Technical Note: Assessment of Pulse Pileup on Single-Energy and Multienergy Images From a Clinical Photon-Counting Detector Computed Tomography.

OBJECTIVE: Pulse pileup effects occur when pulses occur so close together that they fall on top of one another, resulting in count loss and errors in energy thresholding. To date, there has been little work systematically detailing the quantitative effects of pulse pileup on material decomposition accuracy for photon-counting detector (PCD) computed tomography (CT). Our aim in this work was to quantify the effects of pulse pileup on single-energy and multienergy CT images, including low-energy bin (BL), high-energy bin (BH), iodine map, and virtual noncontrast images from a commercial PCD-CT. METHODS: Scans of a 20-cm diameter multienergy CT phantom with 10 solid inserts were acquired at a fixed tube potential as the tube current was varied across the available range. Four types of images (BL, BH, iodine map, and virtual noncontrast) were reconstructed using an iterative reconstruction algorithm at strength 2, a quantitative reconstruction kernel (Qr40), 2-/1-mm slice thickness/increment, and a 210-mm field-of-view. The mean and standard deviation of CT numbers were recorded and the ratios of CT number between BL and BH images were calculated and plotted, along with noise versus tube current and noise × versus tube current. RESULTS: As tube current was increased, the range of variations in CT numbers was less than 13.4 HU for all inserts and image types evaluated. Noise × versus tube current showed a small positive slope equal to a noise increase from 100 mA of 10% at 500 mA and 15% at 900 mA compared with what would be expected if the slope was zero. CONCLUSIONS: Minimal impact on single-energy and multienergy CT numbers and noise performance was observed for the evaluated clinical PCD-CT system.

Three-dimensional printing accuracy analysis for medical applications across a wide variety of printers.

Purpose: Three-dimensional (3D) printing has had a significant impact on patient care. However, there is a lack of standardization in quality assurance (QA) to ensure printing accuracy and precision given multiple printing technologies, variability across vendors, and inter-printer reliability issues. We investigated printing accuracy on a diverse selection of 3D printers commonly used in the medical field. Approach: A specially designed 3D printing QA phantom was periodically printed on 16 printers used in our practice, covering five distinct printing technologies and eight different vendors. Longitudinal data were acquired over six months by printing the QA phantom monthly on each printer. Qualitative assessment and quantitative measurements were obtained for each printed phantom. Accuracy and precision were assessed by comparing quantitative measurements with reference values of the phantom. Data were then compared among printer models, vendors, and printing technologies; longitudinal trends were also analyzed. Results: Differences in 3D printing accuracy across printers were observed. Material jetting and vat photopolymerization printers were found to be the most accurate. Printers using the same 3D printing technology but from different vendors also showed differences in accuracy, most notably between vat photopolymerization printers from two different vendors. Furthermore, differences in accuracy were found between printers from the same vendor using the same printing technology, but different models/generations. Conclusions: These results show how factors such as printing technology, vendor, and printer model can impact 3D printing accuracy, which should be appropriately considered in practice to avoid potential medical or surgical errors.

Construction of a Nearly Unbiased Statistical Estimator of Sinogram to Address CT Number Bias Issues in Low-Dose Photon Counting CT.

Photon counting detector (PCD)-CT has demonstrated promise to reduce ionizing radiation exposure further and improve spatial resolution. However, when the radiation exposure or the detector pixel size is reduced, image noise is elevated, and the CT number becomes more inaccurate. This exposure level-dependent CT number inaccuracy is referred to as statistical bias. The issue of CT number statistical bias is rooted in the stochastic nature of the detected photon number, N, and a log transformation used to generate the sinogram projection data. Due to the nonlinear nature of the log transform, the statistical mean of the log-transformed data is different from the desired sinogram, the log transform of the statistical mean of N. Consequently, when a single instance of N is measured, as in clinical imaging, the log-transform leads to an inaccurate sinogram and statistically biased CT numbers after reconstruction. This work presents a nearly unbiased and closed-form statistical estimator of sinogram as a simple yet highly effective method to address the statistical bias issue in PCD-CT. Experimental results validated that the proposed approach addresses the CT number bias problem and improves the quantification accuracy of both non-spectral and spectral PCD-CT images. Additionally, the process can slightly reduce noise without adaptive filtering or iterative reconstruction.