Table of contents

The Compton camera can simultaneously acquire images of multiple isotopes injected in a body; therefore, it has the potential to introduce a new subfield in the field of biomedical imaging applications. The objective of this study is to assess the ability of a prototype semiconductor-based silicon/cadmium telluride (Si/CdTe) Compton camera to simultaneously image the distributions of technetium (^99mTc)-dimercaptosuccinic acid (DMSA) (141 keV emission) and ¹⁸F-fluorodeoxyglucose (FDG) (511 keV emission) injected into a human volunteer.

^99mTc-DMSA and ¹⁸F-FDG were injected intravenously into a 25-year-old male volunteer. The distributions of ^99mTc-DMSA and ¹⁸F-FDG were simultaneously made visible by setting a specified energy window for each radioisotope. The images of these radiopharmaceuticals acquired using the prototype Compton camera were superimposed onto computed tomography images for reference.

The reconstructed image showed that ^99mTc-DMSA had accumulated in both kidneys, which is consistent with the well-known diagnostic distribution determined by clinical imaging via single-photon emission computed tomography. In the ¹⁸F-FDG image, there is broad distribution around the liver and kidneys, which was expected based on routine clinical positron emission tomography imaging.

The current study demonstrated for the first time that the Si/CdTe Compton camera was capable of simultaneously imaging the distributions of two radiopharmaceuticals, ^99mTc-DMSA and ¹⁸F-FDG, in a human body. These results suggest that the Si/CdTe Compton camera has the potential to become a novel modality for nuclear medical diagnoses enabling multi-probe simultaneous tracking.

This paper presents a practical method for converting dose measured with thermoluminescent dosimeters (TLD) to dose in lung and bone for 6 MV and 15 MV photon beams. Monte Carlo (MC) simulations and Burlin cavity theory calculations were performed to calculate , the dose-to-TLD to dose-to-medium conversion factor. A practical method was proposed for converting TLD-measured-dose to dose-in-medium using the TLD dose calibration in water and dose-to-medium to dose-to-water conversion factor. Theoretical calculations for were performed using photon spectrum weighted parameters and were compared with MC simulations. Verification of the proposed method was done using phantoms having either bone or lung equivalent slabs stacked in between solid water slabs. Percent depth dose (PDD) curves were measured using 0.089 cm thick LiF:Mg,Ti (TLD-100) dosemeters placed at various depths within these phantoms. They were then corrected with factors using the proposed dose conversion method, and were compared with the MC simulations. For 6 MV beam, the MC calculated factors were 0.942 and 1.002 for bone and lung, and for 15 MV it was 0.927 and 1.005 for bone and lung, respectively. The difference between the MC simulated and spectrum weighted theoretical factors were within 3% for both lung and bone. The PDD curves measured with TLD-100 chips that were corrected using the proposed method agreed well within 1.5% of the MC simulated PDD curves for both the water/lung/water and water/bone/water (WBW) phantoms. The dose-to-medium correction using MC simulated is convenient, easy, and accurate. Therefore, it can be used instead of Burlin cavity theory, especially in media with high atomic numbers such as bone for accurate dose quantification.

As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide an overview of the method to medical physics researchers interested in DL to help them start the endeavor. Second, we will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions. As such, we divide the article into two major parts. The first part introduces general concepts and principles of DL and summarizes major research resources, such as computational tools and databases. The second part discusses challenges faced by DL, present available methods to mitigate some of these challenges, as well as our recommendations.

This article reviews the development and summarizes the state-of-the-art in absorbed dose calorimetry for all the common clinical beam modalities covered in reference dosimetry codes of practice, as well as for small and nonstandard fields, and brachytherapy. It focuses primarily on work performed in the last ten years by national laboratories and research institutions and is not restricted to primary standard instruments. The most recent absorbed dose calorimetry review article was published over twenty years ago by Ross and Klassen (1996Phys. Med. Biol. 41 1–29), and even then, its scope was limited to water calorimeters. Since the application of calorimetry to the measurement of radiation has a long and often overlooked history, a brief introduction into its origins is provided, along with a summary of some of the landmark research that have shaped the current landscape of absorbed dose calorimeters. Technical descriptions of water and graphite calorimetry are kept general, as these have been detailed extensively in relatively recent review articles (e.g. McEwen and DuSautoy (2009Metrologia46 S59–79) and Seuntjens and Duane (2009Metrologia46 S39–58). The review categorizes calorimeters by the radiation type for which they are applied; from the widely established standards for Co-60 and high-energy x-rays, to the prototype calorimeters used in high-energy electrons and hadron therapy. In each case, focus is placed on the issues and constraints affecting dose measurement in that beam type, and the innovations developed to meet these requirements. For photons, electrons, proton and carbon ion beams, a summary of the ionization chamber beam quality conversion factors (k_Q) determined using said calorimeters is also provided. The article closes with a look forward to some of the most promising new techniques and areas of research and speculates about the future clinical role of absorbed dose calorimetry.

The purpose of this study is to propose a reconstruction method of a target and its neighborhood, representative of the moment of radiotherapy delivery, based on differences in its transit images between the time of planning computed tomography (pCT) and the time of treatment beam delivery. To validate the method, a lung phantom with a target object was constructed, and CT-scanned before and after making a shift of the target. The latter scan was intended to simulate a potential organ movement at the time of treatment, and to serve as ground-truth images. Treatment planning using arc-beam delivery was done on the first pCT images. The planned beams were irradiated to the phantom after the shift, while cine transit images were acquired. Cine transit images were also calculated through the pCT images before the shift. From the ratio of the measured and calculated transit images, the amount of image changes due to the organ movement between the time of pCT and that of treatment was three-dimensionally reconstructed. By adding the reconstructed images to the pCT images before the shift, the CT images of the phantom at the time of the beam delivery were generated and compared with the ground truth images. The phantom after the shift was also scanned by on-board cone-beam computer tomography (CBCT) and reconstructed from the measured transit images (MVCT) for comparison. The proposed method reconstructed images that are very close to the ground-truth images in the volume and HU values of the target and the dose-volume coverage of the target and lung. Similar agreement was not found in the CBCT and MVCT images. The method may be used for 4D target image reconstruction, and, combined with the reconstructed image of un-irradiated areas, may offer clinically useful images of the entire region of interest.

Recently, it has been proposed that a mixed helium/carbon beam could be used for online monitoring in carbon ion beam therapy. Fully stripped, the two ion species exhibit approximately the same mass/charge ratio and hence could potentially be accelerated simultaneously in a synchrotron to the same energy per nucleon. At the same energy per nucleon, helium ions have about three times the range of carbon ions, which could allow for simultaneous use of the carbon ion beam for treatment and the helium ion beam for imaging. In this work, measurements and simulations of PMMA phantoms as well as anthropomorphic phantoms irradiated sequentially with a helium ion and a carbon ion beam at equal energy per nucleon are presented. The range of the primary helium ion beam and the fragment tail of the carbon ion beam exiting the phantoms were detected using a novel range telescope made of thin plastic scintillator sheets read out by a flat-panel CMOS sensor. A 10:1 carbon to helium mixing ratio is used, generating a helium signal well above the carbon fragment background while adding little to the dose delivered to the patient. The range modulation of a narrow air gap of 1 mm thickness in the PMMA phantom that affects less than a quarter of the particles in a pencil beam were detected, demonstrating the achievable relative sensitivity of the presented method. Using two anthropomorphic pelvis phantoms it is shown that small rotations of the phantom as well as simulated bowel gas movements cause detectable changes in the helium/carbon beam exiting the phantom. The future prospects and limitations of the helium/carbon mixing as well as its technical feasibility are discussed.

Depth-of-interaction (DOI) encoding can contribute to improving spatial resolution uniformity and sensitivity in positron-emission-tomography (PET) scanners. In addition, time-of-flight (TOF) PET scanners with DOI encoding have received considerable interest because of their potential for improving the spatial resolution, sensitivity, and image quality of the overall system. In this study, a new DOI detector configuration utilizing scintillators' emission wavelength is proposed, and experimental results on the energy, timing, and DOI performance of the detector are provided.

The DOI information from the proposed phoswich-type detector can be acquired at the detector level without complex signal processing by utilizing a single optical filter with customized optical properties. For this, we used either a short pass filter (SPF) or a long pass filter (LPF) that allows light photons of a specific wavelength to pass. The two-layered phoswich detector was configured with two scintillators with different photon-emission spectra. In this study, we used Ce:GAGG (3 mm  ×  3 mm  ×  10 mm) and LYSO:Ce (3 mm  ×  3 mm  ×  10 mm) as the top and bottom layer scintillators, respectively. A digital silicon photomultiplier (dSiPM) was used as the photosensor and for data acquisition. The phoswich detector was placed in the center of two dSiPM pixels, where one of the dSiPM pixels was covered with the optical filter, and the light guide was placed on the other pixel. The detector was tested for energy, timing, and DOI encoding performance.

When an SPF was used, the energy resolutions of 16.2% and 11.8% were achieved for the Ce:GAGG (top layer) and LYSO:Ce (bottom layer) respectively without correcting for saturation effect. With a small (3 mm  ×  3 mm  ×  5 mm) LYSO crystal as the reference detector, CRTs (coincidence-resolving times) of 338 ps and 244 ps were recorded for the top and bottom layers respectively. The detector configuration also provides an excellent DOI-separation figure-of-merit (FoM) value of 1.9.

In the case of LPF, the energy resolutions of 12.0% and 12.9% were achieved for the Ce:GAGG (top layer) and LYSO:Ce (bottom layer), respectively. CRTs (coincidence resolving times) of 314 ps and 263 ps were recorded for the top and bottom layers, respectively. The DOI-separation FoM value of 1.5 was achieved in this setup.

Results show that the proposed method can provide excellent discrete DOI positioning accuracy without compromising the timing performance of the detector.

Compton cameras are gamma-ray imaging systems which have been proposed for a wide variety of applications such as medical imaging, nuclear decommissioning or homeland security. In the design and optimization of such a system Monte Carlo simulations play an essential role. In this work, we propose a generic module to perform Monte Carlo simulations and analyses of Compton Camera imaging which is included in the open-source GATE/Geant4 platform. Several digitization stages have been implemented within the module to mimic the performance of the most commonly employed detectors (e.g. monolithic blocks, pixelated scintillator crystals, strip detectors...). Time coincidence sorter and sequence coincidence reconstruction are also available in order to aim at providing modules to facilitate the comparison and reproduction of the data taken with different prototypes. All processing steps may be performed during the simulation (on–the–fly mode) or as a post–process of the output files (offline mode). The predictions of the module have been compared with experimental data in terms of energy spectra, angular resolution, efficiency and back-projection image reconstruction. Consistent results within a 3-sigma interval were obtained for the energy spectra except for low energies where small differences arise. The angular resolution measure for incident photons of 1275 keV was also in good agreement between both data sets with a value close to 13°. Moreover, with the aim of demonstrating the versatility of such a tool the performance of two different Compton camera designs was evaluated and compared.

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder–decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.

Recently, new studies have shown that combined laser and ultrasound, or photo-mediated ultrasound therapy (PUT), can enhance cavitation in optically absorptive targets to disrupt tissues through photoacoustic (PA) effect. These studies, including both experimental and theoretical investigations, have largely focused on blood vessels, which are modeled as cylindrically-shaped optical absorbers for PA wave generation and propagation. However, in many clinical situations, target tissues may not be cylindrically-shaped. In this paper we investigated the effect of PUT on a slab-shaped optical absorber, much larger than the size of the laser beam or the ultrasound focal point. Our results demonstrated that laser light could generate a PA wave that could enhance cavitation not only at the surface of a slab, but also at depths when combined with ultrasound, suggesting that PUT may be effective in enhancing cavitation in a large range of soft tissues. Our results also demonstrated that the cavitation enhancement was based on the optical absorption of the targeted tissue, allowing for self-targeting treatments when optical contrast is present. Additionally, we demonstrated that for the greatest cavitation enhancement in deeper layers a focused laser beam geometry would be most effective.

Electrical properties (EP), namely conductivity and permittivity, can provide endogenous contrast for tissue characterization. Using electrical property tomography (EPT), maps of EP can be generated from conventional MRI data. This report investigates the feasibility and accuracy of EPT at 21.1 T for multiple RF coils and modes of operation using phantoms. Additionally, it demonstrates the EP of the in vivo rat brain with and without ischemia.

Helmholtz-based EPT was implemented in its Full-form, which demands the complex field, and a simplified form requiring either just the field phase for conductivity or the field magnitude for permittivity. Experiments were conducted at 21.1 T using birdcage and saddle coils operated in linear or quadrature transceive mode, respectively. EPT approaches were evaluated using a phantom, ex and in vivo Sprague-Dawley rats under naïve conditions and ischemic stroke via transient middle cerebral artery occlusion.

Different conductivity reconstruction approaches applied to the phantom displayed average errors of 12%–73% to the target acquired from dielectric probe measurements. Permittivity reconstructions showed higher agreement and an average 3%–8% error to the target depending on reconstruction approach. Conductivity and permittivity of ex and in vivo rodent brain were measured. Elevated EP in the ischemia region correlated with the increased sodium content and the influx of water intracellularly following ischemia in the lesion were detected.

The Full-form technique generated from the linear birdcage provided the best accuracy for EP of the phantom. Phase-based conductivity and magnitude-based permittivity mapping provided reasonable estimates but also demonstrated the limitations of Helmholtz-based EPT at 21.1 T. Permittivity reconstruction was improved significantly over lower fields, suggesting a novel metric for in vivo brain studies. EPT applied to ischemic rat brain proved sensitivity to physiological changes, motivating the future application of more advanced reconstruction approaches.

To better understand how radiotherapy delivery parameters affect the depletion of circulating lymphocytes in patients treated for intra-cranial tumors, we developed a computational human body blood flow model (BFM), that enables to estimate the dose to the circulating blood during the course of fractionated radiation therapy.

A hemodynamic cardiovascular system based on human body reference values was developed to distribute the cardiac output to 24 different organs, described by a discrete Markov Chain. For explicit intracranial blood flow modeling, we extracted major cerebral vasculature from MRI data of a patient and complemented them with an extension network of generic vessels in the frontal and occipital lobes to guarantee even overall blood supply to the entire brain volume. An explicit Monte Carlo simulation was implemented to track the propagation of each individual blood particle (BP) through the brain and time-dependent radiation fields, accumulating dose along their trajectories.

The cerebral model includes 1050 path lines and explicitly simulates more than 266 000 BP at any given time that are tracked with a time resolution of 10 ms. The entire BFM for the whole body contains 22 178 000 BP, corresponding to 4200 BP per ml of blood. We have used the model to investigate the difference between proton and photon therapy, and the effect of different dose rates and patient characteristics on the dose to the circulating blood pool.

The mean dose to the blood pool is estimated to be 0.06 and 0.13 Gy after 30 fractions of proton and photon therapy, respectively, and the highest dose to 1% of blood was found to be 0.19 Gy and 0.34 Gy. The fraction of blood volume receiving any dose after the first fraction is significantly lower for proton therapy, 10.1% compared to 18.4% for the photon treatment plan. 90% of the blood pool will have received dose after the 11th fraction using photon therapy compared to the 21st fraction with proton therapy. Higher dose rates can effectively reduce the fraction of blood irradiated to low doses but increase the amount of blood receiving high doses. Patient characteristics such as blood pressure, gender and age lead to smaller effects than variations in the dose rate.

We developed a 4D human BFM including recirculating to estimate the radiation dose to the circulating blood during intracranial treatment and demonstrate its application to proton- versus photon-based delivery, various dose rates and patient characteristics. The radiation dose estimation to the circulating blood provides us better insight into the origins of radiation-induced lymphopenia.

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT of the brain is to compensate for aberrations and attenuation in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method can be employed that can model the heterogeneous elastic properties of the medium. In this study, an optimization-based image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward-adjoint operator pair based on a finite-difference time-domain discretization of the 3D elastic wave equation is utilized to compute penalized least squares estimates of the initial pressure distribution. Computer-simulation and experimental studies are conducted to investigate the robustness of the reconstruction method to model mismatch and its ability to effectively resolve cortical and superficial brain structures.

The 3D/2D registration of pre-operative computed tomography angiography (CTA) and intra-operative x-ray angiography (XRA) images in vascular intervention is imperative for guiding surgical instruments and reducing the dosage of toxic contrast agents. In this study, 3D/2D vascular registration is formulated as a search tree problem on the basis of the topological continuity of vessels and the fact that matching can be decomposed into continuous states. In each node of the tree, a closed-solution of 3D/2D transformation is used to obtain the registration results based on the dense correspondences of vessel points, and the results of matching and registration are calculated and recorded. Then, a hand-crafted score that quantifies the qualities of matching and registration of vessels is used, and the remaining problem focuses on finding the highest score in the search tree. An improved heuristic tree search strategy is also proposed to find the best registration. The proposed method is evaluated and compared with four state-of-the-art methods. Experiments on simulated data demonstrate that our method is insensitive to initial pose and robust to noise and deformation. It outperforms other methods in terms of registering real model data and clinical coronary data. In the 3D/2D registration of uninitialized and initialized coronary arteries, the average registration errors are 1.85 and 1.79 mm, respectively. Given that the proposed method is independent of the initial pose, it can be used to navigate vascular intervention for clinical practice.

Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensities to PET µ-map values (e.g. modality artifacts, implanted devices, or contrast agents). This work presents a deep learning-based attenuation correction (DL-AC) method to generate attenuation corrected PET (AC PET) from non-attenuation corrected PET (NAC PET) images for whole-body PET imaging, without the use of structural information. 3D patch-based cycle-consistent generative adversarial networks (CycleGAN) is introduced to include NAC-PET-to-AC-PET mapping and inverse mapping from AC PET to NAC PET, which constrains NAC-PET-to-AC-PET mapping to be closer to one-to-one mapping. Since NAC PET images share similar anatomical structures to the AC PET image but lack contrast information, residual blocks, which aim to learn the differences between NAC PET and AC PET, are used to construct generators of CycleGAN. After training, patches from NAC PET images were fed into NAC-PET-to-AC-PET mapping to generate DL-AC PET patches. DL-AC PET image was then reconstructed through patch fusion. We conducted a retrospective study on 55 datasets of whole-body PET/CT scans to evaluate the proposed method. In comparing DL-AC PET with original AC PET, average mean error (ME) and normalized mean square error (NMSE) of the whole-body were 0.62%  ±  1.26% and 0.72%  ±  0.34%. The average intensity changes measured on sequential PET images with AC and DL-AC on both normal tissues and lesions differ less than 3%. There was no significant difference of the intensity changes between AC and DL-AC PET, which demonstrate DL-AC PET images generated by the proposed DL-AC method can reach a same level to that of original AC PET images. The method demonstrates excellent quantification accuracy and reliability and is applicable to PET data collected on a single PET scanner or hybrid platform with computed tomography (PET/CT) or magnetic resonance imaging (PET/MRI).

To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features.

We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model.

In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90  ±  0.02 for the training, 0.88  ±  0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837).

The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.

Using Cherenkov radiation in positron emission tomography (PET) has the potential to improve the time of flight (TOF) resolution and reduce the cost of detectors. In previous studies promising TOF results were achieved when lead fluoride (PbF₂) crystals were used instead of a scintillator. In this work, a whole-body PbF₂ Cherenkov TOF-PET scanner was simulated and optimized. Different configurations of the PbF₂ crystals and their surface treatment were considered. Also evaluated was the influence of the crystal-photodetector coupling and of the detection efficiency of the photodetectors. Of special interest is a whole-body PbF₂ Cherenkov TOF-PET scanner with a multi-layer detector, which improves the time resolution and reduces the parallax error, without compromising the detection efficiency. Images of a phantom were reconstructed for different configurations of the simulated whole-body PbF₂ Cherenkov TOF-PET scanner and the quality of images was compared to that of a whole-body TOF-PET scanner with standard LSO scintillators. The TOF resolution of the whole-body PbF₂ Cherenkov TOF-PET scanner with a multi-layer detector was 143 ps FWHM, out of which the fundamental limitation due to light production and transportation was only 22 ps FWHM.

Algorithm benchmarking and characterization are an important part of algorithm development and validation prior to clinical implementation. However, benchmarking may be limited to a small collection of test cases due to the resource-intensive nature of establishing 'ground-truth' references. This study proposes a framework for selecting test cases to assess algorithm and workflow equivalence. Effective test case selection may minimize the number of ground-truth comparisons required to establish robust and clinically relevant benchmarking and characterization results.

To demonstrate the proposed framework, we clustered differences between two independent workflows estimating during-treatment dose objective violations for 15 head and neck cancer patients (15 planning CTs, 105 on-unit CBCTs). Each workflow used a different deformable image registration algorithm to estimate inter-fractional anatomy and contour changes. The Hopkins statistic tested whether workflow output was inherently clustered and k-medoid clustering formalized cluster assignment. Further statistical analyses verified the relevance of clusters to algorithm output. Data at cluster centers ('medoids') were considered as candidate test cases representative of workflow-relevant algorithm differences.

The framework indicated that differences in estimated dose objective violations were naturally grouped (Hopkins  =  0.75, providing 90% confidence). K-medoid clustering identified five clusters which stratified workflow differences (MANOVA: p   <  0.001) in estimated parotid gland D50%, spinal cord/brainstem Dmax, and high dose CTV coverage dose violations (Kendall's tau: p   <  0.05). Systematic algorithm differences resulting in workflow discrepancies were: parotid gland volumes (ANOVA: p   <  0.001), external contour deformations (t-test: p   =  0.022), and CTV-to-PTV margins (t-test: 0.009), respectively. Five candidate test cases were verified as representative of the five clusters.

The framework successfully clustered workflow outputs and identified five test cases representative of clinically relevant algorithm discrepancies. This approach may improve the allocation of resources during the benchmarking and characterization process and the applicability of results to clinical data.

To provide Monte Carlo calculated beam quality correction factors (k_Q) for monoenergetic proton beams using , a toolkit based on the Monte Carlo code .

Monte Carlo simulations of six plane-parallel and four cylindrical ionization chambers were carried out. The latest ICRU 90 recommendations on key data for ionizing-radiation dosimetry were used to calculate the electronic stopping powers and to select the mean energy necessary to create an ion pair in air ().

factors were calculated for a ⁶⁰Co spectrum at a depth of 5 g cm⁻². f _Q factors and ratios as well as k_Q factors were calculated at the entrance region of monoenergetic proton beams with energies between 60 MeV and 250 MeV.

Additionally, perturbation correction factors for the Exradin A1SL ionization chamber at an energy of 250 MeV were calculated.

factors agreed within 0.7% or better, f _Q factors within 1.7% or better and ratios within 2.2% or better with Monte Carlo calculated values provided in the literature. Furthermore, k_Q factors calculated in this work were found to agree within 1% or better with experimentally determined k_Q factors provided in the literature, with only two exceptions with deviations of 1.4% and 2.4%.

The total perturbation correction factor for the Exradin A1SL chamber was 0.969(7) and hence significantly different than unity in contrast to the assumption from the IAEA TRS-398 code of practice (CoP).

can be used to calculate k_Q factors in clinical proton beams. k_Q factors for six plane-parallel and four cylindrical ionization chambers were calculated and provided for the upcoming update of the IAEA TRS-398 CoP.

K-edge subtraction (KES) imaging is a technique able to map a specific element such as e.g. a contrast agent within the tissues, by exploiting the sharp rise of its absorption coefficient at the K-edge energy. Whereas mainly explored at synchrotron radiation sources, the energy discrimination properties of modern x-ray photon counting detectors (XPCDs) pave the way for an implementation of single-shot KES imaging with conventional polychromatic sources. In this work we present an x-ray CT imaging system based on the innovative Pixie-III detector and discrete reconstruction. The results reported here show that a reliable automatic localization of Barium (above a certain concentration) is possible with a few dozens of tomographic projections for a volume having an axial slice of 512 512 pixels. The final application is a routine high-fidelity 3D mapping of a specific element ready for further morphological quantification by means of x-ray CT with potential promising applications in vivo.