High resolution X-ray imaging using a silicon strip detector

Beuville, E.; Cahn, R. N.; Cederström, Björn; Danielsson, Mats; Hall, A.; Hasegawa, B.H.; Luo, Linqing; Lundqvist, Mats; Nygren, D. R.; Oltman, E.; Walton, J.T.

doi:10.1109/23.737664

Cited by 44 publications

(19 citation statements)

References 10 publications

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“…An alternative detector configuration approach involves multiple stacked layers of segmented detectors with varying layer thicknesses. 23,46 Having multiple layers along the depth direction significantly decreases the count rates each layer needs to handle. The thickness of the individual layers is optimized for count rate performance and to avoid saturation.…”

Section: A Basic Architectures and Detection Mechanismmentioning

confidence: 99%

“…Another potential merit is a possibility of estimating the energy using the depth of interaction. 46 This approach has a number of challenges related to the interconnections between the various layers and readout electronics. In order to keep the detector layers close together, connections to the readout electronics need to be elongated which might cause additional stray capacitances and vulnerability to cross talk, if not carefully designed.…”

Section: A Basic Architectures and Detection Mechanismmentioning

confidence: 99%

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

2013

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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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Section: A Basic Architectures and Detection Mechanismmentioning

confidence: 99%

Section: A Basic Architectures and Detection Mechanismmentioning

confidence: 99%

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

2013

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“…Calibration was obtained from one phantom of 14 cm in diameter. This one-point calibration was used to decompose breast sizes of 8,10,12,14,16,18, and 20 cm in diameter. As seen from the graphs, measurements were more accurate in the vicinity of 14 cm.…”

Section: Resultsmentioning

confidence: 99%

“…This provides different measurements of the same object that can then be used in decomposition algorithms. Photon counting detectors that have been investigated include Si strips, 12 Xe gas avalanche, 13 and microchannel plate.…”

Section: Introductionmentioning

confidence: 99%

Least squares parameter estimation methods for material decomposition with energy discriminating detectors

Lê

Molloi

2010

Medical Physics

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Purpose: Energy resolving detectors provide more than one spectral measurement in one image acquisition. The purpose of this study is to investigate, with simulation, the ability to decompose four materials using energy discriminating detectors and least squares minimization techniques. Methods: Three least squares parameter estimation decomposition techniques were investigated for four-material breast imaging tasks in the image domain. The first technique treats the voxel as if it consisted of fractions of all the materials. The second method assumes that a voxel primarily contains one material and divides the decomposition process into segmentation and quantification tasks. The third is similar to the second method but a calibration was used. The simulated computed tomography ͑CT͒ system consisted of an 80 kVp spectrum and a CdZnTe ͑CZT͒ detector that could resolve the x-ray spectrum into five energy bins. A postmortem breast specimen was imaged with flat panel CT to provide a model for the digital phantoms. Hydroxyapatite ͑HA͒ ͑50, 150, 250, 350, 450, and 550 mg/ml͒ and iodine ͑4, 12, 20, 28, 36, and 44 mg/ml͒ contrast elements were embedded into the glandular region of the phantoms. Calibration phantoms consisted of a 30/70 glandular-toadipose tissue ratio with embedded HA ͑100, 200, 300, 400, and 500 mg/ml͒ and iodine ͑5, 15, 25, 35, and 45 mg/ml͒. The x-ray transport process was simulated where the Beer-Lambert law, Poisson process, and CZT absorption efficiency were applied. Qualitative and quantitative evaluations of the decomposition techniques were performed and compared. The effect of breast size was also investigated. Results: The first technique decomposed iodine adequately but failed for other materials. The second method separated the materials but was unable to quantify the materials. With the addition of a calibration, the third technique provided good separation and quantification of hydroxyapatite, iodine, glandular, and adipose tissues. Quantification with this technique was accurate with errors of 9.83% and 6.61% for HA and iodine, respectively. Calibration at one point ͑one breast size͒ showed increased errors as the mismatch in breast diameters between calibration and measurement increased. A four-point calibration successfully decomposed breast diameter spanning the entire range from 8 to 20 cm. For a 14 cm breast, errors were reduced from 5.44% to 1.75% and from 6.17% to 3.27% with the multipoint calibration for HA and iodine, respectively. Conclusions:The results of the simulation study showed that a CT system based on CZT detectors in conjunction with least squares minimization technique can be used to decompose four materials. The calibrated least squares parameter estimation decomposition technique performed the best, separating and accurately quantifying the concentrations of hydroxyapatite and iodine.

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“…These energy resolved detectors use high speed, typically solid-state, radiation sensors combined with very fast readout application specific integrated circuits ͑ASICs͒ that process individual photons. [15][16][17] These ASICs have an electronics chain for each pixel in the detector. The current signal from the detector is integrated, amplified, and shaped.…”

Section: Introductionmentioning

confidence: 99%

Material separation in x‐ray CT with energy resolved photon‐counting detectors

et al. 2011

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Purpose: The objective of the study was to demonstrate that, in x-ray computed tomography ͑CT͒, more than two types of materials can be effectively separated with the use of an energy resolved photon-counting detector and classification methodology. Specifically, this applies to the case when contrast agents that contain K-absorption edges in the energy range of interest are present in the object. This separation is enabled via the use of recently developed energy resolved photoncounting detectors with multiple thresholds, which allow simultaneous measurements of the x-ray attenuation at multiple energies. Methods: To demonstrate this capability, we performed simulations and physical experiments using a six-threshold energy resolved photon-counting detector. We imaged mouse-sized cylindrical phantoms filled with several soft-tissue-like and bone-like materials and with iodine-based and gadolinium-based contrast agents. The linear attenuation coefficients were reconstructed for each material in each energy window and were visualized as scatter plots between pairs of energy windows. For comparison, a dual-kVp CT was also simulated using the same phantom materials. In this case, the linear attenuation coefficients at the lower kVp were plotted against those at the higher kVp. Results: In both the simulations and the physical experiments, the contrast agents were easily separable from other soft-tissue-like and bone-like materials, thanks to the availability of the attenuation coefficient measurements at more than two energies provided by the energy resolved photon-counting detector. In the simulations, the amount of separation was observed to be proportional to the concentration of the contrast agents; however, this was not observed in the physical experiments due to limitations of the real detector system. We used the angle between pairs of attenuation coefficient vectors in either the 5-D space ͑for non-contrast-agent materials using energy resolved photon-counting acquisition͒ or a 2-D space ͑for contrast agents using energy resolved photon-counting acquisition and all materials using dual-kVp acquisition͒ as a measure of the degree of separation. Compared to dual-kVp techniques, an energy resolved detector provided a larger separation and the ability to separate different target materials using measurements acquired in different energy window pairs with a single x-ray exposure. Conclusions: We concluded that x-ray CT with an energy resolved photon-counting detector with more than two energy windows allows the separation of more than two types of materials, e.g., soft-tissue-like, bone-like, and one or more materials with K-edges in the energy range of interest. Separating material types using energy resolved photon-counting detectors has a number of advantages over dual-kVp CT in terms of the degree of separation and the number of materials that can be separated simultaneously.

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High resolution X-ray imaging using a silicon strip detector

Cited by 44 publications

References 10 publications

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

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

Least squares parameter estimation methods for material decomposition with energy discriminating detectors

Material separation in x‐ray CT with energy resolved photon‐counting detectors

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