The phantom was then filled with NiSO4solution and scanned on a 3T PET-MR Siemens scanner for selected T1 and T2 sequences, from which distortion vectors were generated and analysed using in-house software written in Python.Results All deviations of the node positions from the print design were less than 1 mm, with an average displacement of 0.228 mm. The majority of the deviations were smaller than the 0.692 mm pixel size for this dataset.Conclusion A customised 3D printed MRI-phantom was successfully printed and tested for assessing geometric distortion on MRI scanners. 3D printed phantoms can be considered for clinics wishing to assess geometric distortions under specific conditions, but require resources for design, fabrication, commissioning, and verification.Purpose. To develop an automated optimization strategy to facilitate collimator design for small-field radiotherapy systems.Methods and Materials.We developed an objective function that links the dose profile characteristics (FWHM, penumbra, and central dose rate) and the treatment head geometric parameters (collimator thickness/radii, source-to-distal-collimator distance (SDC)) for small-field radiotherapy systems. We performed optimization using a downhill simplex algorithm. this website We applied this optimization strategy to a linac-based radiosurgery system to determine the optimal geometry of four pencil-beam collimators to produce 5, 10, 15, and 20 mm diameter photon beams (from a 6.7 MeV, 2.1 mm FWHM electron beam). Two different optimizations were performed to prioritize minimum penumbra or maximum central dose rate for each beam size. We compared the optimized geometric parameters and dose distributions to an existing clinical system (CyberKnife).Results.When minimum penumbra was prioritized, using the same colsharp penumbra. This represents proof-of-principle that an automated optimization strategy may apply to more complex collimator designs with multiple optimization parameters.Purpose. Radiation dose delivered to targets located near the upper-abdomen or in the thorax are significantly affected by respiratory-motion. Relatively large-margins are commonly added to compensate for this motion, limiting radiation-dose-escalation. Internal-surrogates of target motion, such as a radiofrequency (RF) tracking system, i.e. Calypso®System, are used to overcome this challenge and improve normal-tissue sparing. RF tracking systems consist of implanting transponders in the vicinity of the tumor to be tracked using radiofrequency-waves. Unfortunately, although the manufacture provides a universal quality-assurance (QA) phantom, QA-phantoms specifically for lung-applications are limited, warranting the development of alternative solutions to fulfil the tests mandated by AAPM's TG142. Accordingly, our objective was to design and develop a motion-phantom to evaluate Calypso for lung-applications that allows the Calypso®Beacons to move in different directions to better simulate truelung-motion.Methoold.Conclusions.The robotic QA-phantom designed and developed in this study provides an independent phantom for performing Calypso lung-QA for commissioning and acceptance testing of Calypso for lung treatments.X-ray scatter remains a major physics challenge in volumetric computed tomography (CT), whose physical and statistical behaviors have been commonly leveraged in order to eliminate its impact on CT image quality. In this work, we conduct an in-depth derivation of how the scatter distribution and scatter to primary ratio (SPR) will change during the spectral correction, leading to an interesting finding on the property of scatter. Such a characterization of scatter's behavior provides an analytic approach of compensating for the SPR as well as approximating the change of scatter distribution after spectral correction, even though both of them might be significantly distorted as the linearization mapping function in spectral correction could vary a lot from one detector pixel to another. We conduct an evaluation of SPR compensations (SPRCs) on a Catphan phantom and an anthropomorphic chest phantom to validate the characteristics of scatter. In addition, this scatter property is also directly adopted into CT imaging using a spectral modulator with flying focal spot technology (SMFFS) as an example to demonstrate its potential in practical applications. For cone-beam CT (CBCT) scans at both 80 and 120 kVp, CT images with accurate CT numbers can be achieved after spectral correction followed by the appropriate SPRC based on our presented scatter property. In the case of the SMFFS based CBCT scan of the Catphan phantom at 120 kVp, after a scatter correction using an analytic algorithm derived from the scatter property, CT image quality was significantly improved, with the averaged root mean square error reduced from 297.9 to 6.5 Hounsfield units.Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C5H12, N2O, NO2, C2H4, CO, CO2) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.