Intracavitary cervical brachytherapy delivers high doses of radiation to the target tissue and a portion of these doses will also hit the rectal organs due to their close proximity. Rectal dose can be evaluated from dosimetric parameters in the treatment planning system (TPS) and in vivo (IV) dose measurement. This study analyzed the correlation between IV rectal dose with selected volume and point dose parameters from TPS. A total of 48 insertions were performed and IV dose was measured using the commercial PTW 9112 semiconductor diode probe. In 18 of 48 insertions, a single MOSkin detector was attached on the probe surface at 50 mm from the tip. Four rectal dosimetric parameters were retrospectively collected from TPS; (a) PTW 9112 diode maximum reported dose (RPmax) and MOSkin detector, (b) minimum dose to 2 cc (D2cc), (c) ICRU reference point (ICRUr), and (d) maximum dose from additional points (Rmax). The IV doses from both detectors were analyzed for correlation with these dosimetric parameters. This study found a significantly high correlation between IV measured dose from RPmax (r = 0.916) and MOSkin (r = 0.959) with TPS planned dose. The correlation between measured RPmax with both D2cc and Rmax revealed high correlation of r > 0.7, whereas moderate correlation (r = 0.525) was observed with ICRUr. There was no significant correlation between MOSkin IV measured dose with D2cc, ICRUr and Rmax. The non-significant correlation between parameters was ascribable to differences in both detector position within patients, and dosimetric volume and point location determined on TPS, rather than detector uncertainties.
Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.
Transperineal ultrasound (TPUS) is an image-guided radiotherapy system used for tracking intrafraction prostate displacements in real time. The objectives of this study are to evaluate intrafraction prostate displacements and derive planning target volume (PTV) margins for prostate radiotherapy at our institution. The ultrasound (US) data of nine prostate cancer patients referred for VMAT radiotherapy was retrieved. Prior to beam on, patient position was set up with the US probe positioned transperineally with the aid of reference images (fused US and computed tomography images). In each fraction, prostate displacements in three directions [superior/inferior (SI), left/right (LR) and anterior/posterior (AP)] were recorded. PTV margins were determined using Van Herk's formula. To assess the prostate displacement time trend, continuous displacement data were plotted in 30-s intervals for eight minutes. The intrafraction prostate monitoring found a population mean setup error (Mp) of 0.8, 0.1, - 1.7 mm, a systematic error of (∑p) 0.7, 0.4, 0.9 mm and random error (σp) of 0.2, 0.1, 0.3 mm in SI, LR and AP directions, respectively. The PTV margin was found to be the largest in the AP direction at 2.5 mm compared with 1.9 mm and 1.1 mm for SI and LR directions, respectively. The PTV margin allowed for prostate radiotherapy at our institution was 2.5 mm in all directions. The prostate displacement time trend showed an increase in intrafraction displacements, with most patients were observed to have strong positive correlation between time and intrafraction prostate displacements in SI direction. TPUS is feasible for monitoring intrafraction displacement of the prostate and may facilitate PTV margin generation to account for such displacements during radiotherapy.
Deep inspiration breath-hold radiotherapy (DIBH-RT) reduces cardiac dose by over 50%. However, poor breath-hold reproducibility could result in target miss which compromises the treatment success. This study aimed to benchmark the accuracy of a Time-of-Flight (ToF) imaging system for monitoring breath-hold during DIBH-RT. The accuracy of an Argos P330 3D ToF camera (Bluetechnix, Austria) was evaluated for patient setup verification and intra-fraction monitoring among 13 DIBH-RT left breast cancer patients. The ToF imaging was performed simultaneously with in-room cone beam computed tomography (CBCT) and electronic portal imaging device (EPID) imaging systems during patient setup and treatment delivery, respectively. Patient surface depths (PSD) during setup were extracted from the ToF and the CBCT images during free breathing and DIBH using MATLAB (MathWorks, Natick, MA) and the chest surface displacement were compared. The mean difference ± standard deviation, correlation coefficient, and limit of agreement between the CBCT and ToF were 2.88 ± 5.89 mm, 0.92, and - 7.36, 1.60 mm, respectively. The breath-hold stability and reproducibility were estimated using the central lung depth extracted from the EPID images during treatment and compared with the PSD from the ToF. The average correlation between ToF and EPID was - 0.84. The average intra-field reproducibility for all the fields was within 2.70 mm. The average intra-fraction reproducibility and stability were 3.74 mm, and 0.80 mm, respectively. The study demonstrated the feasibility of using ToF camera for monitoring breath-hold during DIBH-RT and shows good breath-hold reproducibility and stability during the treatment delivery.
TMP is gradually becoming a fundamental element for quality assurance and control in ionizing and non-ionizing radiation imaging modalities as well as in the development of different techniques. This study aimed to evaluate and obtain polyvinyl chloride tissue mimicking material for dual-modality breast phantoms in mammography and ultrasound. Breast tissue equivalence was evaluated based on X-ray attenuation properties, speed of sound, attenuation, and acoustic impedance. There are six samples of PVC-plasticizer material with variations of PVC concentration and additives. The evaluation of X-ray attenuation was carried out using mammography from 23 to 35 kV, while the acoustic properties were assessed with mode A ultrasound and a transducer frequency of 5 MHz. A breast phantom was created from TMP material with tissue equivalence and was then evaluated using mammography as well as ultrasound to analyze its image quality. The results showed that samples A (PVC 5%, DOP 95%), B (PVC 7%, DOP 93%), C (PVC 10%, DOP 90%), E (PVC 7%, DOP 90%, graphite 3%), and F (PVC 7%, DOP 90%, silicone oil 3%) have the closest equivalent to the ACR breast phantom material with a different range of 0.01-1.39 in the 23-35 kV range. Based on the evaluation of the acoustic properties of ultrasound, A had high similarity to fat tissue with a difference of 0.03 (dB cm- 1 MHz- 1) and 0.07 (106 kg m- 2 s- 1), while B was close to the glandular tissue with a difference of 9.2 m s- 1. Multilayer breast phantom images' results showed gray levels in mammography and ultrasound modalities. Therefore, this study succeeded in establishing TMP material for mammography and ultrasound. It can also be used for simple quality assurance and control programs.
Neutrons can be generated in medical linear accelerators (Linac) due to the interaction of high-energy photons (> 10 MeV) with the components of the accelerator head. The generated photoneutrons may penetrate the treatment room if a suitable neutron shield is not used. This causes a biological risk to the patient and occupational workers. The use of appropriate materials in the barriers surrounding the bunker may be effective in preventing the transmission of neutrons from the treatment room to the outside. In addition, neutrons are present in the treatment room due to leakage in the Linac's head. This study aims to reduce the transmission of neutrons from the treatment room by using graphene/hexagonal boron nitride (h-BN) metamaterial as a neutron shielding material. MCNPX code was used to model three layers of graphene/h-BN metamaterial around the target and other components of the linac, and to investigate its effect on the photon spectrum and photoneutrons. Results indicate that the first layer of a graphene/h-BN metamaterial shield around the target improves photon spectrum quality at low energies, whereas the second and third layers have no significant effect. Regarding neutrons, three layers of the metamaterial results in a 50% reduction in the number of neutrons in the air within the treatment room.
The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.
In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.
Assessment of the prosthetic gait is an important clinical approach to evaluate the quality and functionality of the prescribed lower limb prosthesis as well as to monitor rehabilitation progresses following limb amputation. Limited access to quantitative assessment tools generally affects the repeatability and consistency of prosthetic gait assessments in clinical practice. The rapidly developing wearable technology industry provides an alternative to objectively quantify prosthetic gait in the unconstrained environment. This study employs a neural network-based model in estimating three-dimensional body segmental orientation of the lower limb amputees during gait. Using a wearable system with inertial sensors attached to the lower limb segments, thirteen individuals with lower limb amputation performed two-minute walk tests on a robotic foot and a passive foot. The proposed model replicates features of a complementary filter to estimate drift free three-dimensional orientation of the intact and prosthetic limbs. The results indicate minimal estimation biases and high correlation, validating the ability of the proposed model to reproduce the properties of a complementary filter while avoiding the drawbacks, most notably in the transverse plane due to gravitational acceleration and magnetic disturbance. Results of this study also demonstrates the capability of the well-trained model to accurately estimate segmental orientation, regardless of amputation level, in different types of locomotion task.
This study proposes and investigates the feasibility of the passive assistive device to assist agricultural harvesting task and reduce the Musculoskeletal Disorder (MSD) risk of harvesters using computational musculoskeletal modelling and simulations. Several passive assistive devices comprised of elastic exotendon, which acts in parallel with different back muscles (rectus abdominis, longissimus, and iliocostalis), were designed and modelled. These passive assistive devices were integrated individually into the musculoskeletal model to provide passive support for the harvesting task. The muscle activation, muscle force, and joint moment were computed with biomechanical simulations for unassisted and assisted motions. The simulation results demonstrated that passive assistive devices reduced muscle activation, muscle force, and joint moment, particularly when the devices were attached to the iliocostalis and rectus abdominis. It was also discovered that assisting the longissimus muscle can alleviate the workload by distributing a portion of it to the rectus abdominis. The findings in this study support the feasibility of adopting passive assistive devices to reduce the MSD risk of the harvesters during agricultural harvesting. These findings can provide valuable insights to the engineers and designers of physical assistive devices on which muscle(s) to assist during agricultural harvesting.
Chronic kidney disease is a leading public health problem worldwide. The global prevalence of chronic kidney disease is nearly five hundred million people, with almost one million deaths worldwide. Estimated glomerular filtration rate, imaging such as conventional ultrasound, and histopathological findings are necessary as each technique provides specific information which, when taken together, may help to detect and arrest the development of chronic kidney disease, besides managing its adverse outcomes. However, estimated glomerular filtration rate measurements are hampered by substantial error margins while conventional ultrasound involves subjective assessment. Although histopathological assessment is the best tool for evaluating the severity of the renal pathology, it may lead to renal insufficiency and haemorrhage if complications occurred. Ultrasound shear wave elastography, an emerging imaging that quantifies tissue stiffness non-invasively has gained interest recently. This method applies acoustic force pulses to generate shear wave within the tissue that propagate perpendicular to the main ultrasound beam. By measuring the speed of shear wave propagation, the tissue stiffness is estimated. This paper reviews the literature and presents our combined experience and knowledge in renal shear wave elastography research. It discusses and highlights the confounding factors on shear wave elastography, current and future possibilities in ultrasound renal imaging and is not limited to new sophisticated techniques.
Many studies have investigated the dielectric properties of human and animal tissues, particularly to differentiate between normal cells and tumors. However, these studies are invasive as tissue samples have to be excised to measure the properties. This study aims to investigate the dielectric properties of urine in relation to bladder cancer, which is safe and non-invasive to patients. 30 healthy subjects and 30 bladder cancer patients were recruited. Their urine samples were subjected to urinalysis and cytology assessment. A vector network analyzer was used to measure the dielectric constant (Ɛ') and loss factor (Ɛ″) at microwave frequencies of between 0.2 and 50 GHz at 25 °C, 30 °C and 37 °C. Significant differences in Ɛ' and Ɛ″ were observed between healthy subjects and patients, especially at frequencies of between 25 and 40 GHz at 25 °C. Bladder cancer patients had significant lower Ɛ' and higher Ɛ″ compared with healthy subjects. The Ɛ' was negatively correlated with urinary exfoliated urothelial cell number, and Ɛ″ was positively correlated. The study achieved a receiver operating characteristic area under curve (ROC-AUC) score of 0.69099 and an optimum accuracy of 75% with a sensitivity of 80% and a specificity of 70%. The number of exfoliated urothelial cell had significant effect on the dielectric properties, especially in bladder cancer patients. Urinary dielectric properties could potentially be used as a tool to detect bladder cancer.
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.
Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks. The proposed PE-YNet model is tested with two public datasets, achieving a maximum mean detection and segmentation accuracy of 99.89% and 99.83%, respectively, on the CAD-PE challenge dataset. Similarly, it also achieves a detection accuracy of 99.75% and a segmentation accuracy of 99.81% on the FUMPE dataset. Additionally, sensitivity analysis also shows a high sensitivity of 0.9885 for the localization error ɛ = 0 for the CAD-PE dataset, demonstrating the model's robustness against false predictions compared to state-of-the-art models. Further, this model also exhibits lower inference time, size, and memory usage compared to representative models. An automated PE-YNet tool can assist physicians with PE diagnosis, treatment, and prognosis monitoring in the clinical management of CoVID-19.
Modern radiotherapy techniques have advanced and become more sophisticated. End-to-end 3D verification of the complex radiotherapy dose distribution in an anthropomorphic phantom can ensure the accuracy of the treatment delivery. The phantoms commonly used for dosimetry are homogeneous solid water phantom which lacks the capability to measure the 3D dose distribution for heterogeneous tissues necessary for advanced radiotherapy techniques. Therefore, we developed an end-to-end 3D radiotherapy dose verification system based on MAX-HD anthropomorphic phantom (Integrated Medical Technologies Inc., Troy, New York) with bespoke intracranial insert for PRESAGE® dosimeter. In this study, several advanced radiotherapy treatment techniques of various levels of complexity; 3D-CRT, IMRT and VMAT treatment, were planned for a 20 mm diameter of a spherical target in the brain region and delivered to the phantom. The dosimeters were read out using an in-house developed optical computed tomography (OCT) imaging system known as 3DmicroHD-OCT. It was found that the measured dose distribution of the PRESAGE® when compared with the measured dose distribution of EBT film and Monaco TPS has a maximum difference of less than 3% for 3D-CRT, IMRT and VMAT treatment plans. The gamma analysis results of PRESAGE® in comparison to EBT film and Monaco TPS show pass rates of more than 95% for the criteria of 3% dose difference and 3 mm distance-to-agreement. This study proves the capability of PRESAGE® and bespoke MAX-HD phantom in conjunction with the 3DmicroHD-OCT system to measure 3D dose distribution for end-to-end dosimetry verification.
This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.