Background: Photon-counting computed tomography (CT) bears promise to substantially improve spectral and spatial resolution. One reason for the relatively slow evolution of photon-counting detectors in CT—the technology has been used in nuclear medicine and planar radiology for decades—is pulse pileup, that is, the random staggering of pulses, resulting in count loss and spectral distortion, which in turn cause image bias and reduced contrast-to-noise ratio (CNR). The deterministic effects of pileup can be mitigated with a pileup-correction algorithm, but the loss of CNR cannot be recovered, and must be minimized by hardware design. In the deep-silicon photon-counting detector, each pixel is split into depth segments, which enables optimization of the count rate per detector channel to reduce pileup. Virtual clinical trials are attracting growing interest for efficient evaluation of cutting-edge technology like the deep-silicon design, but a virtual trial requires an accurate simulation model of the imaging system, a digital twin, which captures all relevant aspects of the system over the full spectrum of clinical applications. Purpose: We are developing a framework for digital twins of deep-silicon photon-counting CT to enable in-silico system evaluation and virtual clinical trials of the technology. The primary purpose of this study is to validate the framework with respect to pileup, that is, it is not a validation of the detector performance, but a validation of the correspondence between simulation and measurements from a prototype device. A secondary purpose is to employ the framework for investigating the impact of pileup on image quality and the effectiveness of a data-driven pileup correction algorithm. Methods: A pileup model that simulates individual photon events in accordance with the semi-nonparalyzable detector behavior was integrated into the CatSim environment. Measured count data from a prototype deep-silicon system were used to validate the simulation framework with respect to pileup. A typical image chain was integrated into the framework, including material decomposition (MD) and data-driven pileup correction. Images of a software phantom were generated to illustrate the effect of pileup on images and to assess the effectiveness of the pileup correction algorithm. Results: Simulated data were described well by the semi-nonparalyzable detector model and exhibited deviations to the measured count rate and variance of less than 5% across energy bins and depth segments, and a wide range of tube currents. The investigated pileup correction algorithm suppressed artifacts to below the noise level in monochromatic images and material images, and reduced iodine bias from 26% to 2% in the range from a factor of 3 lower to a factor of 1.7 higher than the calibrated count rate without impacting CNR. Conclusions: The observed discrepancies are reasonable given known uncertainties, and the model provides a reliable representation of the pileup effect. The framework for digital twins helped confirm adequate performance of the pileup correction algorithm, which can reduce the need for repeated MD calibrations in mA-modulated scans. Next steps include simulation speed up and expansion of the framework to other detector effects.

Purpose: Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps. Approach: The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net. Results: Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data. Conclusions: These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

There is provided an arrangement including an x-ray detector arranged in conjunction with in-line x-ray focusing optics configured for manipulation of x-rays in medical transmission radiography, wherein the in-line x-ray optics includes an array of lenses, in which the lenses cover parts of, or the entire, field of view, and in which the x-ray detector is a photon-counting detector. Furthermore, the x-ray detector is an energy-resolving detector and chromatic aberration of the lens array and/or limited coherence of the source is compensated for by the energy resolution of the energy-resolving detector, and/or the x-ray detector is a depth-resolving detector and chromatic aberration of the lens array and/or limited coherence of the source is compensated for by depth resolution or volumetric resolution in the detector.

Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. In mammography, measurement of breast density, dose estimation, and differentiation between cysts and solid tumours are example applications requiring accurate data on tissue attenuation. Published attenuation data are, however, sparse and cover a relatively wide range. To supplement available data we have previously measured the attenuation of cyst fluid and solid lesions using photon-counting spectral mammography. The present study aims to measure the attenuation of normal adipose and glandular tissue, and to measure the effect of formalin fixation, a major uncertainty in published data. A total of 27 tumour specimens, 7 fibro-glandular tissue specimens, and 15 adipose tissue specimens were included. Spectral (energy-resolved) images of the samples were acquired and the image signal was mapped to equivalent thicknesses of two known reference materials, from which x-ray attenuation as a function of energy can be derived. The spread in attenuation between samples was relatively large, partly because of natural variation. The variation of malignant and glandular tissue was similar, whereas that of adipose tissue was lower. Formalin fixation slightly altered the attenuation of malignant and glandular tissue, whereas the attenuation of adipose tissue was not significantly affected. The difference in attenuation between fresh tumour tissue and cyst fluid was smaller than has previously been measured for fixed tissue, but the difference was still significant and discrimination of these two tissue types is still possible. The difference between glandular and malignant tissue was close-to significant; it is reasonable to expect a significant difference with a larger set of samples. We believe that our studies have contributed to lower the overall uncertainty of breast tissue attenuation in the literature due to the relatively large sample sets, the novel measurement method, and by clarifying the difference between fresh and fixed tissue.

Spectral imaging is an umbrella term for energy-resolved x-ray imaging in medicine. The technique makes use of the energy dependence of x-ray attenuation to either increase the contrast-to-noise ratio, or to provide quantitative image data and reduce image artefacts by so-called material decomposition. Spectral imaging is not new, but has gained interest in recent years because of rapidly increasing availability of spectral and dual-energy CT and the dawn of energy-resolved photon-counting detectors. This review examines the current technological status of spectral and dual-energy imaging and a number of practical applications of the technology.

An x-ray imaging system includes an x-ray source, an x-ray detector including a plurality of detector strips arranged in a first direction of the x-ray detector. Each detector strip includes a plurality of detector pixels arranged in a second direction of the x-ray detector. A phase grating and a plurality of analyzer gratings including grating slits are disposed between the x-ray source and detectors. The x-ray source and the x-ray detector are adapted to perform a scanning movement in relation to an object in the first direction, in order to scan the object. Each of the plurality of analyzer gratings (162) is arranged in association with a respective detector strip with the grating slits arranged in the second direction. The grating slits of the analyzer gratings of the detector strips are offset relative to each other in the second direction.  

Measurements of breast density have the potential to improve the efficiency and reduce the cost of screening mammography through personalized screening. Breast density has traditionally been evaluated from the dense area in a mammogram, but volumetric assessment methods, which measure the volumetric fraction of fibro-glandular tissue in the breast, are potentially more consistent and physically sound. The purpose of the present study is to evaluate a method for measuring the volumetric breast density using photon-counting spectral tomosynthesis. The performance of the method was evaluated using phantom measurements and clinical data from a small population (n=18). The precision was determined to 2.4 percentage points (pp) of volumetric breast density. Strong correlations were observed between contralateral (R2=0.95) and ipsilateral () breast-density measurements. The measured breast density was anti-correlated to breast thickness, as expected, and exhibited a skewed distribution in the range [3.7%, 55%] and with a median of 18%. We conclude that the method yields promising results that are consistent with expectations. The relatively high precision of the method may enable novel applications such as treatment monitoring. 

Purpose: Beam-quality optimization in digital mammography traditionally considers detection of a target obscured by quantum noise in a homogeneous background. This does not correspond well to the clinical imaging task because real mammographic images contain a complex superposition of anatomical structures, resulting in anatomical noise that may dominate over quantum noise. The purpose of this paper is to assess the influence on optimal beam quality in mammography when anatomical noise is taken into account. Methods: The detectability of microcalcifications and masses was quantified using a theoretical ideal-observer model that included quantum noise as well as anatomical noise and a simplified model of a photon-counting mammography system. The outcome was experimentally verified using two types of simulated tissue phantoms. Results: The theoretical model showed that the detectability of tumors and microcalcifications behaves differently with respect to beam quality and dose. The results for small microcalcifications were similar to what traditional optimization methods yield, which is to be expected because quantum noise dominates over anatomical noise at high spatial frequencies. For larger tumors, however, low-frequency anatomical noise was the limiting factor. Because anatomical structure noise has similar energy dependence as tumor contrast, the optimal x-ray energy was found to be higher and the useful energy region was wider than traditional methods suggest. A simplified scalar model was able to capture this behavior using a fitted noise mixing parameter. The phantom measurements confirmed these theoretical results. Conclusions: It was shown that since quantum noise constitutes only a small fraction of the noise, the dose could be reduced substantially without sacrificing tumor detectability. Furthermore, when anatomical noise is included, the tube voltage can be increased well beyond what is conventionally considered optimal and used clinically, without loss of image quality. However, no such conclusions can be drawn for the more complex mammographic imaging task as a whole. (C) 2014 American Association of Physicists in Medicine.