Dead-time correction for spectroscopic photon-counting pixel detectors

Clinical applications of photon counting detector CT

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

Detection Probability Analysis of True Random Coding Photon Counting Lidar

With the wide application of lidar in the future, the problem of crosstalk between lidars will become more serious. True random coding photon counting lidar with high anti-crosstalk ability will play an important role in solving this problem. In this paper, based on the working principle of Gm-APD, the detection probability theoretical model of true random coding photon counting lidar is built, and the impact of jitter on detection probability is considered for the first time. The influence of mean echo photon number, mean pulse count density, sequence length and pulse width on detection probability is analyzed. Monte Carlo simulation and experimental results are highly consistent with the theoretical model, which proves the correctness of the detection probability theoretical model. This theoretical model provides an effective means to evaluate the system performance.

Simultaneous Dual-Contrast Imaging of Small Bowel With Iodine and Bismuth Using Photon-Counting-Detector Computed Tomography: A Feasibility Animal Study

OBJECTIVES

Dual-energy and multienergy computed tomography (DECT/MECT) has the potential to simultaneously visualize two contrast agents in the small bowel: arterial enhancement of iodine in the bowel wall and enteric enhancement of bismuth in the bowel lumen. The purpose of this study was to explore its feasibility in a swine study using a research whole-body photon-counting-detector (PCD) computed tomography (CT) system.

MATERIALS AND METHODS

A phantom study was initially performed to evaluate the quantification accuracy of iodine and bismuth separation from a single PCD-CT scan, which also served as the calibration reference for material decomposition of in vivo swine PCD-CT data. In the animal study, a test bolus scan was first performed to determine the time-attenuation curve for the arterial enhancement, based on which the timing of the PCD-CT dual-contrast scan was determined. A 600 mL homogeneous bismuth-saline solution (180 mL Pepto-Bismol + 420 mL normal saline) was orally administered to the pig using esophageal intubation. Approximately 1 hour after bismuth administration, 40 mL iodine contrast (Omnipaque 350, 5 mL/s) was injected intravenously. A PCD-CT scan was performed 13 seconds after the initiation of the contrast injection to simultaneously capture the arterial enhancement of iodine and the enteric enhancement of bismuth. To provide optimal material separation and quantification, all PCD-CT scans in both phantom and animal studies were operated at 140 kV with 4 energy thresholds of 25, 50, 75, and 90 keV.

RESULTS

Using a generic image-based material decomposition method, the iodine and bismuth samples were successfully delineated and quantified in the phantom images with a root-mean-square-error of 1.32 mg/mL in iodine measurement and 0.64 mg/mL in bismuth measurement. In the pig study, the enhancing bowel wall containing iodine and the small bowel loop containing bismuth were not differentiable in the original PCD-CT images. However, they were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. In addition, quantitative analysis showed that the misclassification between the two contrast materials was less than 1.0 mg/mL.

CONCLUSIONS

Our study demonstrated the feasibility of simultaneous imaging of iodine and bismuth in small bowel of swine using PCD-CT.

Gamma detector dead time correction using Lambert W function

Background

For therapeutic applications of several isotopes (e.g., ¹³¹I, ¹⁵³Sm, ¹⁷⁷Lu) in nuclear medicine, the high activities typically applied require accurate dead time correction in early time point imaging. We present a novel, straightforward dead time correction method using the Lambert W function, which is in principle exact for the paralyzable detector model with a single parameter τ (i.e., dead time).

Results

As a proof of concept, the method is validated with a simple model: a commonly used isotope, ^99mTc, with a single photopeak. We measured count rates of a gamma camera both intrinsically and extrinsically (i.e., with collimators) with point sources in air and in a scatter phantom (extrinsic only). τ was estimated for both open window (τ_OW) and a ^99mTc photopeak window (τ_Tc), using a “graphical” method for fitting the count rate of decaying sources. These values for τ were subsequently used for dead time correction.

τ varied significantly between the different geometries for both energy windows, but τ_OW was more reproducible than τ_Tc, particularly for the scatter phantom measurements.

τ_OW measured from the phantom measurements was approximately 30% lower than τ_OW from the intrinsic measurement but corresponded within 15% with the extrinsic point source measurements. Accordingly, using the intrinsic τ_OW led to an overcorrection of 8% at high count rates; τ_OW from the extrinsic point source measurements corrected the phantom measurement to within 2%.

However, significant differences were observed between τ_Tc values. All measured τ_Tc values underestimated dead time losses in a second independent phantom measurement, with even τ_Tc from the first phantom measurement underestimating activity with 5–10% at the highest count rates. Based on measurements of the effect of energy window settings and geometry, we tentatively attribute the added dead time losses to pulse pile-up.

Conclusions

Analytic dead time correction based on the Lambert W function is accurate for the range in which gamma detectors behave as paralyzable systems. However, further investigation indicated measured τ values to be variable with geometry as well as window fraction. We propose that dead time correction should be based on the open window value, τ_OW, corrected for window fraction.