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Magnetic resonance imaging-only radiotherapy treatment planning (MRI-only RTP) and positron emission tomography (PET)-MRI imaging require generation of synthetic computed tomography (sCT) images from MRI images. In this study, initial dosimetric evaluation was performed for a previously developed MRI-based attenuation correction (MRAC) method for use in MRI-only RTP of the brain. MRAC-based sCT images were retrospectively generated from Dixon MR images of 20 patients who had previously received external beam radiation therapy (EBRT). Bone segmentation performance and Dice similarity coefficient of the sCT conversion method were evaluated for bone volumes on CT images. Dose calculation accuracy was assessed by recalculating the CT-based EBRT plans using the sCT images as the base attenuation data. Dose comparison was done for the sCT- and CT-based EBRT plans in planning target volume (PTV) and organs at risk (OAR). Parametric dose comparison showed mean relative differences of

Using magnetic resonance imaging (MRI) as the only imaging method for radiotherapy treatment planning (RTP) is becoming more common as MRI-only RTP solutions have evolved. The geometric accuracy of MR images is an essential factor of image quality when determining the suitability of MRI for RTP. The need is therefore clear for clinically feasible quality assurance (QA) methods for the geometric accuracy measurement.This work evaluates long-term stability of geometric accuracy and the validity of a 2D geometric accuracy QA method compared to a prototype 3D method and analysis software in routine QA. The long-term follow-up measurements were conducted on one of the 1.5 T scanners over a period of 19 months using both methods. Inter-scanner variability of geometric distortions was also evaluated in three 1.5 T and three 3 T MRI scanners from a single vendor by using the prototype 3D QA method.The geometric accuracy of the magnetic resonance for radiotherapy (MR-RT) platform remained stable within 2 mm at distances of250 mm from isocenter. All scanners achieved good geometric accuracy with mean geometric distortions of1 mm at150 mm and2 mm at250 mm from the isocenter. Both measurement methods provided relevant information about geometric distortions.Geometric distortions are often considered a limitation of MRI-only RTP. Results indicate that geometric accuracy of modern scanners remain within acceptable limits by default even after many years of clinical use based on the 3D QA evaluation.

The paper presents a novel method to measure 3-D deformation of a large metallic frame structure of a crane under loading from one to several images, when the cameras need to be attached to the self deforming body, the structure sways during loading, and the imaging geometry is not optimal due to physical limitations. The solution is based on modeling the deformation with adequate shape functions and taking into account that the cameras move depending on the frame deformation. It is shown that the deformation can be estimated even from a single image of targeted points if the 3-D coordinates of the points are known or have been measured before loading using multiple cameras or some other measuring technique. The precision of the method is evaluated to be 1 mm at best, corresponding to 1:11400 of the average distance to the target.

Purpose For monitoring the performance of Magnetic Resonance Imaging (MRI) systems, it is essential to implement regular quality control (QC) protocols. We evaluated the MRI system performance of the Philips Ingenuity TF PET/MR, using the American college of radiology (ACR) QC protocol [1] and automated QC software. Methods A total of 80 QC scans were performed during years 2013 to 2018 using the ACR QC protocol. In the initial analysis, 21 scans were analyzed by a human observer and automatic software implemented in MATLAB2015b [2] , which performs the analysis according to ACR guidelines [1] . Five quantitative tests were included: geometric accuracy (148 ± 2 and 190 ± 2 mm), slice thickness accuracy (5.0 ± 0.7 mm), slice position accuracy ( 82 %) and percent signal ghosting ( Results For geometric accuracy, (M = 147 ± 1), (A = 148 ± 1) and (M = 190 ± 0.5), (A = 191 ± 0.2) were measured for the localizer and T1 series. For slice thickness accuracy, (M = 5.5 ± 0.5), (A = 4.7 ± 0.8) and (M = 5.9 ± 0.5), (A = 4.5 ± 1.4) were measured for the T1 and T2 series. For slice position accuracy, (M = 5.6 ± 3.3), (A = 4.2 ± 2.7) and (M = 5.3 ± 2.8), (A = 4.0 ± 2.5) were measured for the T1 and T2 series. For image intensity uniformity, (M = 85 ± 1), (A = 96 ± 1) and (M = 89 ± 2), (A = 97 ± 2) were measured for the T1 and T2 series. For percent signal ghosting, (M = 0.0010 ± 0.0009) and (M = 0.0015 ± 0.0007) were measured. The time to analyze and report was two minutes with the automatic software. Conclusions Majority of the tests reached the acceptance criteria in the initial analysis. Slice thickness and slice position accuracy were the most common tests to fail. Automated software was beneficial for easing the analysis workflow. The QC software with a test dataset will be provided during the conference.