Australasian Physical and Engineering Sciences in Medicine This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Improvising limitations of DNN based ultrasound image reconstruction
AbstractUltrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniqu...
Source: Australasian Physical and Engineering Sciences in Medicine - September 29, 2022 Category: Biomedical Engineering Source Type: research

Analysis of tumour dose –response data from animal experiments via two TCP models accounting for tumor hypoxia and resensitization
AbstractTo treat animal dose –response data exhibiting inverse dose–response behavior with two tumor control probability (TCP) models accounting for tumor hypoxia and re-oxygenation leading to resensitization of the tumor. One of the tested TCP models uses a modified linear-quadratic (LQ) model of cell survival where bothα andβ radiosensitivities increase in time during the treatment due to re-oxygenation of the hypoxic tumor sub-population. The other TCP model deals with two types of hypoxia —chronic and acute—and accounts for tumor re-sensitization via oxygenation of the chronically hypoxic and fluctuating oxyg...
Source: Australasian Physical and Engineering Sciences in Medicine - September 26, 2022 Category: Biomedical Engineering Source Type: research

Left ventricle analysis in echocardiographic images using transfer learning
AbstractThe segmentation of cardiac boundaries, specifically Left Ventricle (LV) segmentation in 2D echocardiographic images, is a critical step in LV segmentation and cardiac function assessment. These images are generally of poor quality and present low contrast, making daily clinical delineation difficult, time-consuming, and often inaccurate. Thus, it is necessary to design an intelligent automatic endocardium segmentation system. The present work aims to examine and assess the performance of some deep learning-based architectures such as U-Net1, U-Net2, LinkNet, Attention U-Net, and TransUNet using the public CAMUS (C...
Source: Australasian Physical and Engineering Sciences in Medicine - September 21, 2022 Category: Biomedical Engineering Source Type: research

An approach for the online determination of Entrance Surface Air Kerma (ESAK) from Kerma Area product (PKA) in digital radiology
AbstractWe propose determining the entrance surface air kerma (ESAK) from the kerma area product (PKA) in digital radiology. ESAK values were derived from the X-ray tube outputs and patient exposure factors across five X-ray departments. Using linear regression between ESAK and PKA values, the slope and intercept coefficients were determined for each X-ray equipment and procedure. The method was examined using the data collected from patients who underwent chest PA/LAT, abdomen, pelvic AP, and lumbar spine AP/LAT X-ray examinations. The results showed a highly significant correlation between ESAK and PKA values and correla...
Source: Australasian Physical and Engineering Sciences in Medicine - September 15, 2022 Category: Biomedical Engineering Source Type: research

Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation
This study proposed an enhanced lightweight U-Net model with an Attention Gate (AG) to segment OD images. We also used a transfer learning strategy to extract relevant features using a pre-trained EfficientNet-B0 CNN, which preserved the receptive field size and AG, which reduced the impact of gradient vanishing and overfitting. Additionally, the neural network trained using the binary focal loss function improved segmentation accuracy. The pre-trained Attention U-Net was validated using publicly available datasets, such as DRIONS-DB, DRISHTI-GS, and MESSIDOR. The model significantly reduced parameter quantity by around 0....
Source: Australasian Physical and Engineering Sciences in Medicine - September 12, 2022 Category: Biomedical Engineering Source Type: research

Transit dosimetry of stereotactic body radiotherapy treatments with electronic portal dosimetry device in patient with spinal implant
In this study, we aimed to investigate the dose distributions of SBRT treatment plans in patients with spinal implants by transit EPID dosimetry. Implants were inserted in phantoms that mimic the vertebrae, and VMAT plans were created on the phantoms to deliver 16  Gy radiation doses to the target in 1 fraction. Transit EPID measurements were performed for each irradiation. The results were compared with the treatment planning system using the gamma analysis method. According to the gamma analysis results, while the non-implant model met the acceptance crite ria with a rate of 95.4%, the implanted models did not pass the ...
Source: Australasian Physical and Engineering Sciences in Medicine - September 8, 2022 Category: Biomedical Engineering Source Type: research

The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer
AbstractTo establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve (AUC) was used for performance evaluation in this study. The radiomics model got AUCs of 0.755 (95% CI 0.650 –0.859), 0.71 (...
Source: Australasian Physical and Engineering Sciences in Medicine - September 5, 2022 Category: Biomedical Engineering Source Type: research

Multi-modality MRI for Alzheimer ’s disease detection using deep learning
AbstractDiffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer ’s disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain’s gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structura...
Source: Australasian Physical and Engineering Sciences in Medicine - September 5, 2022 Category: Biomedical Engineering Source Type: research

A comparison of in-house and shared RapidPlan models for prostate radiation therapy planning
In this study, we assess the success of a using a publicly available KBP model compared with developing an in-house model for prostate cancer radiotherapy using a single, commercially available treatment planning system based on the ability of the model to achieve the centre ’s planning goals. Two radiation oncology centres each created a prostate cancer KBP model using the Eclipse RapidPlan software. These two models and a third publicly-available, shared model were tested at three centres in a retrospective planning study. The publicly-available model achieved lower rectum doses than the other two models. However, the ...
Source: Australasian Physical and Engineering Sciences in Medicine - September 5, 2022 Category: Biomedical Engineering Source Type: research

Effects of illuminance intensity on the green channel of remote photoplethysmography (rPPG) signals
This study presents a quantitative empirical analysis where the quality and morphology of rPPG signals were assessed under different light settings. Participants ’ faces were exposed to the white LED spotlight, first when the sources were installed directly behind the video camera, and then when the sources were installed in a cross-polarized scheme. Hence, the effect of specular reflectance on rPPG signals could be observed in an increasing projection. Th e signal qualities were analyzed in each intensity level using a signal-to-noise (SNR) ratio metric. In 3 of 7 participants, placing the video camera on the same level...
Source: Australasian Physical and Engineering Sciences in Medicine - August 29, 2022 Category: Biomedical Engineering Source Type: research

Varian eclipse stereotactic 5  mm cone data commissioning
AbstractConical collimators are effective and readily available accessories for the field shaping of small stereotactic fields, however the measurements required to accurately characterise the smallest radiation fields are difficult, prone to large errors, and there is little published commissioning data to compare measurements against. The aim of this investigation was to commission the cone dose calculation algorithm of a Varian Eclipse treatment planning system for a Varian 5  mm cone attached to a Varian TrueBeam linear accelerator beam-matched to the Varian Golden Beam Data (GBD). Tissue maximum ratios (TMRs) and off...
Source: Australasian Physical and Engineering Sciences in Medicine - August 23, 2022 Category: Biomedical Engineering Source Type: research

Ensemble of deep capsule neural networks: an application to pediatric pneumonia prediction
AbstractPneumonia disease accounts for 15% of all deaths in children under the age of five and early detection of the disease significantly improves survival chances. In this work, we introduce a novel deep neural network model for evaluating pediatric pneumonia from chest radio-graph images. The proposed network is an ensemble of multiple candidate networks, each with interleaved convolutional and capsule layers. Individual networks are stitched together with dense layers and trained as a single model to minimize joint loss. The proposed approach is validated through extensive experimentation on the benchmark pneumonia da...
Source: Australasian Physical and Engineering Sciences in Medicine - August 23, 2022 Category: Biomedical Engineering Source Type: research

Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net
This study has found that, DWI image-weights have a higher diagnostic value for glioma segmentation with U-Net in comparison with anatomical image-weights and post enhancement image-weights. The results of this investigation show that ADC and eADC maps have higher performance for glioma segmentation with U-Net. (Source: Australasian Physical and Engineering Sciences in Medicine)
Source: Australasian Physical and Engineering Sciences in Medicine - August 23, 2022 Category: Biomedical Engineering Source Type: research

Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning
AbstractBrain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed wit...
Source: Australasian Physical and Engineering Sciences in Medicine - August 23, 2022 Category: Biomedical Engineering Source Type: research

Performance evaluation of an LED flatbed scanner for triple channel film dosimetry with EBT3 and EBT-XD film
AbstractWe investigate the properties of a light emitting diode (LED) flatbed scanner for use with EBT3 and EBT-XD film types in a clinical radiochromic film (RCF) dosimetry program with modern treatment techniques. The flatbed scanner was characterised in terms of lateral and longitudinal response, X –Y scaling integrity, scanning reproducibility, scanner warm up dependence and film orientation dependence. The preferred lateral response artefact (LRA) corrections are investigated for the LED light source. Supporting evidence is provided regarding the dose independent nature of the corrections while also providing result...
Source: Australasian Physical and Engineering Sciences in Medicine - August 23, 2022 Category: Biomedical Engineering Source Type: research