In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of iterative algorithms as well as FDK, including a range of algorithms in the SART family, the Krylov subspace family and a range of methods using total variation regularization. Additionally, the toolbox has GPU-accelerated projection and back projection using the latest techniques and it has a modular design that facilitates the implementation of new algorithms. We present an overview of the structure and techniques used in the creation of the toolbox, together with two usage examples. The TIGRE Toolbox is released under an open source licence, encouraging people to contribute.
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Ander Biguri et al 2016 Biomed. Phys. Eng. Express 2 055010
Benita S Mackay et al 2021 Biomed. Phys. Eng. Express 7 052002
Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.
Taisa Higino and Rodrigo França 2022 Biomed. Phys. Eng. Express 8 042001
The use of nanoparticles as biomaterials with applications in the biomedical field is growing every day. These nanomaterials can be used as contrast imaging agents, combination therapy agents, and targeted delivery systems in medicine and dentistry. Usually, nanoparticles are found as synthetic or natural organic materials, such as hydroxyapatite, polymers, and lipids. Besides that, they are could also be inorganic, for instance, metallic or metal-oxide-based particles. These inorganic nanoparticles could additionally present magnetic properties, such as superparamagnetic iron oxide nanoparticles. The use of nanoparticles as drug delivery agents has many advantages, for they help diminish toxicity effects in the body since the drug dose reduces significantly, increases drugs biocompatibility, and helps target drugs to specific organs. As targeted-delivery agents, one of the applications uses nanoparticles as drug delivery particles for bone-tissue to treat cancer, osteoporosis, bone diseases, and dental treatments such as periodontitis. Their application as drug delivery agents requires a good comprehension of the nanoparticle properties and composition, alongside their synthesis and drug attachment characteristics. Properties such as size, shape, core-shell designs, and magnetic characteristics can influence their behavior inside the human body and modify magnetic properties in the case of magnetic nanoparticles. Based on that, many different studies have modified the synthesis methods for these nanoparticles and developed composite systems for therapeutics delivery, adapting, and improving magnetic properties, shell-core designs, and particle size and nanosystems characteristics. This review presents the most recent studies that have been presented with different nanoparticle types and structures for bone and dental drug delivery.
Nadia Muhammad Hussain et al 2024 Biomed. Phys. Eng. Express 10 022002
Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.
Li Liu et al 2018 Biomed. Phys. Eng. Express 4 015004
Adhesives that involve adhesion to the skin have been of great technological importance in medical or pharmaceutical fields, including recently emerging wearable sensors and electronics. The objective of this work was to evaluate the performances of silicone-based adhesives with skin, using a peel adhesion test. Specifically, we explored the effect of adhesive cleansing, which is an inevitable daily event for patients' comfort in long-term applications. Firstly, three medical grade silicone gels, Silbione® RT 4717, Silbione® RT 4642, and Silpuran® 2130, were used to fabricate adhesive pads. Their peel strength values were subsequently measured and compared, among which Silbione® RT gel 4717 possessed the highest peel strength. Therefore, it was selected as the raw material to fabricate the pads with a thickness range of 640–740 μm. Secondly, the peel adhesion of Silbione® RT 4717 adhesive pad was further compared with a series of commercial products that employ various medical-grade adhesives. The peel strength results indicated that our custom-made adhesive pad had an adequately strong adhesion for clinical use. Thirdly, in order to observe and predict the long-term performance of the adhesives, an aging test was performed in an ambient environment, revealing that Silbione® RT 4717 adhesive remained highly sticky for 5 days. Lastly, adequate cleansing protocols were established by monitoring the changes in peel strength after washing and wiping events. The reusability analysis showed that Silbione® 4717 adhesive pad was reusable in a one-week period for the washing method and 3 days for the wiping method.
Sotiris Raptis et al 2024 Biomed. Phys. Eng. Express 10 035016
Radiomics-based prediction models have shown promise in predicting Radiation Pneumonitis (RP), a common adverse outcome of chest irradiation. Τhis study looks into more than just RP: it also investigates a bigger shift in the way radiomics-based models work. By integrating multi-modal radiomic data, which includes a wide range of variables collected from medical images including cutting-edge PET/CT imaging, we have developed predictive models that capture the intricate nature of illness progression. Radiomic features were extracted using PyRadiomics, encompassing intensity, texture, and shape measures. The high-dimensional dataset formed the basis for our predictive models, primarily Gradient Boosting Machines (GBM)—XGBoost, LightGBM, and CatBoost. Performance evaluation metrics, including Multi-Modal AUC-ROC, Sensitivity, Specificity, and F1-Score, underscore the superiority of the Deep Neural Network (DNN) model. The DNN achieved a remarkable Multi-Modal AUC-ROC of 0.90, indicating superior discriminatory power. Sensitivity and specificity values of 0.85 and 0.91, respectively, highlight its effectiveness in detecting positive occurrences while accurately identifying negatives. External validation datasets, comprising retrospective patient data and a heterogeneous patient population, validate the robustness and generalizability of our models. The focus of our study is the application of sophisticated model interpretability methods, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), to improve the clarity and understanding of predictions. These methods allow clinicians to visualize the effects of features and provide localized explanations for every prediction, enhancing the comprehensibility of the model. This strengthens trust and collaboration between computational technologies and medical competence. The integration of data-driven analytics and medical domain expertise represents a significant shift in the profession, advancing us from analyzing pixel-level information to gaining valuable prognostic insights.
Renata Saha et al 2024 Biomed. Phys. Eng. Express 10 035028
To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve's complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(μMS) is showing incredible promise. This neuromodulation technique uses microcoils(μcoils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday's law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidal μcoil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) following μMS of the vagus. The μcoils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000 μsec) at a frequency of 20 Hz. Under the influence of isoflurane, μMS of the left vagus at 1000 μsec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast, μMS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagal μMS suggesting the drop in MAP associated with vagus μMS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed upon μMS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy of μMS on the vagal nerve. Absence of HR modulation upon μMS could offer alternative treatment options using VNS with fewer heart-related side-effects.
Muhammad Suhaib Shahid et al 2024 Biomed. Phys. Eng. Express 10 032001
The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.e. not only mouth) as well as synergistic stimuli such as audio and facial thermal data. 3D modelling technologies capable of accurately and efficiently capturing a complete representation of the mouth for an individual have broad applications in the study of oral actions, due to their cost-effectiveness and time efficiency. This review delves into the field of clinical phonetics to classify oral actions pertaining to both speech and non-speech movements, identifying how the various vocal organs play a role in the articulatory and masticatory process. Vitaly, it provides a summation of 12 articulatory recording methods, forming a tool to be used by researchers in identifying which method of recording is appropriate for their work. After addressing the cost and resource-intensive limitations of existing methods, a new system of modelling is proposed that leverages external to internal correlation modelling techniques to create a more efficient models of the oral cavity. The vision is that the outcomes will be applicable to a broad spectrum of oral functions related to physiology, health and wellbeing, including speech, oral processing of foods as well as dental health. The applications may span from speech correction, designing foods for the aging population, whilst in the dental field we would be able to gain information about patient's oral actions that would become part of creating a personalised dental treatment plan.
Yunfei Hu et al 2022 Biomed. Phys. Eng. Express 8 025023
In this study, the performance of a new iterative reconstruction algorithm, the pre-clinical AcurosXB iCBCT algorithm, has been characterized on Varian Halcyon linear accelerators with respect to the potential of radiotherapy dose calculations on CBCT images. The study utilized various phantom setups to verify the accuracy of the pre-clinical algorithm under different scatter conditions and compared dose calculations performed on CBCT images reconstructed with the pre-clinical algorithm to those performed on typical planning CT images. The results indicated that despite showing improvements compared to the existing iCBCT protocol, certain restrictions should be introduced when the pre-clinical AcurosXB iCBCT algorithm was used for dose calculations. Changes in the scatter condition exhibited a larger effect on CBCTs than on planning CTs. Therefore, users should be careful in offsetting the patient and positioning the patient's arms if the resultant images will be used for dose calculations. In addition, protocols with different kV settings should be approached with caution, where 100 kV protocols should only be used to scan the head and neck area, while the rest of the body should be scanned with the 125 kV and 140 kV protocols. When the patient is set up properly and the appropriate energy is selected for the anatomical area, the uncertainty of using the novel AcurosXB iCBCT algorithm for treatment planning dose calculation is within ±2.0%.
Fan Peng et al 2024 Biomed. Phys. Eng. Express 10 035038
Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B-US) images created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection. Approach. We presented an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection. Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers. Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications.
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Jingyuan Wu et al 2024 Biomed. Phys. Eng. Express 10 045026
Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.
Ratheesh K E and Mayakannan Krishnan 2024 Biomed. Phys. Eng. Express 10 045025
Radiotherapy (RT) is one of the major treatment modalities among surgery and chemotherapy for carcinoma breast. The surface dose study of modified reconstructive constructive Mastectomy (MRM) breast is important due to the heterogeneity in the body contour and the conventional treatment angle to save the lungs and heart from the radiation. These angular entries of radiation beam cause an unpredictable dose deposition on the body surface, which has to be monitored. Thermoluminescent dosimeter (TLD) or optically stimulated luminescent dosimeter (nano OSLD) are commonly preferable dosimeters for this purpose. The surface dose response of TLD and nano OSLD during MRM irradiation has been compared with the predicted dose from the treatment planning system (TPS). The study monitored 100 MRM patients by employing a total 500 dosimeters consisting of TLD (n = 250) and nano OSLD (n = 250), during irradiation from an Elekta Versa HD 6 MV Linear accelerator. The study observed a variance of 3.9% in the dose measurements for TLD and 3.2% for nano OSLD from the planned surface dose, with a median percentage dose of 44.02 for nano OSLD and 40.30 for TLD (p value 0.01). There was no discernible evidence of variation in dose measurements attributable to differences in field size or from patient to patient. Additionally, no variation was observed in dose measurements when comparing the placement of the dosimeter from central to off-centre positions. In comparison, a minor difference in dose measurements were noted between TLD and nano OSLD, The study's outcomes support the applicability of both TLD and nano OSLD as effective dosimeters during MRM breast irradiation for surface dose evaluation.
J E Parker et al 2024 Biomed. Phys. Eng. Express 10 045024
A study of burn thresholds from superficially penetrating radio-frequency (RF) energy at 8.2 and 95 GHz for swine skin was conducted. The study determined the thresholds for superficial, partial-thickness, and full-thickness burn severities after 5 seconds of exposure at power densities of 4–30 W/cm2 and 2–15 W/cm2 at 8.2 and 95 GHz, respectively. There were significant differences in he burn thresholds at the different severities between the two frequencies due to the large difference in energy penetration depths. Biopsies were collected from each burn site at 1, 24, 72, and 168 hr post exposure. Each sample was assessed by a burn pathologist against 20 histological factors to characterize the damage resulting from these RF overexposures. A one-dimensional, layered digital phantom that utilized realistic values for dielectric and thermal properties was used to explain some observed thresholds. The results of the heating and cooling response of the animal model and histology scores of each exposure are provided to enhance future efforts at simulation of RF overexposures and to establish damage thresholds.
Udit Sharma et al 2024 Biomed. Phys. Eng. Express 10 045022
Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequent activation of the prosthetic digits accordingly. Vision-based grasp classification techniques offer improved coordination between amputees and prosthetic hands without physical contact. Deep learning methods, particularly Convolutional Neural Networks (CNNs), are utilized to process visual information for classification. The key challenge lies in developing a model that can effectively generalize across various object shapes and accurately classify grasp classes. To address this, a compact CNN model named GraspCNet is proposed, specifically designed for grasp classification in prosthetic hands. The use of separable convolutions reduces the computational burden, making it potentially suitable for real-time applications on embedded systems. The GraspCNet model is designed to learn and generalize from object shapes, allowing it to effectively classify unseen objects beyond those included in the training dataset. The proposed model was trained and tested using various standard object data sets. A cross-validation strategy has been adopted to perform better in seen and unseen object class scenarios. The average accuracy achieved was 82.22% and 75.48% in the case of seen, and unseen object classes respectively. In computer-based real-time experiments, the GraspCNet model achieved an accuracy of 69%. A comparative analysis with state-of-the-art techniques revealed that the proposed GraspCNet model outperformed most benchmark techniques and demonstrated comparable performance with the DcnnGrasp method. The compact nature of the GraspCNet model suggests its potential for integration with other sensing modalities in prosthetic hands.
Steven Squires et al 2024 Biomed. Phys. Eng. Express 10 045021
Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions. Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained. Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases. Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.
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Mohammed Ali et al 2024 Biomed. Phys. Eng. Express 10 032003
Guided tissue/bone regeneration (GTR/GBR) is a widely used technique in dentistry to facilitate the regeneration of damaged bone and tissue, which involves guiding materials that eventually degrade, allowing newly created tissue to take its place. This comprehensive review the evolution of biomaterials for guided bone regeneration that showcases a progressive shift from non-resorbable to highly biocompatible and bioactive materials, allowing for more effective and predictable bone regeneration. The evolution of biomaterials for guided bone regeneration GTR/GBR has marked a significant progression in regenerative dentistry and maxillofacial surgery. Biomaterials used in GBR have evolved over time to enhance biocompatibility, bioactivity, and efficacy in promoting bone growth and integration. This review also probes into several promising fabrication techniques like electrospinning and latest 3D printing fabrication techniques, which have shown potential in enhancing tissue and bone regeneration processes. Further, the challenges and future direction of GTR/GBR are explored and discussed.
Muhammad Suhaib Shahid et al 2024 Biomed. Phys. Eng. Express 10 032001
The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.e. not only mouth) as well as synergistic stimuli such as audio and facial thermal data. 3D modelling technologies capable of accurately and efficiently capturing a complete representation of the mouth for an individual have broad applications in the study of oral actions, due to their cost-effectiveness and time efficiency. This review delves into the field of clinical phonetics to classify oral actions pertaining to both speech and non-speech movements, identifying how the various vocal organs play a role in the articulatory and masticatory process. Vitaly, it provides a summation of 12 articulatory recording methods, forming a tool to be used by researchers in identifying which method of recording is appropriate for their work. After addressing the cost and resource-intensive limitations of existing methods, a new system of modelling is proposed that leverages external to internal correlation modelling techniques to create a more efficient models of the oral cavity. The vision is that the outcomes will be applicable to a broad spectrum of oral functions related to physiology, health and wellbeing, including speech, oral processing of foods as well as dental health. The applications may span from speech correction, designing foods for the aging population, whilst in the dental field we would be able to gain information about patient's oral actions that would become part of creating a personalised dental treatment plan.
Abdallah El Ouaridi et al 2024 Biomed. Phys. Eng. Express 10 032002
Positron emission tomography (PET) is a powerful medical imaging modality used in nuclear medicine to diagnose and monitor various clinical diseases in patients. It is more sensitive and produces a highly quantitative mapping of the three-dimensional biodistribution of positron-emitting radiotracers inside the human body. The underlying technology is constantly evolving, and recent advances in detection instrumentation and PET scanner design have significantly improved the medical diagnosis capabilities of this imaging modality, making it more efficient and opening the way to broader, innovative, and promising clinical applications. Some significant achievements related to detection instrumentation include introducing new scintillators and photodetectors as well as developing innovative detector designs and coupling configurations. Other advances in scanner design include moving towards a cylindrical geometry, 3D acquisition mode, and the trend towards a wider axial field of view and a shorter diameter. Further research on PET camera instrumentation and design will be required to advance this technology by improving its performance and extending its clinical applications while optimising radiation dose, image acquisition time, and manufacturing cost. This article comprehensively reviews the various parameters of detection instrumentation and PET system design. Firstly, an overview of the historical innovation of the PET system has been presented, focusing on instrumental technology. Secondly, we have characterised the main performance parameters of current clinical PET and detailed recent instrumental innovations and trends that affect these performances and clinical practice. Finally, prospects for this medical imaging modality are presented and discussed. This overview of the PET system's instrumental parameters enables us to draw solid conclusions on achieving the best possible performance for the different needs of different clinical applications.
Nadia Muhammad Hussain et al 2024 Biomed. Phys. Eng. Express 10 022002
Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.
Wei-Jen Chan and Huatian Li 2024 Biomed. Phys. Eng. Express 10 022001
In recent years, nanoparticles (NPs) have been extensively developed as drug carriers to overcome the limitations of cancer therapeutics. However, there are several biological barriers to nanomedicines, which include the lack of stability in circulation, limited target specificity, low penetration into tumors and insufficient cellular uptake, restricting the active targeting toward tumors of nanomedicines. To address these challenges, a variety of promising strategies were developed recently, as they can be designed to improve NP accumulation and penetration in tumor tissues, circulation stability, tumor targeting, and intracellular uptake. In this Review, we summarized nanomaterials developed in recent three years that could be utilized to improve drug delivery for cancer treatments.
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Manfred Köller et al 2018 Biomed. Phys. Eng. Express 4 055002
The role of bacterial cell division on the damage of adherent bacteria to titanium (Ti) nano-pillar cicada wing like surface was analyzed. Therefore nano-pillar Ti thin films were fabricated by glancing angle sputter deposition (GLAD) on silicon substrates. Gram-negative E. coli bacteria were allowed to adhere and to proliferate on these nanostructured samples for 3 h at 37 °C either under optimal cell growth conditions (brain heart infusion medium, BHI) or limited growth conditions (RPMI1640 medium). The bacteria adhered to the samples in both media. Compared to BHI medium the growth of E. coli in RPMI1640 medium was significantly inhibited. Concomitantly, the ratio of dead/living adherent bacteria on the nano-pillar surface was significantly decreased after the incubation period in RPMI1640. In addition, when the bacterial proliferation was biochemically halted using DL-serine-hydroxamate a comparable decrease in the ratio of dead/living adherent bacteria was also obtained in BHI medium. These results indicate that cell growth of adherent E. coli which is accompanied by cell elongations of the rod structure is involved in the damage induced by the titanium nano-pillar surface.
James Archer et al 2018 Biomed. Phys. Eng. Express 4 044003
Cherenkov radiation is the primary source of unwanted light in a scintillator dosimetry system. In this work we compare two techniques for temporally separating Cherenkov radiation from a slow scintillator signal. These techniques are applicable to a pulsed radiation beam. We found that by analysing the rising edge of the light pulse to identify the fast Cherenkov light only removed 74% of the Cherenkov light. By integrating the tail of the signal where only scintillation light is present a more accurate result is achieved. The average of the results of the two methods provides up to a 90% improvement in the accuracy of the relative dose when compared to ionisation chamber, in certain measurements. This work demonstrates an alternative methodology for the removal of Cherenkov light using signal analysis, while preserving all the scintillation light signal and minimising the bulk of the experimental equipment.
Natasha Maurmann et al 2017 Biomed. Phys. Eng. Express 3 045005
Materials, such as biopolymers, can be applied to produce scaffolds as mechanical support for cell growth in regenerative medicine. Two examples are polycaprolactone (PCL) and poly (lactic-co-glycolic acid) (PLGA), both used in this study to evaluate the behavior of umbilical cord-derived mesenchymal stem cells. The scaffolds were produced by the 3D printing technique using PCL as a polymer covered with PLGA fibers obtained by electrospinning. The cells were seeded in three concentrations: 8.5 × 103; 25.5 × 103 and 51.0 × 103 on the two surfaces of the scaffolds. With scanning electron microscopy (SEM), it was observed that the electrospun fibers were integrated into the 3D printed matrices. Confocal laser scanning microscopy and SEM confirmed the presence of attached cells and the lactate dehydrogenase release test showed the scaffolds were not cytotoxic. The cells were able to differentiate into osteogenic and chondrogenic lineages on the scaffolds. Mechanical test showed that the cells seeded on the 3D printed PCL matrices coated with PLGA electrospun nanofibers (3D + ES + SC) did not show significant difference in tensile modulus than the pure PCL matrix (3D) or PCL matrices coated with PLGA electrospun nanofibers (3D + ES). The combination of the two polymers facilitated the production of a support with greater mechanical stability due to the presence of the 3D printed PCL matrices fabricated by melted filaments and greater cell adhesion due to the PLGA fibers. The scaffolds are suitable for use in cell therapy and also for tissue regeneration purposes.
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Sohn et al
Small field dosimetry presents unique challenges with source occlusion, lateral charged particle equilibrium and detector size. As detector volume decreases, signal strength declines while noise increases, deteriorating the signal-to-noise ratio (SNR). This issue may be compounded by triaxial cables connecting detectors to electrometers. However, effects of cables, critical for precision dosimetry, are often overlooked. There is a need to evaluate triaxial cable and detector impacts on SNR in small fields. To evaluate the influence of triaxial cables and microdetectors on signal-to-noise ratios in small-field dosimetry. This study also aims to establish the importance of cable quality assurance for measurement accuracy.Six 9.1 m length triaxial cables from different manufacturers were tested with six microdetectors . A 6 MV photon beam (TrueBeam) was used, with a water phantom at 5 cm depth with 0.5×0.5 cm2 to 10×10 cm2 fields at 600 MU/min. Readings were acquired using cable-detector permutations with a dedicated electrometer. Cables had differing connector types, conductor materials, insulation, and diameter. Detectors had various sensitive volumes, materials, typical signals, and bias voltages.
Normalized FOFs showed 13.4% and 4.6% variation across cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively. The maximum estimated error between any cable-detector combinations was 0.2%. No consistent FOF trend was observed with increasing cable diameter, likely due to different types of detectors used. However, absolute FOF differences of 0.9% and 0.3% were noted between cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively.
Regular triaxial cable quality assurance is critical for precision small field dosimetry. A national protocol is needed to standardize cable evaluations/calibrations, particularly for small signals from modern detectors. This could enhance measurement accuracy and treatment delivery with advanced small-field radiotherapy techniques that promise improved patient outcomes. Further studies should expand detector and cable models tested across institutions to establish robust quality control guidelines.
Akamine et al
Objective. Detection of the epileptogenic zone is critical, especially for patients with
drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during
spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians'
surgical decisions and improve patient outcomes. Approach. We designed, fabricated,
and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with
a unique embedded configuration. This array was compared to a commercially available
microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex
elicited by somatosensory evoked potentials and pilocarpine-induced seizures. Main results
Evoked potentials and spatial maps recorded by the multi-scale array ("micros", "mesos", and
"macros" refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively)
were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher
peak amplitudes and greater signal power than those recorded by the larger mesos and macro.
Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-
scale, similar to the commercially available array. The micros had greater SNR than the mesos
and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical
stimulation experimentation, the mesos successfully elicited motor effects. Significance.
Previous studies have compared macro- and microelectrodes for localizing seizure activity in
adjacent regions. The multi-scale design validated here is the first to simultaneously measure
macro- and microelectrode signals from the same overlapping cortical area. This enables direct
comparison of microelectrode recordings to the macroelectrode recordings used in standard
neurosurgical practice. Previous studies have also shown that cortical regions generating
high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More
accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients
Suzuki et al
Evaluating neutron output is important to ensure proper dose delivery for patients in boron neutron capture therapy (BNCT). It requires efficient quality assurance (QA) and quality control (QC) to maintain measurement accuracy. This study investigated the optimal measurement conditions for QA/QC of activation measurements using a high-purity germanium (HP-Ge) detector in an accelerator-based boron neutron capture therapy (AB-BNCT) system employing a lithium target. The QA/QC uncertainty of the activation measurement was evaluated based on counts, reproducibility, and standard radiation source uncertainties. Measurements in a polymethyl methacrylate (PMMA) cylindrical phantom using aluminum-manganese (Al–Mn) foils and aluminum-gold (Al–Au) foils and measurements in a water phantom using gold wire with and without cadmium cover were performed to determine the optimal measurement conditions. The QA/QC uncertainties of the activation measurements were 4.5% for Au and 4.6% for Mn. The optimum irradiation proton charge and measurement time were determined to be 36 C and 900 s for measurements in a PMMA cylindrical phantom, 7.0 C and 900 s for gold wire measurements in a water phantom, and 54 C and 900 s at 0–2.2 cm depth and 3,600 s at deeper depths for gold wire measurements with cadmium cover. Our results serve as a reference for determining measurement conditions when performing QA/QC of activation measurements using HP-Ge detectors at an AB-BNCT employing a lithium target.
Mürer et al
Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or "label-free" imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques with an emphasis on FPM and advanced X-ray microscopies. We next demonstrate with our own results computational imaging through Fourier ptychographic microscopy (FPM) and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are reported. X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are also presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.
Anam et al
Purpose: This study aimed to develop a method to extract statistical low-contrast detectability (LCD) and develop contrast-detail (C-D) curves from clinical patient images.
Method: The LCD measurement and C-D curve development on the patient images were carried out in the region of air surrounding the patient as an alternative for a homogeneous region within the patient. A simple graphical user interface (GUI) was created to set the initial configuration for interest (ROI), ROI size, and minimum-detectable contrast (MDC). The process was started by segmenting with a threshold between -980 HU and -1024 HU to get an air mask. The mask was trimmed from the patient center coordinates to avoid distortion from the table scan. The mask was used to automatically place square ROIs with a predetermined size. The mean pixel values in Hounsfield units (HU) within each ROI was calculated. Next, the standard deviation (SD) from all the means was obtained. The for a particular target size was generated by multiplying SD by 3.29. A C-D curve was obtained by iterating this process for other ROI sizes. The method was applied to the homogeneous phantom to find the correlation of the parameters inside and outside of the phantom, and implemented on 30 patient images.
Results: Phantom images show a very strong correlation between LCDs obtained from outside and inside the phantom, with R2 of 0.97, 0.96, 0.92, 0.93, 0.80, and 0.88 for tube currents of 80, 100, 120, 140, 160, and 200 mA, respectively. This showed that the air region can act as a surrogate for a homogenous region in the phantom to obtain the LCD and C-D curve. 
Conclusion: The C-D curves obtained from outside the ACR phantom show a strong linear correlation with those from inside the phantom.
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James J Sohn and Indra J Das 2024 Biomed. Phys. Eng. Express
Small field dosimetry presents unique challenges with source occlusion, lateral charged particle equilibrium and detector size. As detector volume decreases, signal strength declines while noise increases, deteriorating the signal-to-noise ratio (SNR). This issue may be compounded by triaxial cables connecting detectors to electrometers. However, effects of cables, critical for precision dosimetry, are often overlooked. There is a need to evaluate triaxial cable and detector impacts on SNR in small fields. To evaluate the influence of triaxial cables and microdetectors on signal-to-noise ratios in small-field dosimetry. This study also aims to establish the importance of cable quality assurance for measurement accuracy.Six 9.1 m length triaxial cables from different manufacturers were tested with six microdetectors . A 6 MV photon beam (TrueBeam) was used, with a water phantom at 5 cm depth with 0.5×0.5 cm2 to 10×10 cm2 fields at 600 MU/min. Readings were acquired using cable-detector permutations with a dedicated electrometer. Cables had differing connector types, conductor materials, insulation, and diameter. Detectors had various sensitive volumes, materials, typical signals, and bias voltages.
Normalized FOFs showed 13.4% and 4.6% variation across cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively. The maximum estimated error between any cable-detector combinations was 0.2%. No consistent FOF trend was observed with increasing cable diameter, likely due to different types of detectors used. However, absolute FOF differences of 0.9% and 0.3% were noted between cables for 0.5×0.5 cm2 and 1×1 cm2 fields, respectively.
Regular triaxial cable quality assurance is critical for precision small field dosimetry. A national protocol is needed to standardize cable evaluations/calibrations, particularly for small signals from modern detectors. This could enhance measurement accuracy and treatment delivery with advanced small-field radiotherapy techniques that promise improved patient outcomes. Further studies should expand detector and cable models tested across institutions to establish robust quality control guidelines.
J E Parker et al 2024 Biomed. Phys. Eng. Express 10 045024
A study of burn thresholds from superficially penetrating radio-frequency (RF) energy at 8.2 and 95 GHz for swine skin was conducted. The study determined the thresholds for superficial, partial-thickness, and full-thickness burn severities after 5 seconds of exposure at power densities of 4–30 W/cm2 and 2–15 W/cm2 at 8.2 and 95 GHz, respectively. There were significant differences in he burn thresholds at the different severities between the two frequencies due to the large difference in energy penetration depths. Biopsies were collected from each burn site at 1, 24, 72, and 168 hr post exposure. Each sample was assessed by a burn pathologist against 20 histological factors to characterize the damage resulting from these RF overexposures. A one-dimensional, layered digital phantom that utilized realistic values for dielectric and thermal properties was used to explain some observed thresholds. The results of the heating and cooling response of the animal model and histology scores of each exposure are provided to enhance future efforts at simulation of RF overexposures and to establish damage thresholds.
Steven Squires et al 2024 Biomed. Phys. Eng. Express 10 045021
Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions. Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained. Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases. Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.
Esmaeil Mehrara 2024 Biomed. Phys. Eng. Express 10 045020
Thermoluminescent dosimeters (TLDs) serve as compact and user-friendly tools for various applications, including personal radiation dosimetry and radiation therapy. This study explores the potential of utilizing TLD-100 personal dosimetry, conventionally applied in PET/CT (positron emission tomography/computed tomography) settings, in the PET/MRI (magnetic resonance imaging) environment. The integration of MRI into conventional radiotherapy and PET systems necessitates ionizing radiation dosimetry in the presence of static magnetic fields. In this study, TLD-100 dosimeters were exposed on the surface of a water-filled cylindrical phantom containing PET-radioisotope and positioned on the patient table of a 3 T PET/MRI, where the magnetic field strength is around 0.2 T, aiming to replicate real-world scenarios experienced by personnel in PET/MRI environments. Results indicate that the modified MR-safe TLD-100 personal dosimeters exhibit no significant impact from the static magnetic field of the 3 T PET/MRI, supporting their suitability for personal dosimetry in PET/MRI settings. This study addresses a notable gap in existing literature on the effect of MRI static magnetic field on TLDs.
Wondesen T Gebreamlak and Hassaan H Alkhatib 2024 Biomed. Phys. Eng. Express 10 045017
Purpose. The aim of this study is to determine the planar dose distribution of irregularly-shaped electron beams at their maximum dose depth (zmax) using the modied lateral build-up ratio (LBR) and curve-fitting methods. Methods. Circular and irregular cutouts were created using Cerrobend alloy for a 14 × 14 cm2 applicator. Percentage depth dose (PDD) at the standard source-surface-distance (SSD = 100 cm) and point dose at different SSD were measured for each cutout. Orthogonal profiles of the cutouts were measured at zmax. Data were collected for 6, 9, 12, and 15 MeV electron beam energies on a VERSA HDTM LINAC using the IBA Blue Phantom2 3D water phantom system. The planar dose distributions of the cutouts were also measured at zmax in solid water using EDR2 films. Results. The measured PDD curves were normalized to a normalization depth (d0) of 1 mm. The lateral-buildup-ratio (LBR), lateral spread parameter (σR(z)), and effective SSD (SSDeff) for each cutout were calculated using the PDD of the open applicator as the reference field. The modified LBR method was then employed to calculate the planar dose distribution of the irregular cutouts within the field at least 5 mm from the edge. A simple curve-fitting model was developed based on the profile shapes of the circular cutouts around the field edge. This model was used to calculate the planar dose distribution of the irregular cutouts in the region from 3 mm outside to 5 mm inside the field edge. Finally, the calculated planar dose distribution was compared with the film measurement. Conclusions. The planar dose distribution of electron therapy for irregular cutouts at zmax was calculated using the improved LBR method and a simple curve-fitting model. The calculated profiles were within 3% of the measured values. The gamma passing rate with a 3%/3 mm and 10% dose threshold was more than 96%.
Owen Paetkau et al 2024 Biomed. Phys. Eng. Express 10 045014
Background and purpose. To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy. Materials and methods. Training (n = 64) and testing (n = 23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints. Results. A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity = 80 ± 27% / balanced accuracy = 71 ± 23%, testing: sensitivity = 80 ± 10% / balanced accuracy = 73 ± 11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia. Conclusion. Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.
Ian R. Akamine et al 2024 Biomed. Phys. Eng. Express
Objective. Detection of the epileptogenic zone is critical, especially for patients with
drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during
spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians'
surgical decisions and improve patient outcomes. Approach. We designed, fabricated,
and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with
a unique embedded configuration. This array was compared to a commercially available
microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex
elicited by somatosensory evoked potentials and pilocarpine-induced seizures. Main results
Evoked potentials and spatial maps recorded by the multi-scale array ("micros", "mesos", and
"macros" refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively)
were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher
peak amplitudes and greater signal power than those recorded by the larger mesos and macro.
Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-
scale, similar to the commercially available array. The micros had greater SNR than the mesos
and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical
stimulation experimentation, the mesos successfully elicited motor effects. Significance.
Previous studies have compared macro- and microelectrodes for localizing seizure activity in
adjacent regions. The multi-scale design validated here is the first to simultaneously measure
macro- and microelectrode signals from the same overlapping cortical area. This enables direct
comparison of microelectrode recordings to the macroelectrode recordings used in standard
neurosurgical practice. Previous studies have also shown that cortical regions generating
high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More
accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients
Fredrik Kristoffer Mürer et al 2024 Biomed. Phys. Eng. Express
Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or "label-free" imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques with an emphasis on FPM and advanced X-ray microscopies. We next demonstrate with our own results computational imaging through Fourier ptychographic microscopy (FPM) and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are reported. X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are also presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.
Ma'Moun Abu-Ayyad et al 2024 Biomed. Phys. Eng. Express 10 045012
Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.
Sruthi Sivabhaskar et al 2024 Biomed. Phys. Eng. Express 10 045011
Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling's chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.