Journal of Medical Physics最新文献

Aims: This study evaluated the influence of high dose (HD) vertex numbers and its placement on equivalent uniform dose (EUD) and peak-to-valley dose ratio (PVDR) in lattice radiotherapy (LRT).

Settings and design: One hundred and eighty-eight RapidArc (RA) plans were created for a cohort of 15 patients.

Materials and methods: RA plans were created with zero to eight HD vertices to analyze their relationship with EUD. Eight lattices were systematically and optimally placed (by avoiding proximity to organs at risks [OARs]) to study the impact of vertex placement. Variations in PVDR were assessed using PVDR1 (mean dose to HD vertices by the difference of mean doses to planning target volume [PTV] and HD vertices) and PVDR2 (D10/D90 of PTV in composite plans) across 38 RA plans with HD vertex doses of 9 Gy, 12 Gy, 15 Gy, and 18 Gy. PVDR3 (product of PVDR1 and PVDR2) was evaluated for its variation with peak dose.

Statistical analysis used: Hypothesis testing between vertex placements was performed using a two-tailed Student's t-test.

Results: EUD values ranged from 32.88 Gy to 40.63 Gy. In addition, statistical analysis revealed significant associations (P = 0.0074) between the placement patterns of HD vertices, both in systematic and optimized arrangements. The PVDR and D10/D90 product values were 1.6, 1.8, 2.1, and 2.3 for peak doses of 9 Gy, 12 Gy, 15 Gy, and 18 Gy, respectively.

Conclusions: The addition of one HD vertex increased EUD, emphasizing the impact of individual vertex increments on outcomes. Systematic and optimized vertex placements enhance EUD, with optimized placement yielding better doses to PTV and OARs. PVDR3 offers superior dose reporting for LRT compared to PVDR1 and PVDR2.

Background: Despite extensive research on various brain diseases, a few studies have focused on radiomic feature distribution in healthy brain images. The present study applied a novel radiomic framework to investigate the robustness and baseline values of radiomic features in normal brain magnetic resonance imaging (MRIs) regions.

Materials and methods: Analyses were performed on T1 and T2 images including 276 normal brains and 14 healthy volunteers were scanned with three scanners using the same protocols. The images were divided into 1024 three-dimensional nonoverlap patches with the same pixel size. Seven patches located in the thalamus, putamen, hippocampus and brain stem were selected as volume of interest (VOI). Eighty-five radiomic features were generated. To investigate the variation of features across VOIs, the analysis of variance was performed and coefficient of variation (COV) and intraclass correlation coefficient (ICC) were explored to examine the features repeatability.

Results: Thalamus (right and left) and hippocampus (left) resulted in more stable features (COV ≤ 6%) in T1 and T2 images, respectively. The inter-scanner ICC analysis demonstrated the features of T2 sequences represented more repeatable results and the brain stem and thalamus (both T1 and T2) showed particularly high repeatability (higher ICC values). Robust results (ICC ≥ 0.9) were identified for energy and range features of the first order class and several textures features across different brain regions.

Conclusion: Our results indicated the baselines of the repeatable texture features in healthy brain structural MRI highlighting inter-scanner stability. According to the findings, MRI sequencing and VOI location impact feature robustness and should be considered in brain radiomic studies.

Aim: This article presents a novel approach to automate the segmentation of organ at risk (OAR) for high-dose-rate brachytherapy patients using three deep learning models combined with ensemble learning techniques. It aims to improve the accuracy and efficiency of segmentation.

Materials and methods: The dataset comprised computed tomography (CT) scans of 60 patients obtained from our own institutional image bank and 10 patients from the other institute, all in Digital Imaging and Communications in Medicine format. Experienced radiation oncologists manually segmented four OARs for each scan. Each scan was preprocessed and three models, Double U-Net (DUN), Bi-directional ConvLSTM U-Net (BCUN), and Transformer Networks (TN), were trained on reduced CT scans (240 × 240 × 128) due to memory limitations. Ensemble learning techniques were employed to enhance accuracy and segmentation metrics. Testing and validation were conducted on 12 patients from our institute (OID) and 10 patients from another institute (DID).

Results: For DID test dataset, using the ensemble learning technique combining Transformer Network (TN) and BCUN, i.e., TN + BCUN, the average Dice similarity coefficient (DSC) ranged from 0.992 to 0.998, and for DUN and BCUN (DUN + BCUN) combination, the average DSC ranged from 0.990 to 0.993, which reflecting high segmentation accuracy. The 95% Hausdorff distance (HD) ranged from 0.9 to 1.2 mm for TN + BCUN and 1.1 to 1.4 mm for DUN + BCUN, demonstrating precise segmentation boundaries.

Conclusion: The proposed method leverages the strengths of each network architecture. The DUN setup excels in sequential processing, the BCUN captures spatiotemporal dependencies, and transformer networks provide a robust understanding of global context. This combination enables efficient and accurate segmentation, surpassing human expert performance in both time and accuracy.

Background and aim: A wide range of dental materials have incorporated the concept of nanotechnology into their composition to enhance their physical and antimicrobial properties. In this pretext, silver nanoparticles (AgNPs) are among the most commonly used nanoparticles which are exceptionally noteworthy for their role in medical applications as an antibacterial agent. Another essential, desirable physical characteristic of all endodontic cements is their radiopacity, while in similar context, various radiopacifying agents such as bismuth oxide, barium sulfate, and even AgNPs have been incorporated in endodontic sealers to enhance their physical properties. The aim of the present study was to assess whether the incorporation of AgNPs and 10% bismuth oxide imparted the required radiopacity to the novel cement material (Nano CS) as per the requirement and standards laid by the International Organization for Standardization (ISO) guidelines and whether it complied with the ISO 6876:2001 specifications to achieve the necessary norms.

Materials and methods: The structural characteristics of the novel cement material (Nano CS) were observed using energy-dispersive X-ray analysis under a Zeiss Gemini 500 Field Emission Scanning Electron Microscope, while radiopacity of the test material (Nano CS) was assessed with the help of an aluminum (Al) step-wedge using a nondestructive testing method following ISO guidelines. The optical density of the test material (Nano CS) was tested with the specimens of mineral trioxide aggregate (MTA) as the standard cement material along with the specimens of enamel and dentin that were 1 mm thick, and Al of appropriate thickness with the desired and equivalent radiopacity.

Results: The findings of the present study suggested MTA to have higher radiopacity index equivalent to 4.56 ± 0.00 mm thickness of Al when compared to the test material (Nano CS) (2.78 ± 0.01 mm thickness of Al) and enamel (4.09 ± 0.01 mm thickness of Al) and dentin (2.01 ± 0.01 mm thickness of Al) specimens. Furthermore, the radiopacity index of test material (Nano CS) was found to be more when compared to dentin, though, less when compared to the enamel specimens with the results being statistically highly significant (P < 0.001).

Conclusion: The addition of nanosilver and bismuth oxide to the test material (Nano CS) imparted characteristic radiopacity, though the required specifications laid down by the ISO standards were not achieved. Increasing the concentration of the additives used might be considered to bring in the required radiopacity without having a significant impact on the physical and biological properties of the test material (Nano CS) intended to be used for endodontic applications.

Purpose: The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density ρ _e, effective atomic number (Z _eff), and mean excitation energy (I) using one simple robust model and design a machine learning algorithm that will lead to automation.

Methods: Empirical relationships between computed tomography (CT) number and SPR, ρ _e (Z _eff) and I were used to formulate a model that predicts all the four parameters using linear attenuation coefficients which can be converted to CT numbers. The results of these models were compared with the results of other existing models. Thirty-three ICRU human tissues were used as modeling data and 12 Gammex inserts as testing data for the machine learning algorithm designed. More ways of tissue classification were introduced to improve accuracy. In the examples, the dual energy methods were implemented using 80 kVp and 150 kVP/Sn.

Results: The proposed method gave modeling root mean square error (RMSE) near 1% at maximum for the case of SPR and ρ _e for both single and dual-energy CT approaches considered with modeling RMSE of 0.32% for ρ _e and 0.38% for SPR as modeling RMSE with room for improvement (this can be done by adjusting the model number of terms as well as the parameters). The method was able to achieve modeling RMSE of 1.11% for I and 1.66% for Z _{ef f}. The mean error for all the estimated quantities was near 0.00%. In most cases, the proposed method has lower testing RMSE and mean error compare to the other methods presented in the study.

Conclusion: The proposed method proves to be more flexible and robust among all presented methods since it has lower testing error in most cases and can be improved based on data using the machine learning algorithm. The algorithm can also improve estimation by adjusting the model as well as aid in automation and it's easy to implement.

Aim: This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images.

Materials and methods: National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values.

Results: The findings demonstrated that postfiltering significantly affected SUV measurements. The changes in postfiltering values can result in overestimation or underestimation of SUV values, highlighting the importance of carefully selecting appropriate filters. Increasing the matrix size improved SUVmax and SUVmean values, particularly for small-sized spheres. Smaller voxel reconstructions slightly reduced partial volume effects and partially enhanced SUV quantification.

Conclusions: Careful consideration of postfiltering values and matrix size selection can lead to better SUV quantification. These findings emphasize the need to optimize the reconstruction parameters to enhance the clinical utility of PET/CT in detecting and evaluating malignant lesions.

Aim: The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists' radiation exposure.

Materials and methods: This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist's dose.

Results: The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 µSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 µSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses.

Conclusion: This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.

The study aims to assess the fluence distribution and point dosage between two phantoms for patient-specific quality assurance on the Tomotherapy system. This was a retrospective study conducted on 15 patients who had radiation using the Helical Tomotherapy Machine (Radixact, Accuray Inc.). We used two phantoms to quantify the fluence produced by the treatment planning system (TPS) and recorded from the machine. The ArcCHECK (Sun-Nuclear) has 1386 diodes placed in a cylindrical configuration. The minimal resolution for this was 7 mm. The second was Delta⁴, supplied by ScandiDos. It has 1069 diode detectors arrayed in a crossed orthogonal configuration with a minimum resolution of 5 mm. All patient plans were transferred to these phantoms to validate the accuracy of treatment plan delivery. We used SunCHECK and ScandiDos Delta⁴ software to compare the fluence produced by the TPS with the fluence measured by the equipment. In ArcCHECK, we used an external ionization chamber, cc13 (IBA dosimetry), whereas in Delta⁴,we employed a central diode detector to quantify point dosage. The mean and standard deviation of the gamma pass percentage with ArcCHECK were 98.3 ± 0.8%, with an average point dose deviation of ± 0.94%. The mean and standard deviation of the gamma pass percentage using Delta⁴ was 99.1 ± 1.6%, while the average point dose deviation was ± 0.60%, both of which were well within the 3% tolerance employing the two phantoms.

Aim: Magnetic-mediated hyperthermia has emerged as a promising therapeutic approach for treating cancer. This technique employs the heat dissipated by the magnetic nanoparticles when subjected to an external varying magnetic field, to bring about localized hyperthermia in tumor tissues. Owing to their conducive and tuneable "physical, chemical, and magnetic" characteristics, cobalt ferrite (CoFe₂O₄) nanoparticles are recognized as emerging contenders. The aim of the present work was to enhance the magnetic characteristics and guarantee the efficacy of CoFe2 O4 nanoparticles in targeting and eliminating cancer cells.

Methods: CoFe₂O₄ nanoparticles were synthesized using the chemical co-precipitation route and underwent rigorous structural, morphological, and magnetic characterization techniques. The synthesized particles were then subjected to in vitro studies to evaluate their cytotoxicity and antimicrobial susceptibility.

Results: The characterization techniques confirmed the cubic structure, ferrite phase, and spherical and magnetic nature of CoFe₂O₄ nanoparticles. The zeta potential was found to be - 0.0048V (4.8 mV). Cytotoxicity analysis exhibited decreased cell viability with increasing concentrations of CoFe₂O₄ nanoparticles. Antimicrobial studies displayed good inhibiting properties.

Conclusion: The zeta potential of the synthesized CoFe₂O₄ nanoparticles was found to be higher than that of the breast cancer cells (MCF-7) which proves the synthesized drug to be effective. The in vitro studies also disclose the efficacy of the drug over cancer cells.