Physical and Engineering Sciences in Medicine最新文献

This study aims to evaluate the output factors (OPF) of different radiation therapy planning systems (TPSs) using a plastic scintillator detector (PSD). The validation results for determining a practical field size for clinical use were verified. The implemented validation system was an Exradin W2 PSD. The focus was to validate the OPFs of the small irradiation fields of two modeled radiation TPSs using RayStation version 10.0.1 and Monaco version 5.51.10. The linear accelerator used for irradiation was a TrueBeam with three energies: 4, 6, and 10 MV. RayStation calculations showed that when the irradiation field size was reduced from 10 × 10 to 0.5 × 0.5 cm², the results were within 2.0% of the measured values for all energies. Similarly, the values calculated using Monaco were within approximately 2.0% of the measured values for irradiation field sizes between 10 × 10 and 1.5 × 1.5 cm² for all beam energies of interest. Thus, PSDs are effective validation tools for OPF calculations in TPS. A TPS modeled with the same source data has different minimum irradiation field sizes that can be calculated. These findings could aid in verification of equipment accuracy for treatment planning requiring highly accurate dose calculations and for third-party evaluation of OPF calculations for TPS.

Motion management has become an integral part of radiation therapy. Multiple approaches to motion management have been reported in the literature. To allow the sharing of experiences on current practice and emerging technology, the University of Sydney and the New South Wales/Australian Capital Territory branch of the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) held a two-day motion management workshop. To inform the workshop program, participants were invited to complete a survey prior to the workshop on current use of motion management techniques and their opinion on the effectiveness of each approach. A post-workshop survey was also conducted, designed to capture changes in opinion as a result of workshop participation. The online workshop was the most well attended ever hosted by the ACPSEM, with over 300 participants and a response to the pre-workshop survey was received from at least 60% of the radiation therapy centres in Australia and New Zealand. Motion management is extensively used in the region with use of deep inspiration breath-hold (DIBH) reported by 98% of centres for left-sided breast treatments and 91% for at least some right-sided breast treatments. Surface guided radiation therapy (SGRT) was the most popular session at the workshop and survey results showed that the use of SGRT is likely to increase. The workshop provided an excellent opportunity for the exchange of knowledge and experience, with most survey respondents indicating that their participation would lead to improvements in the quality of delivery of treatments at their centres.

Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = - 0.44 and - 0.56 to - 0.29 for the vascular angle, and r = - 0.19 and - 0.34 to - 0.02 for the diameter between the subclavian artery and the aneurysm (Fig. 3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images.

This article documents the work conducted in implementing the IAEA non-agreement TC regional RAS6088 project "Strengthening Education and Training Programmes for Medical Physics". Necessary information on the project was collected from the project counterparts via emails for a period of one month, starting from 21st September 2023, and verified at the Final Regional Coordination Meeting in Bangkok, Thailand from 30th October 2023 to 3rd November 2023. Sixty-three participants were trained in 5 Regional Training Courses (RTCs), with 48%, 32% and 20% in radiation therapy, diagnostic radiology, and nuclear medicine, respectively. One RTC was successfully organised to introduce molecular biology as an academic module to participants. Three participating Member States, namely United Arab Emirates (UAE), Nepal and Afghanistan have initiated processes to start the postgraduate master medical physics education programmes by coursework, adopting the IAEA TCS56 Guidelines. UAE has succeeded in completing the process while Nepal and Afghanistan have yet to initiate the programme. The postgraduate master medical physics programmes by coursework were strengthened in Indonesia, Jordan, Malaysia, Pakistan, Syria, and Thailand, along with the national registration of medical physicists. In particular, Thailand has revised 6 postgraduate master medical physics programmes by coursework during the tenure of this project. Home Based Assignment and RTCs have resulted in two publications. In conclusion, the RAS6088 project was found to have achieved its planned outcomes despite challenges faced due to the COVID-19 pandemic. It is proposed that a follow up project be implemented to increase the number of Member States who are better prepared to improve medical physics education and training in the region.

This paper aims to present a model called SPINDILOMETER, which we propose to be integrated into polysomnography (PSG) devices for researchers focused on electrophysiological signals in PSG, physicians, and technicians practicing sleep in clinics, by examining the methods of the sleep electroencephalogram (EEG) signal analysis in recent years. For this purpose, an assist diagnostic model for PSG has been developed that measures the number and density of sleep spindles by analyzing EEG signals in PSG. EEG signals of 72 volunteers, 51 males and 21 females (age; 51.7 ± 3.42 years and body mass index; 37.6 ± 4.21) diagnosed with sleep-disordered breathing by PSG were analyzed by machine learning methods. The number and density of sleep spindles were compared between the classical method (EEG monitoring with the naked eye in PSG) ('method with naked eye') and the model (SPINDILOMETER). A strong positive correlation was found between 'method with naked eye' and SPINDILOMETER results (correlation coefficient: 0.987), and this correlation was statistically significant (p = 0.000). Confusion matrix (accuracy (94.61%), sensitivity (94.61%), specificity (96.60%)), and ROC analysis (AUC: 0.95) were performed to prove the adequacy of SPINDILOMETER (p = 0.000). In conclusion SPINDILOMETER can be included in PSG analysis performed in sleep laboratories. At the same time, this model provides diagnostic convenience to the physician in understanding the neurological events associated with sleep spindles and sheds light on research for thalamocortical regions in the fields of neurophysiology and electrophysiology.

Stereotactic body radiation therapy (SBRT) has been increasingly used for the ablation of liver tumours. CyberKnife and proton beam therapy (PBT) are two advanced treatment technologies suitable to deliver SBRT with high dose conformity and steep dose gradients. However, there is very limited data comparing the dosimetric characteristics of CyberKnife to PBT for liver SBRT. PBT and CyberKnife plans were retrospectively generated using 4DCT datasets of ten patients who were previously treated for hepatocellular carcinoma (HCC, N = 5) and liver metastasis (N = 5). Dose volume histogram data was assessed and compared against selected criteria; given a dose prescription of 54 Gy in 3 fractions for liver metastases and 45 Gy in 3 fractions for HCC, with previously published consensus-based normal tissue dose constraints. Comparison of evaluation parameters showed a statistically significant difference for target volume coverage and liver, lungs and spinal cord (p < 0.05) dose, while chest wall and skin did not indicate a significant difference between the two modalities. A number of optimal normal tissue constraints was violated by both the CyberKnife and proton plans for the same patients due to proximity of tumour to chest wall. PBT resulted in greater organ sparing, the extent of which was mainly dependent on tumour location. Tumours located on the liver periphery experienced the largest increase in organ sparing. Organ sparing for CyberKnife was comparable with PBT for small target volumes.