Accuracy of SRS dose delivery using the TomoTherapy Hi-Art System

Purpose: To quantify the accuracy and precision of both target positioning and dose delivery for intracranial radiosurgery delivered with the TomoTherapy Hi-Art System using a non-invasive immobilization device. Methods: Techniques developed by Vinci et al (2007) were refined for the measurement of dose distributions in each principal plane using a CIRS head phantom. Pieces of Gafchromic EBT2 film were cut and digitized using a template developed by Vinci et al (2007). A plan was created for a 2 cm diameter x 2 cm long cylindrical target in the TomoTherapy treatment planning system (TPS) version 3.2.1. Intentional misalignments of 5 mm in each of the principal directions were applied to the phantom prior to treatment delivery. The MVCT feature of the TomoTherapy Hi-Art system was used to correct for these misalignments, and then the treatment was delivered. Measured dose distributions (film) were registered to the calculated dose distributions (TPS planar dose) and compared. Results: Alignment errors (displacement between the midpoints of the measured and calculated 70% dose points; mean ± standard deviation) were -0.15 ± 0.47 mm (range: -1.97 to 0.8 mm), -0.36 ± 0.56 mm (range: -1.25 to 0.63 mm), and -0.67 ± 0.93 mm (range: -3.04 to 0.90 mm) in the superior-inferior, anterior-posterior, and lateral directions, respectively. Positional errors of the 80% dose points in millimeters were 1.28 ± 0.91 (range: -0.09 to 3.62), -0.02 ± 0.96 (range: -2.24 to 1.72), -0.04 ± 0.62 (range: -1.24 to 1.25), 0.64 ± 0.52 (range: -0.35 to 1.55), 0.30 ± 0.52 (range: -1.57 to 1.28), and 0.60 ± 0.46 (range: -0.26 to 2.39) for the right, left, posterior, anterior, inferior, and superior directions, respectively. Conclusions: Using a non-invasive immobilization device, 1.98 mm dose voxel size, and manual lateral couch positioning, the spatial accuracy of dose delivery with the TomoTherapy Hi•Art System was not within 1 mm as hypothesized

Methodological approaches for studying the microbial ecology of drinking water distribution systems

The study of the microbial ecology of drinking water distribution systems (DWDS) has traditionally been based on culturing organisms from bulk water samples. The development and application of molecular methods has supplied new tools for examining the microbial diversity and activity of environmental samples, yielding new insights into the microbial community and its diversity within these engineered ecosystems. In this review, the currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated. The study of biofilms is considered particularly important as it plays a critical role in the processes and interactions occurring at the pipe wall and bulk water interface. The advantages, limitations and usefulness of methods that can be used to detect and assess microbial abundance, community composition and function are discussed in a DWDS context. This review will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects

Coronavirus Disease (COVID-19): The Value of Chest Radiography for Patients Greater Than Age 50 Years at an Earlier Timepoint of Symptoms Compared With Younger Patients

Background: A relative paucity of data exists regarding chest radiography (CXR) in diagnosis of coronavirus disease (COVID-19) compared to computed tomography. We address the use of a strict pattern of CXR findings for COVID-19 diagnosis, specifically during early onset of symptoms with respect to patient age. Methods: We performed a retrospective study of patients under investigation for COVID-19 who presented to the emergency department during the COVID-19 outbreak of 2020 and had CXR within 1 week of symptoms. Only reverse transcription polymerase chain reaction (RT-PCR)-positive patients were included. Two board-certified radiologists, blinded to RT-PCR results, assessed 60 CXRs in consensus and assigned 1 of 3 patterns: characteristic, atypical, or negative. Atypical patterns were subdivided into more suspicious or less suspicious for COVID-19. Results: Sixty patients were included: 30 patients aged 52 to 88 years and 30 patients aged 19 to 48 years. Ninety-three percent of the older group demonstrated an abnormal CXR and were more likely to have characteristic and atypical-more suspicious findings in the first week after symptom onset than the younger group. The relationship between age and CXR findings was statistically significant (chi(2) [2, n=60]=15.70; P=0.00039). The relationship between negative and characteristic COVID-19 CXR findings between the 2 age cohorts was statistically significant with Fisher exact test resulting in a P value of 0.001. Conclusion:COVID-19 positive patients \u3e50 years show earlier, characteristic patterns of statistically significant CXR changes than younger patients, suggesting that CXR is useful in the early diagnosis of infection. CXR can be useful in early diagnosis of COVID-19 in patients older than 50 years

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19 : A Geometric Transformation Perspective

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χ 2 McNemar′s statistic = 163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.Applied Science, Faculty ofMedicine, Faculty ofNon UBCElectrical and Computer Engineering, Department ofRadiology, Department ofReviewedFacultyResearche