Robust Energy Calibration Technique for Photon Counting Spectral Detectors

Vespucci, S.; Park, Chan Soo; Torrico, Raul; Das, Mini

doi:10.1109/tmi.2018.2875932

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…However, DE image contrast may be inferior using this approach since the mean energy separation between the two images is small 79–81 . More recently, there has been interest in the use of photon counting detectors to provide spectral separation which may be used to remove overlaying bone on kV‐projections 82–86 . Furthermore, machine learning approaches, especially deep learning approaches, have been applied to segment kV‐projections 87–92 …”

Section: Discussionmentioning

confidence: 99%

“…[79][80][81] More recently, there has been interest in the use of photon counting detectors to provide spectral separation which may be used to remove overlaying bone on kV-projections. [82][83][84][85][86] Furthermore, machine learning approaches, especially deep learning approaches, have been applied to segment kVprojections. [87][88][89][90][91][92]…”

Section: F I G U R Ementioning

confidence: 99%

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The markerless lung target tracking AAPM Grand Challenge (MATCH) results

et al. 2021

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Purpose: Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study. Methods: MATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were

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Section: Discussionmentioning

confidence: 99%

Section: F I G U R Ementioning

confidence: 99%

The markerless lung target tracking AAPM Grand Challenge (MATCH) results

et al. 2021

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“…This includes an electronic discriminator to exclude photons above a given threshold energy value selected by the experimenter. Using efficient energy-calibration methods, the experimenter can accurately link these electronic thresholds to the corresponding photon energy [15].…”

Section: Introductionmentioning

confidence: 99%

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors

Lewis

Das

2019

Sensors

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Energy-resolving photon-counting detectors (PCDs) separate photons from a polychromatic X-ray source into a number of separate energy bins. This spectral information from PCDs would allow advancements in X-ray imaging, such as improving image contrast, quantitative imaging, and material identification and characterization. However, aspects like detector spectral distortions and scattered photons from the object can impede these advantages if left unaccounted for. Scattered X-ray photons act as noise in an image and reduce image contrast, thereby significantly hindering PCD utility. In this paper, we explore and outline several important characteristics of spectral X-ray scatter with examples of soft-material imaging (such as cancer imaging in mammography or explosives detection in airport security). Our results showed critical spectral signatures of scattered photons that depend on a few adjustable experimental factors. Additionally, energy bins over a large portion of the spectrum exhibit lower scatter-to-primary ratio in comparison to what would be expected when using a conventional energy-integrating detector. These important findings allow flexible choice of scatter-correction methods and energy-bin utilization when using PCDs. Our findings also propel the development of efficient spectral X-ray scatter correction methods for a wide range of PCD-based applications.

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“…A quadratic energy calibration was performed for the energy thresholds using the 59.5 keV gamma emission from americium-241, the 122 keV gamma emission from cobalt-57 and using the DIR method for energies of 40 keV to 80 keV. 6 With this calibration, the low energy threshold used to suppress electronic noise was set at 17 keV while the HE threshold was set to 32 keV and was incremented by steps of approximately 6.5 keV to approximately 90% of the maximum energy of the beam (E max ). For each HE threshold and tube voltage combination, ten LE and HE images were taken using the high-sensitivity anti-coincidence mode on the PCD, which implements a charge sharing correction on the images.…”

Section: Imaging Parametersmentioning

confidence: 99%

Experimental optimization of photon-counting dual-energy thoracic imaging

Dhari

Tanguay

2023

Medical Imaging 2023: Physics of Medical Imaging

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Dual-energy (DE) thoracic imaging with photon counting detectors (PCDs) enables suppressing anatomic noise due to bone structures, potentially improving the detection of lung nodules. Relative to DE approaches based on the kV-switching method, a PCD-based, single-exposure approach may reduce the potential for motion artifacts and suppress electronic noise. In this work, we experimentally optimize the energy thresholds and tube voltages for single-exposure DE thoracic imaging implemented with PCDs. We used a phantom consisting of uniform acrylic slabs totaling 8.6 cm in thickness with a 16 cm air gap between slabs. A lung nodule was simulated by adding 2 mm of acrylic in a small region of interest in the center of the phantom. We imaged the phantom using a PCD with a 750-μm-thick cadmium telluride x-ray convertor, 100×100 μm2 detector elements, two user-defined energy thresholds, and analog charge summing for charge sharing correction. We acquire images for tube voltages ranging from 90 kV to 130 kV in 10-kV increments. For each tube voltage, we varied the energy threshold from 32 keV to 90% of the maximum beam energy. The contrast-to-noise ratio (CNR) of the simulated nodule was measured for each combination of tube voltage and energy threshold. Optimal energy thresholds range from 45 keV to 55 keV for tube voltages ranging from 110 kV to 120 kV. When optimized for the energy thresholds, there was relatively little variability in the CNR across tube voltages beyond that due to measurement error. Future work will compare the CNR of PCD-based DE radiography with the conventional kV-switching approach.

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Robust Energy Calibration Technique for Photon Counting Spectral Detectors

Cited by 13 publications

References 30 publications

The markerless lung target tracking AAPM Grand Challenge (MATCH) results

The markerless lung target tracking AAPM Grand Challenge (MATCH) results

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors

Experimental optimization of photon-counting dual-energy thoracic imaging

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