Physical and Engineering Sciences in Medicine最新文献

An automated scoring system for cleanliness assessment during video capsule endoscopy (VCE) is presently lacking. The present study focused on developing an approach to automatically assess the cleanliness in VCE frames as per the latest scoring i.e., Korea-Canada (KODA). Initially, an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score was developed to collect a multi-label image dataset of twenty-eight patient capsule videos. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated this dataset in a duplicate manner. The labels were saved automatically in real-time. Inter-rater and intra-rater reliability were checked. The developed dataset was then randomly split into train:validate:test ratio of 70:20:10 and 60:20:20. It was followed by a comprehensive benchmarking and evaluation of three multi-label classification tasks using ten machine learning and two deep learning algorithms. Reliability estimation was found to be overall good among the three readers. Overall, random forest classifier achieved the best evaluation metrics, followed by Adaboost, KNeighbours, and Gaussian naive bayes in the machine learning-based classification tasks. Deep learning algorithms outperformed the machine learning-based classification tasks for only VM labels. Thorough analysis indicates that the proposed approach has the potential to save time in cleanliness assessment and is user-friendly for research and clinical use. Further research is required for the improvement of intra-rater reliability of KODA, and the development of automated multi-task classification in this field.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF_50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

The accurate assessment of film results is highly dependent on the methodology and techniques used to process film. This study aims to compare the performance of EBT3 and EBT-XD film for SRS dosimetry using two different film processing methods. Experiments were performed in a solid water slab and an anthropomorphic head phantom. For each experiment, the net optical density of the film was calculated using two different methods; taking the background (initial) optical density from 1) an unirradiated film from the same film lot as the irradiated film (stock to stock (S-S) method), and 2) a scan of the same piece of film taken prior to irradiation (film to film (F-F) method). EBT3 and EBT-XD performed similarly across the suite of experiments when using the green channel only or with triple channel RGB dosimetry. The dosimetric performance of EBT-XD was improved across all colour channels by using an F-F method, particularly for the blue channel. In contrast, EBT3 performed similarly well regardless of the net optical density method used. Across 21 SRS treatment plans, the average per-pixel agreement between EBT3 and EBT-XD films, normalised to the 20 Gy prescription dose, was within 2% and 4% for the non-target (2-10 Gy) and target (> 10 Gy) regions, respectively, when using the F-F method. At doses relevant to SRS, EBT3 provides comparable dosimetric performance to EBT-XD. In addition, an S-S dosimetry method is suitable for EBT3 while an F-F method should be adopted if using EBT-XD.

This guideline has been prepared by the ACPSEM to provide a standardised quality assurance program to be used within General X-ray imaging environments. The guideline includes the responsibilities of various multidisciplinary team members within medical imaging facilities. It must be noted that the listed tests and testing frequencies are not intended to replace or become regulatory requirements. Implementing a quality assurance program as outlined in this position paper is there to ensure best practice for imaging facilities by providing a framework to establish and monitor correct equipment performance. The current document has been produced through an extensive review of current international practices and local experience within the Australasian healthcare environment. Due to the constant evolution of digital radiographic equipment, there is no current consensus in international quality assurance guidelines as they continue to be adapted and updated. This document describes the current state of the use of digital General X-ray equipment in the Australasian environment and provides recommendations of test procedures that may be best suited for the current medical imaging climate in Australasia. Due to the everchanging developments in the medical imaging environment and the ability of new technologies to perform more complex tasks it is believed that in the future this document will be further reviewed in the hopes of producing a more globally agreed upon standard quality assurance program. Any such adjustments that are deemed to be necessary to Version 1.0 of this document will be provided in electronic format on the ACPSEM website with a notification to all parties involved in the use of digital General X-ray equipment. This guideline does not provide detailed methodologies for all the quality control tests recommended as it is it is expected that the professionals implementing aspects of this quality assurance program have the working knowledge and access to appropriate resources to develop testing methodologies appropriate for their local imaging environment.

Workforce modelling for Radiation Oncology Medical Physicists (ROMPs) is evolving and challenging, prompting the development of the 2021 Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) ROMP Workforce (ARW) Model. In the exploration of this model at Sir Charles Gairdner Hospital, a comprehensive productivity exercise was conducted to obtain a detailed breakdown of ROMP time at a granular level. The results provide valuable insights into ROMP activities and enabled an evaluation of ARW Model calculations. The findings also capture the changing ROMP role as evidenced by an increasing involvement in consultation and advisory tasks with other professionals in the field. They also suggest that CyberKnife QA time requirements in the data utilised by the model may need to be revised. This study emphasises features inherent in the model, that need to be understood if the model is to be applied correctly.

This study compared twice-refocused spin-echo sequence (TRSE) and Stejskal-Tanner sequence (ST) to evaluate their respective effects on the image quality of magnetic resonance (MR) diffusion-weighted imaging in the presence of radiofrequency (RF) shielding effect of titanium mesh in cranioplasty. A 1.5-T MR scanner with a Head/Neck coil 20 channels and a phantom simulating the T2 and apparent diffusion coefficient (ADC) value of the human brain were used. Imaging was performed with and without titanium mesh placed on the phantom in TRSE and ST, and normalized absolute average deviation (NAAD), Dice similarity coefficient (DSC), and ADC values were calculated. The NAAD values were significantly lower for TRSE than for ST in the area below the titanium mesh, and the drop rates due to titanium mesh were 14.1% for TRSE and 9.8% for ST. The DSC values were significantly lower for TRSE than for ST. The ADC values were significantly higher for TRSE than for ST without titanium mesh. The ADC values showed no significant difference between TRSE and ST with titanium mesh. The ST had a lower RF shielding effect of titanium mesh than the TRSE.

Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the possibility that hyperspectral imaging could become a valuable tool in solving this problem. Hyperspectral images of 11 histologically diagnosed carcinomas (six basal cell carcinomas and five squamous cell carcinomas) were acquired prior clinical evaluation and surgical excision. The hyperspectral data were then analyzed using a newly developed method for delineating skin cancer tumor margins. This proposed method is based on a segmentation process of the hyperspectral images into regions with similar spectral and spatial features, followed by a machine learning-based data classification process resulting in the generation of classification maps illustrating tumor margins. The Spectral Angle Mapper classifier was used in the data classification process using approximately 37% of the segments as the training sample, the rest being used for testing. The receiver operating characteristic was used as the method for evaluating the performance of the proposed method and the area under the curve as a metric. The results revealed that the performance of the method was very good, with median AUC values of 0.8014 for SCCs, 0.8924 for BCCs, and 0.8930 for normal skin. With AUC values above 0.89 for all types of tissue, the method was considered to have performed very well. In conclusion, hyperspectral imaging can become an objective aid in the preoperative evaluation of carcinoma margins.

Electron beam dosimetry is sensitive to the surface contour of the patient. Over 10% difference between Treatment Planning System (TPS) and independent monitor-unit (IMU) calculations have been reported in the literature. Similar results were observed in our clinic between Radformation ClearCalc IMU and Eclipse TPS electron Monte Carlo (eMC) algorithm (v.16.1). This paper presents data measured under 3D printed spherical and cylindrical phantoms to validate the eMC algorithm in the presence of curved geometries. Measurements were performed with multiple detectors and compared to calculations made in Eclipse for the 6, 9 and 12 MeV electron energies. This data is used to create curvature correction factors (CCFs), defined as the ratio of the detector reading with the curved-surface phantom to a flat phantom at the same depth. The mean difference between the TPS calculated and measured CCFs using the NACP, Diode E, microSilicon, and microDiamond detectors were 1.3, 0.9, 0.7 and 0.7% respectively, with maximum differences of 4.5, 2.3, 1.9, and 1.8% respectively. Applying CCFs to previous failing patient IMU calculations improved agreement to the TPS. CCFs were implemented in our clinic for patient-specific IMU calculations with the assistance of a ESAPI script.

The purpose of this work was to determine percentage depth dose (PDD) curves for kilovoltage x-rays from the WOmed-T105 unit, with open-ended steel applicators and beam qualities ranging from 0.5 to 4.2 mm Al. Measurements were made with parallel plate chambers in a water phantom, with extrapolation based on a fifth order polynomial used to estimate the surface dose. Measurements were also made with parallel plate chambers in a plastic water phantom, with thin plastic sheets used to obtain detailed measurements at shallow depths (less than 1 mm). Monte Carlo simulations were performed using the EGSnrc package, with two different sources as input: a SpekPy simulation of the x-ray beam and a full simulation of the x-ray tube, treatment head and applicators. Results showed that all four methods (two measurements and two simulations) agreed within the measurement uncertainty at depths greater than 2 mm. At shallow depths, significant differences were noted. At depths less than 0.1 mm, the full Monte Carlo simulation and the solid water measurements showed a sharp spike in surface dose which is attributed to electron contamination, which was not seen in the SpekPy Monte Carlo simulation or the extrapolated water measurements. At depths between 0.1 mm and 2 mm, beyond the range of contaminant electrons, the extrapolated water measurements underestimate the dose by up to 13% compared to the full Monte Carlo simulation and the solid water measurements, attributed to fluorescent photons generated in the applicators. This work demonstrates that for open-ended applicators, measurement of depth doses in water with extrapolation of surface dose has the potential to significantly underestimate the dose at shallow depths between the surface and 2 mm, even after eliminating electron contamination from the beam.

The cervical vertebral maturation (CVM) method is essential to determine the timing of orthodontic and orthopedic treatment. In this paper, a target detection model called DC-YOLOv5 is proposed to achieve fully automated detection and staging of CVM. A total of 1800 cephalometric radiographs were labeled and categorized based on the CVM stages. We introduced a model named DC-YOLOv5, optimized for the specific characteristics of CVM based on YOLOv5. This optimization includes replacing the original bounding box regression loss calculation method with Wise-IOU to address the issue of mutual interference between vertical and horizontal losses in Complete-IOU (CIOU), which made model convergence challenging. We incorporated the Res-dcn-head module structure to enhance the focus on small target features, improving the model's sensitivity to subtle sample differences. Additionally, we introduced the Convolutional Block Attention Module (CBAM) dual-channel attention mechanism to enhance focus and understanding of critical features, thereby enhancing the accuracy and efficiency of target detection. Loss functions, precision, recall, mean average precision (mAP), and F1 scores were used as the main algorithm evaluation metrics to assess the performance of these models. Furthermore, we attempted to analyze regions important for model predictions using gradient Class Activation Mapping (CAM) techniques. The final F1 scores of the DC-YOLOv5 model for CVM identification were 0.993, 0.994 for mAp0.5 and 0.943 for mAp0.5:0.95, with faster convergence, more accurate and more robust detection than the other four models. The DC-YOLOv5 algorithm shows high accuracy and robustness in CVM identification, which provides strong support for fast and accurate CVM identification and has a positive effect on the development of medical field and clinical diagnosis.