Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging

Ballabriga, R.; Alozy, J.; Campbell, M.; Fröjdh, E.; Heijne, E.; Koenig, Thomas; Llopart, X.; Marchal, J.; Pennicard, David; Poikela, T.; Tlustos, L.; Valerio, P.; Wong, Winnie; Zubér, M. S.

doi:10.1088/1748-0221/11/01/p01007

Cited by 177 publications

(162 citation statements)

References 71 publications

(60 reference statements)

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“…Photon counting detector (PCD) based computed tomography (PCCT) is an emerging imaging technique and substantial progress has been reported in recent years by different researchers (Tümer et al, 2000, Schlomka et al, 2008, Shikhaliev, 2008b, Shikhaliev, 2008a, Iwanczyk et al, 2009, Kappler et al, 2010, Taguchi and Iwanczyk, 2013, Persson et al, 2014, Bennett et al, 2014, Roessl and Proksa, 2007, Ballabriga et al, 2016). PCDs measure individual photons and their associated energy, rather than the integrated charges generated by all measured photons, as occurs with an energy-integrating detector (EID).…”

Section: Introductionmentioning

confidence: 99%

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

et al. 2017

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Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with 5 known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy (DSDE) scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology. Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

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Section: Introductionmentioning

confidence: 99%

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

et al. 2017

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“…The Medipix3 chip implemented a programmable ACS and featured small 55 lm pixel sizes, using ACS to sum 2 9 2 groups to produce an effective pixel pitch of 110 lm. 17 The Medipix3 chip is notable because it was an early design that produced very good spectroscopic results, but the count rate capability of this chip was well below the needs of diagnostic CT. 18 Recently, progress has been made in this regard, with a detector using 0.1 mm CdTe pixels that implements ACS. The authors show that the performance of charge summing improves performance at up to 250 Mcps/ mm 2 for a material decomposition task.…”

Section: Introductionmentioning

confidence: 99%

Digital count summing vs analog charge summing for photon counting detectors: A performance simulation study

Hsieh

Sjölin

2018

Medical Physics

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“…Reconstruction yields an energy- weighted image of linear attenuation coefficients. This loss of energy information results in some major limitations to current CT technologies, including poor soft tissue contrast and reduced accuracy of mass attenuation measurements [2, 3]. …”

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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Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging

Abstract: ASIC, we demonstrate that the spectroscopic information contained in the incoming photon beam can be recovered by the implementation in hardware of an algorithm whereby the signal from a single photon is reconstructed and allocated to the pixel with the largest deposition.

Cited by 177 publications

References 71 publications

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

Digital count summing vs analog charge summing for photon counting detectors: A performance simulation study

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

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