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The aim of this study is to implement lifetime attributable risk (LAR) predictions for radiation induced cancers for Swedish cohorts of patients of various age and sex, undergoing diagnostic investigations by nuclear medicine methods. Methods: Calculations are performed on Swedish groups of patients with Paget's disease and with bone metastases from prostatic cancer and diagnosed with bone scintigraphy with an administration of 500 MBq 99mTc-phosphonate. Results: The inclusion of patient survival rates into the calculations lowers the induced radiation cancer risk, as it takes into account that cohorts of patients have shorter predicted survival times than the general population. Conclusion: LAR estimations could be valuable for referring physicians, nuclear medicine physicians, nurses, medical physicists, radiologists, and oncologists and as well as ethical committees for risk estimates for specific subgroups of patients. Caution is however advised with respect to application of LAR predictions to individuals (because of individual sensitivities, circumstances, etc.).

Denoising reconstruction techniques can introduce nonlinear properties into computed tomography (CT) systems. These nonlinear algorithms introduce distortion which affects the assessment of the resolution of the system. The purpose of the present study was to decouple and investigate amplitude modulation and waveform distortion in reconstruction algorithms in CT. The methodology developed by Wells, J. R. and Dobbins, J. T. III [Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context. Med. Phys. 40, 091906 (2013)] was adapted to CT reconstruction algorithms. The CT simulating program ASTRA Toolbox© for MATLAB™ was used for the reconstruction of the sinusoidal wave functions. Filtered back projection and the simultaneous iterative reconstruction technique were investigated with simple nonlinear mechanisms: a median filter and a non-negative constraint, respectively. The native reconstruction algorithms were not free from nonlinear waveform distortion, however, none of the metrics showed any dependence on the contrast-to-noise ratio (CNR). Furthermore, the algorithms including nonlinear mechanisms showed a clear and specific CNR dependence, indicating the necessity for distortion analysis in nonlinear CT reconstruction.

The purpose of the present work was to evaluate the use of resampling statistical methods for analysis of visual grading data—implemented in the software VGC Analyzer—by comparing the reanalyzed results from previously performed visual grading studies with the results calculated by gold standard receiver operating characteristic (ROC) methodology, Obuchowski-Rockette (OR)-Dorfman–Berbaum–Metz (DBM) multiple-readers and multiple-case (MRMC) and by analysis of simulated visual grading data where the true distribution was presumed to be known. The reanalysis was performed on two multiple-reader studies with non-paired data and paired data, respectively. The simulation study was performed by simulating a large number of visual grading characteristics (VGC) studies and by analyzing the statistical distribution of null hypothesis (H0) rejection rate. The comparison with OR-DBM MRMC showed good agreement when analyzing non-paired data for both fixed-reader and random-reader settings for the calculated area under the curve values and the confidence intervals (CIs). For paired data analysis, VGC Analyzer showed significantly lower CIs compared with the ROC software. This effect was also illustrated by the simulation study, where the VGC Analyzer, in general, showed good accuracy for simulated studies with stable statistical basis. For simulated studies with unstable statistics, the accuracy in the H0 rejection rate decreased. The present study has shown that resampling methodology can be used to accurately perform the statistical analysis of a VGC study, although the resampling technique used makes the method sensitive to small data sets.

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.

Objective The aim of the study was to evaluate the ionizing radiation exposure in patients with Coronavirus disease 2019 (COVID-19). Materials and Methods This was a retrospective study in which all patients presented with suggestive symptoms of COVID-19 were included. The study was carried out in a university-affiliated private hospital in Istanbul, Turkey. Biological radiation dose exposure (cumulative effective dose: CED) was evaluated in millisievert (mSv) units. Results A total of 1410 patients were included in the study. Of all study subjects, 804 patients (57%) underwent only one chest computed tomography (CT) procedure. Six hundred and six patients (43%) had two or more chest CT procedures. Median CED was 6.02 (min–max:1.67–16.27) mSv. The number of patients who were exposed to ≤ 5 mSv were 149 (24.6%), whereas 457 patients (75.4%) were exposed to >5 mSv. Conclusion The radiation exposure in COVID-19 patients seems unjustifiably high. Awareness should be increased as to the proper use of chest CT in COVID-19 as per to the society recommendations.

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.

In contrast to optical colonoscopy, computed tomography colonography (CTC) has the ability to reveal pathology outside of the colon. While identification of colorectal lesions at CTC requires only limited radiation dose, the detection of abnormalities in extracolonic soft tissue requires more radiation. The purpose of this study was to investigate the influence of ultra-low-dose (ULD) CTC on the detection and characterisation of extracolonic findings. In a prospective study 49 patients with colorectal symptoms were examined with CTC adding a ULD series (mean effective dose 0.9 ± 0.4 mSv) to the normal unenhanced standard dose (SD) series (mean effective dose 3.6 ± 1.2 mSv). Five radiologists individually and blindly evaluated the ULD, followed by evaluation of the SD after ≥9 weeks (median 35 weeks). A ViewDEX-based examination protocol was used, including a confidence scale and a graded assessment of need for follow-up according to the CTC Reporting and Data System (C-RADS E0–E4). The reference findings comprised the combined information from CTC (ULD, SD and contrast-enhanced CTC series) and a 4-year radiological and clinical follow-up. For the overall detection of reference findings (E2–E4) we found a statistically significant difference in favour of SD. This, however, was not the case when looking at classification of possibly important/important reference findings (E3–E4). Our results suggest that CTC with ULD (0.9 mSv) is comparable to SD (3.6 mSv) for identification of clinically relevant extracolonic pathology, but there is a large inter-observer variability.

Purpose: Digital tomosynthesis (DTS) is currently undergoing validation for potential clinical implications. The aim of this study was to investigate the potential for DTS as a low-dose alternative to computed tomography (CT) in imaging of pulmonary pathology in patients with cystic fibrosis (CF). Methods: DTS and CT were performed as part of the routine triannual follow-up in 31 CF patients. Extent of disease was quantified according to modality-specific scoring systems. Statistical analysis included Spearman’s rank correlation coefficient (r) and Krippendorff’s alpha (α). Major findings: The median effective dose was 0.14 for DTS and 2.68 for CT. Intermodality correlation was very strong for total score and the subscores regarding bronchiectasis and bronchial wall-thickening (r = 0.82–0.91, P

This paper summarizes the dose to the eye lens of workers of Memorial Sloan Kettering Cancer Center, a high-volume US oncologic and associated diseases facility. The doses presented in this report were collected from personal dosemeter readings using optically stimulated luminescence badges to estimate Hp(3). Doses were collected for 5950 clinical and research workers between January 2012 and December 2017. The median eye lens dose for all monitored workers was 0.23 mSv y−1. Workers performing, or supporting, fluoroscopy procedures received the highest unprotected eye lens dose of all workers with a median eye dose of 10 mSv. The use of leaded glasses by this group reduced the actual doses to the lens. Nurses and technicians involved in positron emission tomography injections received median eye lens dose of 1.2 mSv.

Quantitative mechanistic modeling of the biological effects of ionizing radiation has a long rich history. Initially, it was dominated by target theory, which quantifies damage caused by traversal of cellular targets like DNA by ionizing tracks. The discovery that mutagenesis, death and/or altered behavior sometimes occur in cells that were not themselves traversed by any radiation tracks but merely interacted with traversed cells was initially seen as surprising. As more evidence of such ‘non-targeted’ or ‘bystander’ effects accumulated, the importance of their contribution to radiation-induced damage became more recognized. Understanding and modeling these processes is important for quantifying and predicting radiation-induced health risks. Here we review the variety of mechanistic mathematical models of nontargeted effects that emerged over the past 2–3 decades. This review is not intended to be exhaustive, but focuses on the main assumptions and approaches shared or distinct between models, and on identifying areas for future research.

Pregnancy increases the risk of pulmonary embolism. Computed tomography pulmonary angiography (CTPA) is used for diagnosis. CT generates ionising radiation, and thus, abdominal shielding may be used. This phantom study investigated the effects of patient shielding and scan length reduction on the fetal and maternal ionising radiation dose from CTPA. The absorbed dose to the fetus was measured using thermoluminescent dosemeters. Estimated effective doses to the pregnant patient were based on the dose-length products. Shielding increased both the effective dose to the patient by 47% and the mean absorbed dose to the fetus (0.10 vs. 0.12 mGy; p

Estimation of cancer risk based on the organ-absorbed dose is underway for the Japanese Epidemiological Study on Low-Dose Radiation Effects (J-EPISODE). The reconstruction method for the organ-absorbed dose follows the approach adopted in the IARC 15-Country Collaborative Study, which examined the dosemeter response to photon exposure for the old film badge (FB) type, a multi-element FB and a thermoluminescence dosemeter. Until 2000, the dosemeters used in Japan were almost the same in the IARC study, so IARC study data could be used as they were. However, since 2000, the type of dosemeter has been replaced with active personal dosemeters (hereafter called electronic personal dosemeters), radio-photoluminescent glass dosemeters (Glass badge) and optically stimulated luminescence dosemeters (Luminess badge). Hence, it was necessary to collect these data again. A dosemeter response experiment was conducted using a device that irradiated an anthropomorphic phantom in the Japan Atomic Energy Agency calibration laboratories. The aim of the paper is to provide a conversion factor from reading in terms of Hp(10) to air kerma for realistic conditions for further conversion from air kerma to organ-absorbed dose. The obtained dosemeter responses for the dosemeter types currently used in Japan were consistent with those in the IARC study. These data will be utilized for J-EPISODE in reconstructing the organ-absorbed dose.

The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset. The thyroid-adjusted phantoms were implemented into the Geant4 Monte Carlo code to calculate thyroid location-dependent iodine-131 S values (rT ← thyroid) for a total of 30 target regions. The maximum variation among the observed thyroid locations was 39 mm and 36 mm for male and female patients, respectively. The mean thyroid locations of both male and female patients showed a good agreement with the ICRP reference phantoms. The thyroid location-dependent Iodine-131 S values were significantly different from the reference phantoms for most target regions by up to a factor of 3. The use of thyroid location-dependent S values in dose reconstructions should help quantify the dosimetric uncertainty in epidemiologic investigations of patients receiving iodine-131 therapy for hyperthyroidism and thyroid cancer.

This paper aims to determine the levels of exposure to neutron and photon radiation among the personnel engaged in cyclotron operation and the personnel engaged in the production of radiopharmaceuticals, with the use of the environmental radiation monitoring system (RMS) installed in the positron emission tomography laboratory. The annual exposures of employees operating the cyclotron measured with the use of the RMS system are: 1.39 ± 0.16 mSv in case of photon radiation and 2.61 ± 0.14 mSv in case of neutron radiation. In the case of employees in the radiopharmaceuticals’ production zone, the annual exposures measured by means of the RMS system are 0.15 ± 0.03 mSv in case of photon radiation and 0.11 ± 0.01 mSv in case of neutron radiation. The exposure levels among the personnel engaged in cyclotron operation and the personnel engaged in the production of radiopharmaceuticals are below the permissible radiation dose limits.

We evaluate a series of thin-sheet hydrogel molecularly imprinted polymers (MIPs), using a family of acrylamide-based monomers, selective for the target protein myoglobin (Mb). The simple production of the thin-sheet MIP offers an alternative biorecognition surface that is robust, stable and uniform, and has the potential to be adapted for biosensor applications. The MIP containing the functional monomer N-hydroxymethylacrylamide (NHMAm), produced optimal specific rebinding of the target protein (Mb) with 84.9% (± 0.7) rebinding and imprinting and selectivity factors of 1.41 and 1.55, respectively. The least optimal performing MIP contained the functional monomer N,N-dimethylacrylamide (DMAm) with 67.5% (± 0.7) rebinding and imprinting and selectivity factors of 1.11 and 1.32, respectively. Hydrogen bonding effects, within a protein-MIP complex, were investigated using computational methods and Fourier transform infrared (FTIR) spectroscopy. The quantum mechanical calculations predictions of a red shift of the monomer carbonyl peak is borne-out within FTIR spectra, with three of the MIPs, acrylamide, N-(hydroxymethyl) acrylamide, and N-(hydroxyethyl) acrylamide, showing peak downshifts of 4, 11, and 8 cm−1, respectively.

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.

One of the most common usages of radiation in current medical diagnosis is computed tomography (CT) using X-rays. The potential health risk of CT scans has been discussed in various studies to determine whether low-dose radiation from CT could enhance the chromosome aberration yields in pediatric patients and increase their risk of carcinogenesis. For this reason, it is of great interest to study the effects of low-dose radiation. The induction of DNA damage by a CT scan examination has been demonstrated in several reports by the γ-H2AX assay, the micronuclei assay and dicentrics measurements. However, the results of most studies showed limitations. On the other hand, epidemiological studies give contradictory results for post-natal radiation exposure in the low-dose range, so it is still difficult to draw conclusions about the effects of CT examinations and risk of carcinogenesis. This article provides an overview of previously published data and summarizes the current state of knowledge.

Since EMF levels from wireless telecommunication networks are non-stationary and exhibit large temporal variations, the use of continuous measurements during extended periods (preferably 24 h or longer) with a data-logging system is required. Because of the short-term variations of E field, the 6-min measurements and 6-min averaged results to obtain the mean level strength at a given place appear to be dependent on the time of measurements during the day. This paper presents a new (integral- based) measure to evaluate electromagnetic exposure. The new measure is a pure physical descriptor of the amount of exposed energy density (a parameter accumulated from instantaneous power density values in time). To confirm previous observations, continuous measurements with personal exposure metre were recorded 24 h a day for two weeks at every location in urban area, 14 different locations in total. Additionally, to check temporal variations and repeatability of exposure assessment, a week of prolonged measurements was taken 6 months later, making in total three weeks of measurements at 2 locations. Day-to-day repeatability of RF-EMF exposure was analysed through the time-averaged and integral-based measure. The analysis is based on approximately 5.1 million data samples (1.7 million for each band). The ratio between the maximum and minimum instantaneous (maximum and minimum 6-min averaged) E field values during the day could reach up to 25 dB (20 dB). Therefore, great variability in the results may occur. By applying the 24 h time-averaged and integral-based measure on a 24 h data set of measurements, the variability of daily exposure could stay within ±20% of the week mean level obtained either with the time-averaged or integral-based measure. Both, the time-averaged E field and integral-based power density exposures of the general public in all locations were found to be well below the general public exposure limits of the ICNIRP guidelines.

Library material, and thus parchment, is frequently subjected to bio-deterioration processes caused by microorganisms. Fungi and bacteria cause alterations in the parchment inducing, in some cases, even the partial detachments of the surface layer and the loss of any text present on it. An important contribution to disinfection of the cultural heritage artefacts is given by the use of ionizing radiation. In this work, a preliminary study on the applicability of X-ray radiation as treatment for bio-deterioration removal is proposed. The results on the microbial growth after different irradiation treatments are shown in order to detect the dose protocol for the bio-degradation removal. Furthermore, the evaluation of the irradiation effects on the parchment microstructure is presented in order to define the applicability of the method on parchment artefacts.

The World Health Organisation (WHO) recommends that the concentration of radon in water should be no more than 100 kBq m−3 (100 BqL−1) and the Codex Alimentarius Commission states that the limit of quantification (LOQ) of a method should be no more than one-fifth of this value. In this study, a degassing method with an RAD7 device was used to measure radon concentrations in water, compared to a liquid scintillation counter (LSC) method used as the reference, to investigate whether the numerical value of the LOQ of this method was more than 1/5 (20 kBq m–3) of 100 kBq m–3. The degassing method with leak prevention was shown to reach a target value of 20 kBq m−3 or less under a relative humidity of 6% or lower in the chamber of the RAD7 device. Accordingly, the RAD7 degassing method with leak prevention can be used to accurately measure radon concentrations in water within the guidance level set out by the WHO.

Over the past decades, the application of single-photon emission computed tomography and positron emission tomography in neuroimaging has markedly increased. In the current study, we used a series of Korean computational head phantoms with detailed cranial structures for 6-, 9-, 12-, 15-y-old children and adult and a Monte Carlo transport code, MCNPX, to calculate age-dependent specific absorbed fraction (SAF) for mono-energetic electrons ranging from 0.01 to 4 MeV and S values for seven radionuclides widely used in nuclear medicine neuroimaging for the combination of ten source and target regions. Compared to the adult phantom, the 6-y phantom showed up to 1.7-fold greater SAF (cerebellum < cerebellum) and up to 1.4-fold greater S values (vitreous body < lens) for 123I. The electron SAF data, combined with our previous photon SAF data, will facilitate absorbed dose calculations for various cranial structures in patients undergoing neuroimaging procedures.

Using data from a number of radon surveys, it was assessed that on average, radon progeny concentrations in Canadian homes are about three times higher than in school buildings, 4.7 times higher than in public buildings and indoor workplaces, and 12 times higher than in outdoor air. Canadian statistics show that most Canadians spend ~70% of their time indoors at home, 20% indoors away from home and 10% in outdoors. Due to relatively higher radon concentration in residential homes and longer time spent indoors at home, the exposure at home contributes to 90% of the radon-induced lung-cancer risk.

International audience; Ionizing radiation is a peculiar perturbation when it comes to damage to biological systems it proceeds through discrete energy depositions, over a short temporal scale and a spatial scale critical for subcellular targets as DNA, whose damage complexity determines the outcome of the exposure. This lies at the basis of the success of track structure (and nanodosimetry) and microdosimetry in radiation biology. However, such reductionist approaches cannot account for the complex network of interactions regulating the overall response of the system to radiation, particularly when effects are manifest at the supracellular level and involve long times. Systems radiation biology is increasingly gaining ground, but the gap between reductionist and holistic approaches is becoming larger. This paper presents considerations on what roles track structure and microdosimetry can have in the attempt to fill this gap, and on how they can be further exploited to interpret radiobiological data and inform systemic approaches. © The Author(s) 2018. Published by Oxford University Press.

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.

Purpose: The recent coronavirus disease 2019 (COVID-19) pandemic, which spread across the globe in a very short period of time, revealed that the transmission control of disease is a crucial step to prevent an outbreak and effective screening for viral infectious diseases is necessary. Since the severe acute respiratory syndrome (SARS) outbreak in 2003, infrared thermography (IRT) has been considered a gold standard method for screening febrile individuals at the time of pandemics. The objective of this review is to evaluate the efficacy of IRT for screening infectious diseases with specific applications to COVID-19. Approach: A literature review was performed in Google Scholar, PubMed, and ScienceDirect to search for studies evaluating IRT screening from 2002 to present using relevant keywords. Additional literature searches were done to evaluate IRT in comparison to traditional core body temperature measurements and assess the benefits of measuring additional vital signs for infectious disease screening. Results: Studies have reported on the unreliability of IRT due to poor sensitivity and specificity in detecting true core body temperature and its inability to identify asymptomatic carriers. Airport mass screening using IRT was conducted during occurrences of SARS, Dengue, Swine Flu, and Ebola with reported sensitivities as low as zero. Other studies reported that screening other vital signs such as heart and respiratory rates can lead to more robust methods for early infection detection. Conclusions: Studies evaluating IRT showed varied results in its efficacy for screening infectious diseases. This suggests the need to assess additional physiological parameters to increase the sensitivity and specificity of non-invasive biosensors.

The purpose of this study was to investigate the challenges in comparing digital radiography (DR) systems from different vendors for various combinations of exposure factors in posterior–anterior hand radiographs. Image quality was evaluated for a range of tube voltages and tube current-time products using a technical contrast-detail (CDRAD) phantom and an anthropomorphic hand phantom. 900 technical CDRAD images were analysed providing quality figures of merit (IQFinv) and two experienced reporting radiographers using visual grading analysis (VGA) scored 108 anthropomorphic images. This study demonstrates the differences between the DR systems included. When compensating for variations in dose, Canon showed superior results for technical image quality and Fuji for visual image quality for a standard dose point at DR hand examination (ln(DAP) 1.1, 50 kV and 2.5 mAs).

Purpose: The purpose of this work was to develop a new method of tracking a laparoscopic ultrasound (LUS) transducer in laparoscopic video by combining the hardware [e.g., electromagnetic (EM)] and the computer vision-based (e.g., ArUco) tracking methods. Approach: We developed a special tracking mount for the imaging tip of the LUS transducer. The mount incorporated an EM sensor and an ArUco pattern registered to it. The hybrid method used ArUco tracking for ArUco-success frames (i.e., frames where ArUco succeeds in detecting the pattern) and used corrected EM tracking for the ArUco-failure frames. The corrected EM tracking result was obtained by applying correction matrices to the original EM tracking result. The correction matrices were calculated in previous ArUco-success frames by comparing the ArUco result and the original EM tracking result. Results: We performed phantom and animal studies to evaluate the performance of our hybrid tracking method. The corrected EM tracking results showed significant improvements over the original EM tracking results. In the animal study, 59.2% frames were ArUco-success frames. For the ArUco-failure frames, mean reprojection errors for the original EM tracking method and for the corrected EM tracking method were 30.8 pixel and 10.3 pixel, respectively. Conclusions: The new hybrid method is more reliable than using ArUco tracking alone and more accurate and practical than using EM tracking alone for tracking the LUS transducer in the laparoscope camera image. The proposed method has the potential to significantly improve tracking performance for LUS-based augmented reality applications.

Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy. Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning. Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen–Dice coefficient of 0.849 ± 0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 (CI95 = 0.769 to 0.886) and 0.787 (CI95 = 0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively. Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.

When relativistic electrons are used to irradiate tissues, such as during FLASH pre-clinical irradiations, the electron beam energy is one of the critical parameters that determine the dose distribution. Moreover, during such irradiations, linear accelerators (linacs) usually operate with significant beam loading, where a small change in the accelerator output current can lead to beam energy reduction. Optimisation of the tuning of the accelerator’s radio frequency system is often required. We describe here a robust, easy-to-use device for non-interceptive monitoring of potential variations in the electron beam energy during every linac macro-pulse of an irradiation run. Our approach monitors the accelerated electron fringe beam using two unbiased aluminium annular charge collection plates, positioned in the beam path and with apertures (5 cm in diameter) for the central beam. These plates are complemented by two thin annular screening plates to eliminate crosstalk and equalise the capacitances of the charge collection plates. The ratio of the charge picked up on the downstream collection plate to the sum of charges picked up on the both plates is sensitive to the beam energy and to changes in the energy spectrum shape. The energy sensitivity range is optimised to the investigated beam by the choice of thickness of the first plate. We present simulation and measurement data using electrons generated by a nominal 6 MeV energy linac as well as information on the design, the practical implementation and the use of this monitor.

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA. Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index ( e ' ) as a task-based measure of image quality in cardiac CTA. The e ' index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA). Results: The e ' index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e ' threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training-test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective. Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.

Purpose: We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions. Approach: Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth. Results: There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively. Conclusions: Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.

Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases. ispartof: JOURNAL OF MEDICAL IMAGING vol:8 issue:Suppl 1 ispartof: location:United States status: published

Exposure levels to staff in interventional radiology (IR) may be significant and appropriate assessment of radiation doses is needed. Issues regarding measurements using physical dosemeters in the clinical environment still exist. The objective of this work was to explore the prerequisites for assessing staff radiation dose, based on simulations only. Personal dose equivalent, Hp(10), was assessed using simulations based on Monte Carlo methods. The position of the operator was defined using a 3D motion tracking system. X-ray system exposure parameters were extracted from the x-ray equipment. The methodology was investigated and the simulations compared to measurements during IR procedures. The results indicate that the differences between simulated and measured staff radiation doses, in terms of the personal dose equivalent quantity Hp(10), are in the order of 30–70 %. The results are promising but some issues remain to be solved, e.g. an automated tracking of movable parts such as the ceiling-mounted protection shield.

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.

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). Funding: VetenskapsradetSwedish Research Council [VR-NT 2016-05033]

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. Funding: VetenskapsradetSwedish Research Council [VR-NT 2016-05033]

Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community wellfor over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintainingsufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms fromseveral vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR inabdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitativemethods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlatepositively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions,ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (≈30%) patientdose reductions. The size of the dose reductions depends on the diagnostic task. Funding: ALF-grant from Region Ostergotland [LiO-602731, LIO-697941]; FoU-grant from Region Ostergotland [LIO-724631, LIO-620341]; RFoU-grant from Region Ostergotland; Medical Faculty at Linkoping University

Since 2012, the European Radiation Dosimetry Group (EURADOS) has developed its Strategic Research Agenda (SRA), which contributes to the identification of future research needs in radiation dosimetry in Europe. Continued scientific developments in this field necessitate regular updates and, consequently, this paper summarises the latest revision of the SRA, with input regarding the state of the art and vision for the future contributed by EURADOS Working Groups and through a stakeholder workshop. Five visions define key issues in dosimetry research that are considered important over at least the next decade. They include scientific objectives and developments in (i) updated fundamental dose concepts and quantities, (ii) improved radiation risk estimates deduced from epidemiological cohorts, (iii) efficient dose assessment for radiological emergencies, (iv) integrated personalised dosimetry in medical applications and (v) improved radiation protection of workers and the public. This SRA will be used as a guideline for future activities of EURADOS Working Groups but can also be used as guidance for research in radiation dosimetry by the wider community. It will also be used as input for a general European research roadmap for radiation protection, following similar previous contributions to the European Joint Programme for the Integration of Radiation Protection Research, under the Horizon 2020 programme (CONCERT). The full version of the SRA is available as a EURADOS report (www.eurados.org).

Funder: Cantab Capital Institute for the Mathematics of Information, Funder: Alan Turing Institute; doi: https://doi.org/10.13039/100012338, In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating other symmetries into deep learning methods, in the form of group equivariant convolutional neural networks. Much of this work has been focused on roto-translational symmetry of R d , but other examples are the scaling symmetry of R d and rotational symmetry of the sphere. In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach. Indeed, if the regularisation functional is invariant under a group symmetry, the corresponding proximal operator will satisfy an equivariance property with respect to the same group symmetry. As a result of this observation, we design learned iterative methods in which the proximal operators are modelled as group equivariant convolutional neural networks. We use roto-translationally equivariant operations in the proposed methodology and apply it to the problems of low-dose computerised tomography reconstruction and subsampled magnetic resonance imaging reconstruction. The proposed methodology is demonstrated to improve the reconstruction quality of a learned reconstruction method with a little extra computational cost at training time but without any extra cost at test time.

Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85±0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment.

Purpose: Alterations in the optical absorption behavior of liver tissue secondary to pathological processes can be evaluated by multispectral analysis, which is increasingly being explored as an imaging adjunct for use in liver surgery. Current methods are either invasive or have a limited wavelength spectrum, which restricts utility. This proof of concept study describes the development of a multispectral imaging (MSI) method called multispectral tissue mapping (MTM) that addresses these issues. Approach: The imaging system consists of a tunable excitation light source and a near-infrared camera. Following the development stage, proof of concept experiments are carried out where absorption spectra from colorectal cancer liver metastasis (CRLM), hepatocellular carcinoma (HCC), and liver steatosis specimen are acquired and compared to controls. Absorption spectra are compared to histopathology examination as the current gold standard for tissue assessment. Generalized linear mixed modeling is employed to compare absorption characteristics of individual pixels and to select wavelengths for false color image processing with the aim of visually enhancing cancer tissue. Results: Analysis of individual pixels revealed distinct absorption spectra therefore suggesting that MTM is possible. A prominent absorption peak at 1210 nm was found in lipid-rich animal tissues and steatotic liver specimen. Liver cancer tissue had a heterogeneous appearance on MSI. Subsequent statistical analysis suggests that measuring changes in absorption behavior may be a feasible method to estimate the pixel-based probability of cancer being present. In CRLM, this was observed throughout 1100 to 1700 nm, whereas in HCC it was concentrated around 1140 and 1430 nm. False color image processing visibly enhances contrast between cancer and normal liver tissues. Conclusions: The system’s ability to enable no-touch MSI at 1100 to 1700 nm was demonstrated. Preliminary data suggest that MTM warrants further exploration as a potential imaging tool for the detection of liver cancer during surgery.

Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient. Approach: A pretrained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient’s previously acquired imaging.

Purpose: Hyperspectral imaging (HSI) is a non-contact optical imaging technique with the potential to serve as an intraoperative computer-aided diagnostic tool. Our work analyzes the optical properties of visible structures in the surgical field for automatic tissue categorization. Approach: Building an HSI-based computer-aided tissue analysis system requires accurate ground truth and validation of optical soft tissue properties as these show large variability. We introduce and validate two different hyperspectral intraoperative imaging setups and their use for the analysis of optical tissue properties. First, we present an improved multispectral filter-wheel setup integrated into a fully digital microscope. Second, we present a novel setup of two hyperspectral snapshot cameras for intraoperative usage. Both setups are operating in the spectral range of 400 up to 975 nm. They are calibrated and validated using the same database and calibration set. Results: For validation, a color chart with 18 well-defined color spectra in the visual range is analyzed. Thus the results acquired with both settings become transferable and comparable to each other as well as between different interventions. On patient data of two different otorhinolaryngology procedures, we analyze the optical behaviors of different soft tissues and show a visualization of such different spectral information. Conclusion: The introduced calibration pipeline for different HSI setups allows comparison between all acquired spectral information. Clinical in vivo data underline the potential of HSI as an intraoperative diagnostic tool and the clinical usability of both introduced setups. Thereby, we demonstrate their feasibility for the in vivo analysis and categorization of different human soft tissues.

Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training. Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty. Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant. Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.

Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient.

Nationwide surveys on radiation dose to the population from medical imaging are recommended in order to follow trends in population exposure. The goal of the 2018 survey was to investigate the current exposure. The invoice coding information was collected in five university hospitals and large clinics. To improve the estimation of the effective dose delivered in computed tomography (CT), we collected dose data from different Dose Archiving Communication Systems. On average, we found that 1.2 radiological examinations per year and per inhabitant were performed. Dental radiography was the most frequent examination (48% of all the X-ray examinations), followed by conventional radiography (36%) and CT (11%). The average annual effective dose was estimated to be 1.48 mSv per inhabitant, with CT representing 64% of that dose. Our results show that the exposure of the Swiss population from medical imaging has remained stable since 2013, despite a 15% increase in the number of CT examinations.

The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.

Some of the ethically most sensitive issues in radiation protection arise at imaging of pregnant—and potentially pregnant—patients and of newborn. This article reviews the current literature and recommendations on imaging during pregnancy and breastfeeding. Risks related to alternative non-ionizing radiation methods are also considered. With few exceptions, exposure of the fetus through radiography, computed tomography (CT) and nuclear medicine imaging can be limited to safe levels, although studies such as abdominal-pelvic CT cannot avoid significant exposure to fetuses. Eight to 10 weeks post-conception, the fetus has a thyroid which starts to concentrate iodide having crossed the placenta barrier resulting in unacceptably high doses to the fetal thyroid after administration of 131I- and even 123I-iodide and other radiopharmaceuticals with a high content of free radioiodine. Many radiopharmaceuticals are excreted through breast milk. Breastfeeding interruption recommendations should be followed to keep the effective dose to the infant below 1 mSv.