Physical and Engineering Sciences in Medicine最新文献

In lung CT imaging, motion artifacts caused by cardiac motion and respiration are common. Recently, CLEAR Motion, a deep learning-based reconstruction method that applies motion correction technology, has been developed. This study aims to quantitatively evaluate the clinical usefulness of CLEAR Motion. A total of 129 lung CT was analyzed, and heart rate, height, weight, and BMI of all patients were obtained from medical records. Images with and without CLEAR Motion were reconstructed, and quantitative evaluation was performed using variance of Laplacian (VL) and PSNR. The difference in VL (DVL) between the two reconstruction methods was used to evaluate which part of the lung field (upper, middle, or lower) CLEAR Motion is effective. To evaluate the effect of motion correction based on patient characteristics, the correlation between body mass index (BMI), heart rate and DVL was determined. Visual assessment of motion artifacts was performed using paired comparisons by 9 radiological technologists. With the exception of one case, VL was higher in CLEAR Motion. Almost all the cases (110 cases) showed large DVL in the lower part. BMI showed a positive correlation with DVL (r = 0.55, p < 0.05), while no differences in DVL were observed based on heart rate. The average PSNR was 35.8 ± 0.92 dB. Visual assessments indicated that CLEAR Motion was preferred in most cases, with an average preference score of 0.96 (p < 0.05). Using Clear Motion allows for obtaining images with fewer motion artifacts in lung CT.

Immediate breast Reconstruction is increasing in use in Australia and accounts for almost 10% of breast cancer patients (Roder in Breast 22:1220-1225, 2013). Many treatments include a bolus to increase dose to the skin surface. Air gaps under bolus increase uncertainty in dosimetry and many bolus types are unable to conform to the shape of the breast or are not flexible throughout treatment if there is a swelling induced contour change. This study investigates the use of two bolus types that can be manufactured in house-wet combine and ThermoBolus. Wet combine is a material composed of several water soaked dressings. ThermoBolus is a product developed in-house that consists of thermoplastic encased in silicone. Plans using a volumetric arc therapy technique were created for each bolus and dosimetry performed with thermoluminescent detectors (TLDs) and EBT-3 film over three fractions. Wax was used to simulate swelling and allow analysis of the flexibility of the bolus materials. ThermoBolus had a range of agreement with calculation from -2 to 4% for film measurement and -5.6 to 1.0% for TLDs. Wet combine had a range of agreement with calculation from 1.6 to 10.5% for film measurement and -13.5 to 13.1% for TLDs. It showed consistent conformity and flexibility for all fractions and with induced contour but air gaps of 2-3 mm were observed between layers of the material. ThermoBolus and wet combine are able to conform to contour change without the introduction of large air gaps between the patient surface and bolus. ThermoBolus is reusable and can be remoulded if the patient undergoes significant contour change during the course of treatment. It is able to be modelled accurately by the treatment planning system. Wet combine shows inconsistency in manufacture and requires more than one bolus to be made over the course of treatment, reducing accuracy in modelling and dosimetry.

Artificial Intelligence has shown great promise in healthcare, particularly in non-invasive diagnostics using bio signals. This study focuses on classifying eye states (open or closed) using Electroencephalogram (EEG) signals captured via a 14-electrode neuroheadset, recorded through a Brain-Computer Interface (BCI). A publicly available dataset comprising 14,980 instances was used, where each sample represents EEG signals corresponding to eye activity. Fourteen classical machine learning (ML) models were evaluated using a tenfold cross-validation approach. The preprocessing pipeline involved removing outliers using the Z-score method, addressing class imbalance with SMOTETomek, and applying a bandpass filter to reduce signal noise. Significant EEG features were selected using a two-sample independent t-test (p < 0.05), ensuring only statistically relevant electrodes were retained. Additionally, the Common Spatial Pattern (CSP) method was used for feature extraction to enhance class separability by maximizing variance differences between eye states. Experimental results demonstrate that several classifiers achieved strong performance, with accuracy above 90%. The k-Nearest Neighbours classifier yielded the highest accuracy of 97.92% with CSP, and 97.75% without CSP. The application of CSP also enhanced the performance of Multi-Layer Perceptron and Support Vector Machine, reaching accuracies of 95.30% and 93.93%, respectively. The results affirm that integrating statistical validation, signal processing, and ML techniques can enable accurate and efficient EEG-based eye state classification, with practical implications for real-time BCI systems and offering a lightweight solution for real-time healthcare wearable applications healthcare applications.

Blood pressure is an essential indicator of cardiovascular health in the human body, and regular and accurate blood pressure measurement is essential for preventing cardiovascular diseases. The emergence of photoplethysmography (PPG) and the advancement of machine learning offers new opportunities for noninvasive blood pressure measurement. This paper proposes a non-contact method for measuring blood pressure using face video and machine learning. This method extracts facial remote photoplethysmography (RPPG) signals from face video captured by a camera, and enhances the signal quality of RPPG through a set of filtering processes. The blood pressure regression model is constructed using the extreme gradient boosting tree (XGBoost) method to estimate blood pressure from RPPG signals. This approach achieved accurate blood pressure measurement, with a measurement error of 4.8893 ± 6.6237 mmHg for systolic pressure and 4.0805 ± 5.5821 mmHg for diastolic pressure. Experimental results show that this method fully complies with the American Medical Instrumentation Association (AAMI).Our proposed method has minor errors in predicting the systolic and diastolic blood pressures and achieves grade A evaluation for both systolic and diastolic blood pressures according to the British Hypertension Society (BHS) standards.

Segmenting ischemic stroke lesions from Non-Contrast CT (NCCT) scans is a complex task due to the hypo-intense nature of these lesions compared to surrounding healthy brain tissue and their iso-intensity with lateral ventricles in many cases. Identifying early acute ischemic stroke lesions in NCCT remains particularly challenging. Computer-assisted detection and segmentation can serve as valuable tools to support clinicians in stroke diagnosis. This paper introduces CoAt U SegNet, a novel deep learning model designed to detect and segment acute ischemic stroke lesions from NCCT scans. Unlike conventional 3D segmentation models, this study presents an advanced 3D deep learning approach to enhance delineation accuracy. Traditional machine learning models have struggled to achieve satisfactory segmentation performance, highlighting the need for more sophisticated techniques. For model training, 50 NCCT scans were used, with 10 scans for validation and 500 scans for testing. The encoder convolution blocks incorporated dilation rates of 1, 3, and 5 to capture multi-scale features effectively. Performance evaluation on 500 unseen NCCT scans yielded a Dice similarity score of 75% and a Jaccard index of 70%, demonstrating notable improvement in segmentation accuracy. An enhanced similarity index was employed to refine lesion segmentation, which can further aid in distinguishing the penumbra from the core infarct area, contributing to improved clinical decision-making.

This study assesses the updated Monaco TPS virtual source model (VSM) 2.0, which removes multileaf collimator (MLC) and jaw characterization as editable factors from the MLC geometry section within Monaco. The focus is on the impact of changes to stereotactic radiotherapy (SRT) cases for spinal and intracranial treatments for two beam matched linear accelerators. A validated custom VSM 1.6 model optimized for SRT was compared with the Elekta Accelerated Go Live 6 MV flattening filter-free (FFF) and VSM 2.0. Evaluations included measured MLC characteristics with a high-resolution detector, measured output factors (OPF), ion chamber fields in the thorax phantom, and recalculations of clinically relevant SRT cases. VSM 2.0 improves MLC modelling. Ion chamber measurements for IAEA TD1583 measurements were found to be within expected tolerances. Gamma pass rates for two matched LINACs evidenced improvement at 1%, 1 mm and 10% threshold for single and multi-SRS brain and SABR Spine treatments. VSM 2.0 represents a meaningful advancement in beam modelling within a Monte Carlo-based TPS environment, offering improved dosimetric performance and operational simplicity. Commercially available detectors were used to demonstrate that VSM 2.0 enhances agility MLC modelling, supporting more precise SRT and SABR delivery for matched LINACs. Removing configurable dependencies from the beam model will result in more consistent high quality beam models, an improves workflows for commissioning of the Monaco TPS.

Zirconium-89 is presently undergoing pre-clinical investigation for its potential application as a positron emission tomography (PET) theranostic radioisotope. A critical consideration in the increasing number of trials and eventual clinical implementations is a comprehensive understanding of the radioactive waste byproducts and their quantification. This study focuses on the investigation and characterisation of the waste isotopes generated during the production of Zirconium-89, employing a combination of Geant4 Monte Carlo simulation and experimental methodologies utilising commercially obtainable starting materials from Thermofisher. Post cyclotron production samples of waste were taken and measured using a high purity germanium detector. Subsequent spectrum analysis consistently revealed the presence of the following isotopes in units of kBq per GBq of Zirconium-89 produced: cobalt-56 (13 ± 2, 14 ± 2, 15 ± 1), cobalt-57 (0.087 ± 0.004, 0.097 ± 0.004, 0.086 ± 0.007), rhenium-183 (2.61 ± 0.06, 3.29 ± 0.06, 2.47 ± 0.09), scandium-48 (27 ± 0.9, 21.1 ± 0.4), yttrium-88 (0.67 ± 0.06, 1.1 ± 0.4, 0.73 ± 0.06) and zirconium-88 (90 ± 5, 1340 ± 60, 35 ± 2). All the waste isotopes were able to reasonably be estimated using Geant4 Monte Carlo simulations or the deviation was able to be justified. The repeatability and predictability of isotopes and activities will enable informed decision-making regarding storage and disposal procedures in accordance with local legislative requirements.

Targeted alpha therapy (TαT) employs alpha particle-emitting radioisotopes conjugated to tumour-specific carriers to precisely irradiate tumour cells. Monte-carlo techniques have been used to accurately simulate absorbed dose and DNA damage for the four promising TαT radionuclides, Actinium-225 (²²⁵Ac), Radium-223, (²²³Ra), Lead-212 (²¹²Pb) and Astatine-211, (²¹¹At). TOPAS and TOPAS-nBio, based on the Geant4 and Geant4-DNA monte-carlo codes respectively, were used to model the radioactive decay and alpha particle transport within a simplified spherical cell model. Four different sites within the cell model were used for the initial radionuclide distributions: the cell membrane layer, within the cytoplasm volume, on the nucleus surface, and within the nucleus volume. Results indicate higher absorbed doses to the nucleus per decay when radionuclides are initially located on the nucleus wall or within the nucleus volume. ²²⁵Ac and ²²³Ra, with longer decay chains and higher alpha yields, exhibit higher doses to the nucleus per decay compared to ²¹²Pb and ²¹¹At. Notably, ²¹¹At, particularly when initially distributed within the nucleus volume or at its surface, demonstrates high relative efficacy, indicated by the absorbed dose to the nucleus per decay and number of single and double-strand breaks. These findings suggest that tumour-specific molecules should ideally target the nucleus to optimize efficacy.

The ESD is calculated using the backscatter factor (BSF). However, BSFs for flat surfaces have been used even though simulations have shown that the BSFs for curved surfaces, which represent the human body more accurately, do not match those for flat surfaces. Measuring these values in practice presents a challenge because conventional dosimeters used for BSF measurement have sensitive volumes that cannot conform to curved surfaces. In this study, we measured the BSF for a curved surface using a flexible scintillator. The scintillator, composed of Gd₃Al₂Ga₃O₁₂ (GAGG) scintillator powder mixed with a silicone adhesive, was securely attached to the curved surface of a cylindrical phantom. Diagnostic X-rays were irradiated onto the scintillator, and the BSFs were evaluated as the ratio of the light output with and without the phantom. We successfully measured BSFs on a curved surface using a flexible scintillator. The mean difference between the BSFs obtained from the experiments using the flexible scintillator and those obtained from the simulations for the cylindrical phantom was 0.43%. The maximum difference was 1.47%, which was observed at a tube voltage of 40 kV. Thus, the BSFs measured using the flexible scintillator agree well with the simulated results. Our scintillator is useful for measuring BSFs on curved surfaces and contributes to dose management.

An understanding of the repeatability of imaging biomarkers is key to their implementation as clinical tools. In this study we calculate the repeatability and inter-correlation of radiomic features derived from quantitative MRI (qMRI) of Glioblastoma (GBM) patients and assess the effect of image standardisation methods on these factors. We analysed scan-rescan Diffusion Weighted MR Images (DWI) and Dynamic Contrast Enhanced MR Images (DCE) from 36 GBM patients obtained from The Cancer Imaging Archive (TCIA). These included 17 patients, from the QIN-GBM-Treatment-Response patient cohort, scanned post surgery and prior to chemo-radiation therapy and 19 patients, from the RIDER Neuro MRI patient cohort, scanned at diagnosis of tumour recurrence. For both patient cohorts, two sets of scans were taken 2-6 days apart. Each of these patient cohorts was analysed independently to determine if findings were consistent across different acquisition parameters. Parametric maps of Apparent Diffusion Coefficient (ADC) and Cerebral Blood Volume (CBV) were obtained from DWI and DCE data, respectively. Intensity normalisation and noise filtering were applied to the parametric maps in multiple permutations to give 7 distinct standardisation methods. Shape, first order and second order radiomic features for the parametric maps were calculated within the Gross Tumour Volume (GTV). The Intraclass Correlation Coefficient (ICC) was calculated between the feature value at each imaging timepoint. The ICC of first and second order features derived from images with each standardisation method was compared to the ICC of corresponding features derived from images without standardisation. Based on the average ICC of features derived from ADC images without image standardisation, first order features were the most repeatable in both patient cohorts. For ADC derived features in the QIN cohort, shape features were the second most repeatable followed by second order features. For ADC derived features in the RIDER cohort, second order features were the second most repeatable followed by shape features. In CBV images, shape features were the most repeatable followed by second order and then first order in both patient cohorts. No image standardisation method implemented in this study was found to significantly increase the repeatability of ADC-derived first or second order features. For first order CBV features z-score normalisation without noise filtering produced a significant improvement in feature repeatability in both patient cohorts. Radiomic feature repeatability is impacted by feature class. Image standardisation methods implemented in this study were not found to be effective at improving the repeatability of ADC-derived features and had limited utility for improving CBV derived features. Future radiomic studies should consider feature repeatability as an important factor in feature selection.