Photon-counting CT: Technical Principles and Clinical Prospects

Willemink, Martin J.; Persson, Mats; Pourmorteza, Amir; Pelc, Norbert J.; Fleischmann, Dominik

doi:10.1148/radiol.2018172656

Cited by 866 publications

(676 citation statements)

References 98 publications

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“…The performance of multi‐contrast imaging relies on the degree to which the acquired multi‐energy datasets represent unique distributions of photon energies. Limitations of PCD‐CT technology, such as K‐escape, charge sharing, and pulse pileup, cause the energy response of each energy bin to deviate considerably from the ideal response . The resulting imperfect separation of the acquired energy distributions (i.e., the presence of spectral overlap) degrades the performance of multi‐contrast imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Limitations of PCD-CT technology, such as K-escape, charge sharing, and pulse pileup, cause the energy response of each energy bin to deviate considerably from the ideal response. [31][32][33][34] The resulting imperfect separation of the acquired energy distributions (i.e., the presence of spectral overlap) degrades the performance of multi-contrast imaging. With EID-based CT, DE datasets can be acquired by using two sets of tube/detector pairs (i.e., DSCT), with each pair operating at different tube potentials.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

et al. 2019

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Purpose Photon‐counting‐detector‐computed tomography (PCD‐CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K‐edge escape, and pulse pile‐up, which compromise spectral separation of multi‐energy data and degrade multi‐contrast imaging performance. The purpose of this work was to determine the performance of a dual‐source (DS) PCD‐CT relative to a single‐source (SS) PCD‐CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents. Methods Phantom experiments were performed using a research whole‐body PCD‐CT and head/abdomen‐sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS‐PCD‐CT, the phantoms were scanned twice on the SS‐PCD‐CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low‐energy tube, while the high‐energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS‐PCD‐CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess‐board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy‐bin data sets, which permits simultaneous multi‐contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre‐defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS‐PCD‐CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K‐edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy‐bin images obtained from each scan (scan pair) were used to generate I‐, Gd‐, Bi‐specific image via material decomposition. Root‐mean‐square‐error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20‐cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi‐specific images. Results Phantom results showed that DS‐PCD‐CT reduced noise in material‐specific images for both head and body phantoms compared to SS‐PCD‐CT. The noise level of SS‐PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS‐PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS‐PCD‐CT was reduced relative to DS‐PCD‐CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS‐PCD) to 0.72 (DS‐PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi). Co...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

et al. 2019

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“…Energy‐resolved photon‐counting detectors enable spectral computed tomography (CT) and continue to show promising developments . Several research prototypes have been developed, and studies comparing photon‐counting CT scanners to those with conventional energy integrating detectors have been performed .…”

Section: Introductionmentioning

confidence: 99%

“…Energy-resolved photon-counting detectors enable spectral computed tomography (CT) and continue to show promising developments. 5 Several research prototypes have been developed, and studies comparing photon-counting CT scanners to those with conventional energy integrating detectors have been performed. 6,7 As the sensor and electronic technologies continue to advance, the clinical advantages of photon-counting CT, such as lower dose and increased contrast and material separability, may surpass that of scanners with conventional detectors.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

et al. 2020

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Purpose Spectral computed tomography (CT) material decomposition algorithms require accurate physics‐based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CT imaging of K‐edge contrast agents using simulated and experimental measurements. Methods A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench‐top photon‐counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look‐up tables. Results The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data and simulations estimated 3.26, 6.26, and 12.45 mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88 mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images. Conclusions The results demonstrate that empirical K‐edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.

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“…With this advanced technology, ErCT can improve contrast-to-noise ratio, decrease electronic noise, and improve dose efficiency, compared with EiCT [1]. Due to technical limitation, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion [2]. Therefore, this makes researchers less accessible to the ErCT images compared to the EiCT images available.…”

Section: Introductionmentioning

confidence: 99%

Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network

Yao

Sui

Wei

et al. 2019

2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrating CT (EiCT) images via deep neural network. Specifically, different from other networks that produce ErCT images at one specific energy, this model employs a unified generative adversarial network (uGAN) to concurrently train EiCT datasets and ErCT datasets with different energies and then performs image-to-image translation from existing EiCT images to multiple ErCT image outputs at various energy bins. In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image slices and show that the present uGAN model can produce promising ErCT estimation results compared with the ground truth qualitatively and quantitatively.

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Photon-counting CT: Technical Principles and Clinical Prospects

Cited by 866 publications

References 98 publications

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network

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