanism, which learns to undo the distortion in spectral CT, resulting in improved material decomposition accuracy. To address noise, post-reconstruction denoising based on bilateral filtration, which jointly enforces intensity gradient sparsity between spectral samples, is used to further improve the robustness of ANN training and material decomposition accuracy. Methods Our ANN-based distortion correction method is calibrated using 3D-printed phantoms and a model of our spectral CT system. To enable realistic simulations and validation of our method, we first modeled the spectral distortions using experimental data acquired from 109Cd and 133Ba radioactive sources measured with our PCXD. Next, we trained an ANN to learn the relationship between the distorted spectral CT projections and the ideal, distortion-free projections in a calibration step. This required knowledge of the ground truth, distortion-free spectral CT projections, which were obtained by simulating a spectral CT scan of the digital version of a 3D-printed phantom. Once the training was completed, the trained ANN was used to perform distortion correction on any subsequent scans of the same system with the same parameters. We used joint bilateral filtration (BF) to perform noise reduction by jointly enforcing intensity gradient sparsity between the reconstructed images for each energy bin. Following reconstruction and denoising, the CT data was spectrally decomposed using the photoelectric effect, Compton scattering, and a K-edge material (i.e. iodine). The ANN-based distortion correction approach was tested using both simulations and experimental data acquired in phantoms and a mouse with our PCXD-based micro-CT system for 4 bins and full-spectrum acquisition modes. The iodine detectability and decomposition accuracy were assessed using the contrast-to-noise ratio and relative error in iodine concentration estimation metrics in images with and without distortion correction. Results In simulation, the material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with 50% and 20% reductions in material conce...

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Section: Introductionmentioning

confidence: 99%

A neural network-based method for spectral distortion correction in photon counting x-ray CT

et al. 2016

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“…2.C are either count rate-dependent or count rate-independent, and the integrated phenomenon can be modeled by cascading the corresponding models. [76][77][78] Below we discuss models of both of these factors.…”

Section: D Pcd Modelsmentioning

confidence: 99%

Vision 20/20: Single photon counting x‐ray detectors in medical imaging

2013

Self Cite

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Photon counting detectors (PCDs) with energy discrimination capabilities have been developed for medical x-ray computed tomography (CT) and x-ray (XR) imaging. Using detection mechanisms that are completely different from the current energy integrating detectors and measuring the material information of the object to be imaged, these PCDs have the potential not only to improve the current CT and XR images, such as dose reduction, but also to open revolutionary novel applications such as molecular CT and XR imaging. The performance of PCDs is not flawless, however, and it seems extremely challenging to develop PCDs with close to ideal characteristics. In this paper, the authors offer our vision for the future of PCD-CT and PCD-XR with the review of the current status and the prediction of (1) detector technologies, (2) imaging technologies, (3) system technologies, and (4) potential clinical benefits with PCDs.

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“…The spectral response of photon-counting detectors is degraded by effects such as pulse-pileup and charge sharing, which can introduce error in material decomposition estimates [4, 7]. To investigate how much the experimental measurements deviated from ideal measurements, the experimental Teflon measurements were also decomposed using a maximum likelihood estimator that assumed an ideal detector [3].…”

Section: Methodsmentioning

confidence: 99%

Experimental comparison of empirical material decomposition methods for spectral CT

Zimmerman

Schmidt

2015

Phys. Med. Biol.

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Material composition can be estimated from spectral information acquired using photon counting x-ray detectors with pulse height analysis. Non-ideal effects in photon counting x-ray detectors such as charge-sharing, k-escape, and pulse-pileup distort the detected spectrum, which can cause material decomposition errors. This work compared the performance of two empirical decomposition methods: a neural network estimator and a linearized maximum likelihood estimator with correction (A-table method). The two investigated methods differ in how they model the nonlinear relationship between the spectral measurements and material decomposition estimates. The bias and standard deviation of material decomposition estimates were compared for the two methods, using both simulations and experiments with a photon-counting x-ray detector. Both the neural network and A-table methods demonstrated similar performance for the simulated data. The neural network had lower standard deviation for nearly all thicknesses of the test materials in the collimated (low scatter) and uncollimated (higher scatter) experimental data. In the experimental study of Teflon thicknesses, non-ideal detector effects demonstrated a potential bias of 11–28%, which was reduced to 0.1–11% using the proposed empirical methods. Overall, the results demonstrated preliminary experimental feasibility of empirical material decomposition for spectral CT using photon-counting detectors.

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A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon‐counting x‐ray detector for CT

Cited by 75 publications

References 37 publications

A neural network-based method for spectral distortion correction in photon counting x-ray CT

A neural network-based method for spectral distortion correction in photon counting x-ray CT

Vision 20/20: Single photon counting x‐ray detectors in medical imaging

Experimental comparison of empirical material decomposition methods for spectral CT

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