Disclosed is a calibration phantom for an x-ray imaging system having an x-ray source and an x-ray detector. The calibration phantom includes a combination of geometric objects of at least three different types and/or compositions including: a first object located in the middle, including a first material; a plurality of second objects arranged around the periphery of the first object, at least a subset of the second objects including a second material different than the first material, wherein the first object is relatively larger than the second objects; a plurality of third objects arranged around the periphery of the first object and/or around the periphery of at least a subset of the second objects, at least a subset of the third objects including a third material different than the first material and the second material, wherein the third objects are relatively smaller than the second objects.

An x-ray imaging apparatus includes an x-ray source and detector with multiple detector elements. The source and detector are on a support that rotates around a subject, enabling projections at different view angles. The apparatus operates the x-ray source in switched kVp mode for alternately applying different voltages, including lower and higher voltages, during rotation to enable lower-energy and higher-energy exposures over the projections, providing for lower-energy projections and higher-energy projections. The x-ray detector is a photon-counting multi-bin detector allocating photon counts to multiple energy bins, and the apparatus selects counts from at least a subset of the bins to provide corresponding photon count information for both lower- and higher-energy projections. The apparatus performs material basis decomposition for some of the lower-energy projections and higher-energy projections and/or for some combinations of at least one lower-energy projection and at least one higher-energy projection, based on the corresponding photon count information.

The detector pixel size can be a severe limitation in x-ray imaging of fine details in the human body. We demonstrate a method of using spectral x-ray measurements to image the spatial distribution of the linear attenuation coefficient on a length scale smaller than one pixel, based on the fact that interfaces parallel to the x-ray beam have a unique spectral response, which distinguishes them from homogeneous materials. We evaluate the method in a simulation study by simulating projection imaging of the border of an iodine insert with 200 mg/ml I in a soft tissue phantom. The results show that the projected iodine profile can be recovered with an RMS resolution of 5% to 34% of the pixel size, using an ideal energy-resolving detector. We also validate this method in an experimental study by imaging an iodine insert in a polyethylene phantom using a photon-counting silicon-strip detector. The results show that abrupt and gradual transitions can be distinguished based on the transmitted x-ray spectrum, in good agreement with simulations. The demonstrated method may potentially be used for improving visualization of blood vessel boundaries, e.g., in acute stroke care.

Purpose: The highest photon fluence rate that a computed tomography (CT) detector must be able to measure is an important parameter. The authors calculate the maximum transmitted fluence rate in a commercial CT scanner as a function of patient size for standard head, chest, and abdomen protocols. Methods: The authors scanned an anthropomorphic phantom (Kyoto Kagaku PBU-60) with the reference CT protocols provided by AAPM on a GE LightSpeed VCT scanner and noted the tube current applied with the tube current modulation (TCM) system. By rescaling this tube current using published measurements on the tube current modulation of a GE scanner [N. Keat, "CT scanner automatic exposure control systems," MHRA Evaluation Report 05016, ImPACT, London, UK, 2005], the authors could estimate the tube current that these protocols would have resulted in for other patient sizes. An ECG gated chest protocol was also simulated. Using measured dose rate profiles along the bowtie filters, the authors simulated imaging of anonymized patient images with a range of sizes on a GE VCT scanner and calculated the maximum transmitted fluence rate. In addition, the 99th and the 95th percentiles of the transmitted fluence rate distribution behind the patient are calculated and the effect of omitting projection lines passing just below the skin line is investigated. Results: The highest transmitted fluence rates on the detector for the AAPM reference protocols with centered patients are found for head images and for intermediate-sized chest images, both with a maximum of 3.4 . 10(8) mm(-2) s-1, at 949 mm distance from the source. Miscentering the head by 50 mm downward increases the maximum transmitted fluence rate to 5.7 . 10(8) mm(-2) s(-1). The ECG gated chest protocol gives fluence rates up to 2.3 . 10(8)-3.6 . 10(8) mm(-2) s(-1) depending on miscentering. Conclusions: The fluence rate on a CT detector reaches 3 . 10(8)-6 . 10(8) mm(-2) s(-1) in standard imaging protocols, with the highest rates occurring for ECG gated chest and miscentered head scans. These results will be useful to developers of CT detectors, in particular photon counting detectors. (C) 2016 American Association of Physicists in Medicine.

Material basis decomposition in the sinogram domain requires accurate knowledge of the forward model in spectral computed tomography (CT). Misspecifications over a certain limit will result in biased estimates and make quantum limited (where statistical noise dominates) quantitative CT difficult. We present a method whereby users can determine the degree of allowed misspecification error in a spectral CT forward model and still have quantification errors that are limited by the inherent statistical uncertainty. For a particular silicon detector based spectral CT system, we conclude that threshold determination is the most critical factor and that the bin edges need to be known to within 0.15 keV in order to be able to perform quantum limited material basis decomposition. The method as such is general to all multibin systems.

The variation among energy thresholds in a multibin detector for photon-counting spectral CT can lead to ring artefacts in the reconstructed images. Calibration of the energy thresholds can be used to achieve homogeneous threshold settings or to develop compensation methods to reduce the artefacts. We have developed an energy-calibrationmethod for the different comparator thresholds employed in a photon-counting silicon-strip detector. In our case, this corresponds to specifying the linear relation between the threshold positions in units of mV and the actual deposited photon energies in units of keV. This relation is determined by gain and offset values that differ for different detector channels due to variations in the manufacturing process. Typically, the calibration is accomplished by correlating the peak positions of obtained pulse-height spectra to known photon energies, e. g. with the aid of mono-energetic x rays from synchrotron radiation, radioactive isotopes or fluorescence materials. Instead of mono-energetic x rays, the calibrationmethod presented in this papermakes use of a broad x-ray spectrum provided by commercial x-ray tubes. Gain and offset as the calibration parameters are obtained by a regression analysis that adjusts a simulated spectrum of deposited energies to ameasured pulse-height spectrum. Besides the basic photon interactions such as Rayleigh scattering, Compton scattering and photo-electric absorption, the simulation takes into account the effect of pulse pileup, charge sharing and the electronic noise of the detector channels. We verify the method for different detector channels with the aid of a table-top setup, where we find the uncertainty of the keV-value of a calibrated threshold to be between 0.1 and 0.2 keV.

There is provided a method of image reconstruction based on energy-resolved image data from a photon-counting multi-bin detector or an intermediate storage. The method comprises processing (S1) the energy-resolved image data by performing at least two separate basis decompositions using different number of basis functions for modeling linear attenuation, wherein a first basis decomposition is performed using a first smaller set of basis functions to obtain at least one first basis image representation, and wherein a second basis decomposition is performed using a second larger set of basis functions to obtain at least one second basis image representation. The method also comprises reconstructing a first image based on said at least one first basis image representation obtained from the first basis decomposition, and combining the first image with information representative of said at least one second basis image representation.