Accurate CT numbers obtained from Dual-energycomputed tomography (DECT), may improve the accuracy of radiation treatment planning. Our Dual energy Iterative Reconstruction Algorithm (DIRA) utilizes material decompositionin the iterative loop to produce accurate CT numbers of the imaged object. DIRA produces two mono-energy images. These mono-energies correspond to the effective energies of the two X-ray spectra. In simulations, the resulting CT numbers in the reconstructed images have shown to agree well with tabulated values. The aim of this work is to see if DIRA can also be used on real data. Compared to simulated data, the following adjustments were needed; i) Elimination of patient table. ii) Bow-tie filter modeling and adaptation to this in DIRA. iii) Application of inverse water beam hardening correction. A Siemens SOMATOM Force scanner was used to scan a cylindrical PMMA phantom with four rod inserts made of different materials. For simlicity, an axial scan was used. 6 iterations of DIRA was applied, where the 0th iteration is our own implementation of filtered backprojection (FBP). DIRA at iteration 6 gave values close to the tabulated ones. DIRA also performed well on an anthropomorphic head phantom (CIRS model 731-HN), with specified material composition.

The choice of the material base to which the material decomposition is performed in dual-energy computed tomography may affect the quality of reconstructed images. Resulting inaccuracies may lower their diagnostic value, or if the data are used for radiation treatment planning, the accuracy of such plans. The aim of this work is to investigate how the commonly used (water, bone) (WB), (water, iodine) (WI), and (approximate photoelectric effect, Compton scattering) (PC) doublets affect the reconstructed linear attenuation coefficient in the case of the Alvarez–Macovski (AM) method. The performance of this method is also compared to the performance of the dual-energy iterative reconstruction algorithm DIRA. In both cases, the study is performed using simulations.

The results show that the PC and WB doublets accurately predicted the linear attenuation coefficient (LAC) values for human tissues and elements with Z = 1, …, 20, in the 20–150 keV range, though there was a small (<5% discrepancy in the 20–35 keV range. The WI doublet did not represent the tissues as well as PC and WB; the largest discrepancies (>50% in some cases) were in the 20–40 keV range.

LACs reconstructed with the AM and DIRA followed this trend. AM produced artifacts when iodine was present in the phantom together with human tissues since AM can only work with one doublet at a time. It was shown that these artifacts could be avoided with DIRA using different doublets at different spatial positions, i.e., WB for soft and bone tissue and WI for the iodine solution.

Dual-energy computed tomography (CT) can be used in radiotherapy treatment planning for the calculation of absorbed dose distributions. The aim of this work is to evaluate whether there is room for improvement in the accuracy of the Monoenergetic Plus algorithm by Siemens Healthineers. A Siemens SOMATOM Force scanner was used to scan a cylindrical polymethyl methacrylate phantom with four rod-inserts made of different materials. Images were reconstructed using ADMIRE and processed with Monoenergetic Plus. The resulting CT numbers were compared with tabulated values and values simulated by the proof-of-a-concept algorithm DIRA developed by the authors. Both the Monoenergetic Plus and DIRA algorithms performed well; the accuracy of attenuation coefficients was better than about ±1% at the energy of 70 keV. Compared with DIRA, the worse performance of Monoenergetic Plus was caused by its (i) two-material decomposition to iodine and water and (ii) imperfect suppression of the beam hardening artifact in ADMIRE.

Cerium oxide nanoparticles with integrated gadolinium have been proved to be useful as contrast agents in magnetic resonance imaging. Of question is their performance in dual-energy computed tomography. The aims of this work are to determine (1) the relation between the computed tomography number and the concentration of the I, Gd or Ce contrast agent and (2) under what conditions it is possible to resolve the type of contrast agent. Hounsfield values of iodoacetic acid, gadolinium acetate and cerium acetate dissolved in water at molar concentrations of 10, 50 and 100 mM were measured in a water phantom using the Siemens SOMATOM Definition Force scanner; gadolinium- and cerium acetate were used as substitutes for the gadolinium-integrated cerium oxide nanoparticles. The relation between the molar concentration of the I, Gd or Ce contrast agent and the Hounsfield value was linear. Concentrations had to be sufficiently high to resolve the contrast agents.

The choice of the material base to which the material decomposition is performed in dual-energy computed tomography may affect the quality of reconstructed images. The aim of this work is to investigate how the commonly used bases (water, bone), (water, iodine) and (photoelectric effect, Compton scattering) affect the reconstructed linear attenuation coefficient in the case of the Alvarez–Macovski method. The performance of this method is also compared with the performance of the Dual-energy Iterative Reconstruction Algorithm (DIRA). In both cases, the study is performed using simulations. The results show that the Alvarez–Macovski method produced artefacts when iodine was present in the phantom together with human tissues since this method can only work with one doublet. It was shown that these artefacts could be avoided with DIRA using the (water, bone) doublet for tissues and the (water, iodine) doublet for the iodine solution.

Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction algorithms where it improves the accuracy of resulting CT numbers. Of special interest are pelvic bones, which—because of their strong attenuation—affect the accuracy of brachytherapy in this region. This work evaluated the performance of the JJ2016 algorithm with the performance of MK2014v2 and JS2018 algorithms; all these algorithms were developed by authors. Visual comparison, and, in the latter case, also Dice similarity coefficients derived from the ground truth were used. It was found that the 3D-based JJ2016 performed better than the 2D-based MK2014v2, mainly because of the more accurate hole filling that benefitted from information in adjacent slices. The neural network-based JS2018 outperformed both traditional algorithms. It was, however, limited to the resolution of 1283 owing to the limited amount of memory in the graphical processing unit (GPU).

Purpose An important characteristic of radiation dosimetry detectors is their energy response which consists of absorbed-dose and intrinsic energy responses. The former can be characterized using Monte Carlo (MC) simulations, whereas the latter (i.e., detector signal per absorbed dose to detector) is extracted from experimental data. Such a characterization is especially relevant when detectors are used in nonrelative measurements at a beam quality that differs from the calibration beam quality. Having in mind the possible application of synthetic diamond detectors (microDiamond PTW 60019, Freiburg, Germany) for nonrelative dosimetry of low-energy brachytherapy (BT) beams, we determined their intrinsic and absorbed-dose energy responses in 25-250 kV beams relative to a Co-60 beam, which is usually the reference beam quality for detector calibration in radiotherapy. Material and Methods Three microDiamond detectors and, for comparison, two silicon diodes (PTW 60017) were calibrated in terms of air-kerma free in air in six x-ray beam qualities (from 25 to 250 kV) and in terms of absorbed dose to water in a Co-60 beam at the national metrology laboratory in Sweden. The PENELOPE/penEasy MC radiation transport code was used to calculate the absorbed-dose energy response of the detectors (modeled based on blueprints) relative to air and water depending on calibration conditions. The MC results were used to extract the relative intrinsic energy response of the detectors from the overall energy response. Measurements using an independent setup with a single ophthalmic BEBIG I25.S16 I-125 BT seed (effective photon energy of 28 keV) were used as a qualitative check of the extracted intrinsic energy response correction factors. Additionally, the impact of the thickness of the active volume as well as the presence of extra-cameral components on the absorbed-dose energy response of a microDiamond detector was studied using MC simulations. Results The relative intrinsic energy response of the microDiamond detectors was higher by a factor of 2 in 25 and 50 kV beams compared to the Co-60 beam. The variation in the relative intrinsic energy response of silicon diodes was within 10% over the investigated photon energy range. The use of relative intrinsic energy response correction factors improved the agreement among the absorbed dose to water values determined using microDiamond detectors and silicon diodes, as well as with the TG-43 formalism-based calculations for the I-125 seed. MC study of microDiamond detector design features provided a possible explanation for inter-detector response variation at low-energy photon beams by differences in the effective thickness of the active volume. Conclusions MicroDiamond detectors had a non-negligible variation in the relative intrinsic energy response (factor of 2) which was comparable to that in the absorbed-dose energy response relative to water at low-energy photon beams. Silicon diodes, in contrast, had an absorbed-dose energy dependence on photon energy that varied by a factor of 6, whereas the intrinsic energy dependence on beam quality was within 10%. It is important to decouple these two responses for a full characterization of detector energy response especially when the user and reference beam qualities differ significantly, and MC alone is not enough.

Deep learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging. The aim of this work is to design and evaluate an algorithm capable of segmenting bones in dual-energy CT data sets. A convolutional neural network based on the 3D U-Net architecture was implemented and evaluated using high tube voltage images, mixed images and dual-energy images from 30 patients. The network performed well on all the data sets; the mean Dice coefficient for the test data was larger than 0.963. Of special interest is that it performed better on dual-energy CT volumes compared to mixed images that mimicked images taken at 120 kV. The corresponding increase in the Dice coefficient from 0.965 to 0.966 was small since the enhancements were mainly at the edges of the bones. The method can easily be extended to the segmentation of multi-energy CT data.

Modern X-ray units register the air kerma–area product, PKA, with a built-in KAP meter. Some KAP meters show an energydependent bias comparable with the maximum uncertainty articulated by the IEC (25 %), adversely affecting dose-optimisation processes. To correct for the bias, a reference KAP meter calibrated at a standards laboratory and two calibration methods described here can be used to achieve an uncertainty of <7 % as recommended by IAEA. A computational model of the reference KAP meter is used to calculate beam quality correction factors for transfer of the calibration coefficient at the standards laboratory, Q0, to any beam quality, Q, in the clinic. Alternatively, beam quality corrections are measured with an energy-independent dosemeter via a reference beam quality in the clinic, Q1, to beam quality, Q. Biases up to 35 % of built-in KAP meter readings were noted. Energy-dependent calibration factors are needed for unbiased PKA. Accurate KAP meter calibration as a prerequisite for optimisation in projection radiography.

Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate.