Physical and Engineering Sciences in Medicine最新文献

Performance testing of gamma cameras and single photon computed tomography/computed tomography (SPECT/CT) systems is not subject to regulatory requirements across states and territories in Australia. Internationally recognised testing standards from organisations such as the National Electrical Manufacturers Association (NEMA) describe methodologies for recommended tests. However, variations exist in suggested quality control (QC) schedules from professional bodies such as the Australia and New Zealand Society of Nuclear Medicine (ANZSNM). In this study, a survey was conducted to benchmark current QC programs across a selected sample of eight standalone and networked Australian public hospitals. Vendor-specific flood-field uniformity (intrinsic or extrinsic/system) verification without photomultiplier (PMT) tuning and CT QC were performed at all sites. Weekly and monthly PMT tuning followed by intrinsic flood-field verifications were performed at most sites. At least half of the sites performed monthly centre of rotation (COR) offset verifications. SPECT/CT alignment calibrations and verifications were undertaken by service engineers at all sites, and periodic verifications were performed by local staff at varying frequencies. Variations were observed for other periodic QC tests such as spatial resolution and planar sensitivity. Similarly, variations were observed for tests specific to whole-body systems and SPECT systems. Most sites checked daily and periodic QC results against pass/fail criteria set by vendors. Additional analyses of the QC results, including trend analysis and periodic reviews, were not common practice. The lack of regulatory requirements is likely to have led to variations in QC tests that are generally either harder to perform or are more labour intensive.

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[¹⁸F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.

The use of Al₂O₃:C-based optically stimulated luminescent dosimeters (OSLDs) in diagnostic X-ray is a challenge because of their energy dependence (ED) and variability of element sensitivity factors (ESFs). This study aims to develop a method to determine ED and ESFs of Landauer nanoDot™ OSLDs for clinical X-ray and investigate the uncertainties associated with ESF and ED correction factors. An area of 2 × 2 cm² at the central axis of the X-ray field was used to establish the ESFs. A total of 80 OSLDs were categorized into "controlled" (n = 40) and "less-controlled" groups (n = 40). The ESFs of the OSLDs were determined using an 80 kVp X-ray beam quality in free-air geometry. The OSLDs were cross-calibrated with an ion chamber to establish the average calibration coefficient and ESFs. The OSLDs were then irradiated at tube potentials ranging from 50 to 150 kVp to determine their ED. The uniformity of the X-ray field was ± 1.5% at 100 cm source-to-surface distance. The batch homogeneities of user-defined ESFs were 2.4% and 8.7% for controlled and less-controlled OSLDs, respectively. The ED of OSLDs ranged from 1.125 to 0.812 as tube potential increased from 50 kVp to 150 kVp. The total uncertainty of OSLDs, without ED correction, could be as high as 16%. After applying ESF and ED correction, the total uncertainties were reduced to 6.3% in controlled OLSDs and 11.6% in less-controlled ones. OSLDs corrected with user-defined ESF and ED can reduce the uncertainty of dose measurements in diagnostic X-rays, particularly in managing less-controlled OSLDs.

Plasmonic photothermal therapy (PPTT) involves the use of nanoparticles and near-infrared radiation to attain a temperature above 50 °C within the tumor for its thermal damage. PPTT is largely explored for superficial tumors, and its potential to treat deeper subsurface tumors is dealt feebly, requiring the assessment of thermal damage for such tumors. In this paper, the extent of thermal damage is numerically analyzed for PPTT of invasive ductal carcinoma (IDC) situated at 3-9 mm depths. The developed numerical model is validated with suitable tissue-tumor mimicking phantoms. Tumor (IDC) embedded with gold nanorods (GNRs) is subjected to broadband near-infrared radiation. The effect of various GNRs concentrations and their spatial distributions [viz. uniform distribution, intravenous delivery (peripheral distribution) and intratumoral delivery (localized distribution)] are investigated for thermal damage for subsurface tumors situated at various depths. Results show that lower GNRs concentrations lead to more uniform internal heat generation, eventually resulting in uniform temperature rise. Also, the peripheral distribution of nanoparticles provides a more uniform spatial temperature rise within the tumor. Overall, it is concluded that PPTT has potential to induce thermal damage for subsurface tumors, at depths of upto 9 mm, by proper choice of nanoparticle distribution, dose/concentration and irradiation parameters based on the tumor location. Moreover, intravenous administration of nanoparticles seems a good choice for shallower tumors, while for deeper tumors, uniform distribution is required to attain the necessary thermal damage. In the future, the algorithm may be extended further, involving 3D patient-specific tumors and through mice model-based experiments.

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 $\times {10}^{-4}$ , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 $\times {10}^{-4}$ , for T2w, and .992 ± 2.63 $\times {10}^{-4}$ , 2.49 ± 6.89 $\times {10}^{-2}$ , 40.51 ± 0.22, and 0.993 ± 3.40 $\times {10}^{-4}$ for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identify cardiac diseases in diabetic patients in the absence of expert intervention. Two models are introduced in this study: The MLP model effectively distinguishes between individuals with heart diseases and those without, achieving a high level of accuracy. Subsequently, the deep CNN model further refines the identification of specific cardiac conditions. The PTB-Diagnostic ECG dataset commonly used in the field of biomedical signal processing and machine learning, particularly for tasks related to electrocardiogram (ECG) analysis. a widely recognized dataset in the field, is employed for training, testing, and validation of both the MLP and CNN models. This dataset comprises a diverse range of ECG recordings, providing a comprehensive representation of cardiac conditions. The proposed models feature two hidden layers with weights and biases in the MLP, and a three-layer CNN, facilitating the mapping of ECG data to different disease classes. The experimental results demonstrate that the MLP and deep CNN based models attain accuracy levels of up to 90.0% and 98.35%, and sensitivity 97.8%, 95.77%, specificity 88.9%, 96.3% F1-Score 93.13%, 95.84% respectively. These outcomes underscore the efficacy of deep learning approaches in automating the diagnosis of cardiac diseases through ECG analysis, showcasing the potential for accurate and efficient healthcare solutions.

Breast cancer is the second leading cause of death for women worldwide, and detecting cancer at an early stage increases the survival rate by 97%. In this study, a novel textile-based ultrawideband (UWB) microstrip patch antenna was designed and modeled to work in the 2-11.6 GHz frequency range and a simulation was used to test its performance in early breast cancer detection. The antenna was designed with an overall size of 31*31 mm ${}^2$ using a denim substrate and 100% metal polyamide-based fabric with copper, silver, and nickel to provide comfort for the wearer. The designed antenna was tested in four numerical breast models. The models ranged from simple tumor-free to complex models with small tumors. The size, structure, and position of the tumor were modified to test the suggested ability of the antenna to detect cancers with different shapes, sizes, and positions. The specific absorption rate (SAR), return loss (S11), and voltage standing wave ratio (VSWR) were calculated for each model to measure the antenna performance. The simulation results showed that SAR values were between 1.6 and 2 W/g (10 g SAR) and were within the allowed range for medical applications. Additionally, the VSWR remained in an acceptable range from 1.15 to 2. Depending on the size and location of the tumor, the antenna return losses of the four models ranged from $-$ 36 to $-$ 18.5 dB. The effect of bending was tested to determine the flexibility. The antenna proved to be highly effective and capable of detecting small tumors with diameters of up to 2 mm.

Manual contouring of organs at risk (OAR) is time-consuming and subject to inter-observer variability. AI-based auto-contouring is proposed as a solution to these problems if it can produce clinically acceptable results. This study investigated the performance of multiple AI-based auto-contouring systems in different OAR segmentations. The auto-contouring was performed using seven different AI-based segmentation systems (Radiotherapy AI, Limbus AI version 1.5 and 1.6, Therapanacea, MIM, Siemens AI-Rad Companion and RadFormation) on a total of 42 clinical cases with varying anatomical sites. Volumetric and surface dice similarity coefficients and maximum Hausdorff distance (HD) between the expert's contours and automated contours were calculated to evaluate their performance. Radiotherapy AI has shown better performance than other software in most tested structures considered in the head and neck, and brain cases. No specific software had shown overall superior performance over other software in lung, breast, pelvis and abdomen cases. Each tested AI system was able to produce comparable contours to the experts' contours of organs at risk which can potentially be used for clinical use. A reduced performance of AI systems in the case of small and complex anatomical structures was found and reported, showing that it is still essential to review each contour produced by AI systems for clinical uses. This study has also demonstrated a method of comparing contouring software options which could be replicated in clinics or used for ongoing quality assurance of purchased systems.

The kV cone beam computed tomography (CBCT) is one of the most common imaging modalities used for image-guided radiation therapy (IGRT) procedures. Additional doses are delivered to patients, thus assessment and optimization of the imaging doses should be taken into consideration. This study aimed to investigate the influence of using fixed and patient-specific FOVs on the patient dose. Monte Carlo simulations were performed to simulate kV beams of the imaging system integrated into Truebeam linear accelerator using BEAMnrc code. Organ and size-specific effective doses resulting from chest and pelvis scanning protocols were estimated with DOSXYZnrc code using a phantom library developed by the National Cancer Institute (NCI) of the US. The library contains 193 (100 male and 93 female) mesh-type computational human adult phantoms, and it covers a large ratio of patient sizes with heights and weights ranging from 150 to 190 cm and 40 to 125 kg. The imaging doses were assessed using variable FOV of three sizes, small (S), medium (M), and large (L) for each scan region. The results show that the FOV and the patient size played a major role in the scan dose. The average percentage differences (PDs) for doses of organs that were fully inside the different FOVs were relatively low, all within 11% for both protocols. However, doses to organs that were scanned partially or near the FOVs were affected significantly. For the chest protocol, the inclusion of the thyroid in the scan field could give a dose of 1-7 mGy/100 mAs to the thyroid, compared to 0.4-1 mGy/100 mAs when it was excluded. Similarly, on average, testes doses could be 6 mGy/100 mAs for the male pelvis protocol compared to 3 mGy/100 mAs when it did not lie in the field irradiated. These dose differences resulted in an average increase of up to 27% in the size-specific effective dose of the protocols. Since changing the field size is possible for CBCT scans, the results suggest that patient-specific scanning protocols could be applied for each scan area in a manner similar to that used for CT scans. Adjustment of the FOV size should be subject to the clinical needs, and assist in improving the treatment accuracy. The patient's height and weight might be considered as the main factors upon which, the selection of the appropriate patient-specific protocol is based. This approach should optimize the imaging doses used for IGRT procedures by minimizing doses of a large ratio of patients.

Regarding motor processes, modeling healthy people's brains is essential to understand the brain activity in people with motor impairments. However, little research has been undertaken when external forces disturb limbs, having limited information on physiological pathways. Therefore, in this paper, a nonlinear delay differential embedding model is used to estimate the brain response elicited by externally controlled wrist movement in healthy individuals. The aim is to improve the understanding of the relationship between a controlled wrist movement and the generated cortical activity of healthy people, helping to disclose the underlying mechanisms and physiological relationships involved in the motor event. To evaluate the model, a public database from the Delft University of Technology is used, which contains electroencephalographic recordings of ten healthy subjects while wrist movement was externally provoked by a robotic system. In this work, the cortical response related to movement is identified via Independent Component Analysis and estimated based on a nonlinear delay differential embedding model. After a cross-validation analysis, the model performance reaches 90.21% ± 4.46% Variance Accounted For, and Correlation 95.14% ± 2.31%. The proposed methodology allows to select the model degree, to estimate a general predominant operation mode of the cortical response elicited by wrist movement. The obtained results revealed two facts that had not previously been reported: the movement's acceleration affects the cortical response, and a common delayed activity is shared among subjects. Going forward, identifying biomarkers related to motor tasks could aid in the evaluation of rehabilitation treatments for patients with upper limbs motor impairments.