Journal of Medical Physics最新文献

Background: The present study is aimed at calculating relative absorbed-dose energy response correction (R) of commonly used thermoluminescent dosimeters (TLDs) such as LiF, Li₂B₄O₇, and Al₂O₃ as a function of depth in water for protons (50-250 MeV/n) and carbon ion (80-480 MeV/n) beams using Monte Carlo-based FLUKA code.

Materials and methods: On-axis depth-dose profiles in water are calculated for protons (50-250 MeV/n) and carbon ion (80-480 MeV/n) beams using FLUKA code. For the calculation of R, selective depths are chosen based on the depth-dose profiles. In the simulations, the TLDs of dimensions 1 mm × 1 mm × 1 mm are positioned at the flat, dose gradient, and Bragg peak regions of the depth-dose profile. Absorbed dose to detector was calculated within the TLD material. In the second step, TLD voxels were replaced by water voxel of similar dimension and absorbed dose to water was scored.

Results: The study reveals that for both proton and carbon ion beams, the value of R differs from unity significantly at the Bragg peak position and is close to unity at the flat region for the investigated TLDs. The calculated R value is sensitive to depth in water, beam energy, type of ion beam, and type of TLD.

Discussion: For accurate dosimetry of protons and carbon ion beams using TLDs, the response of the TLD should be corrected to account for its absorbed-dose energy dependence.

Purpose: The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ _e).

Materials and methods: The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ _e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods.

Results: All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM.

Conclusion: The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

Aim: Cancer is a significant public health concern, and National Cancer Control Programs (NCCPs) are crucial for reducing its burden. However, assessing the progress of NCCPs is challenging due to the complexity of cancer control outcomes and the various factors that influence them. Composite indicators can provide a comprehensive and accurate assessment of NCCP progress.

Materials and methods: The dataset was compiled for 144 countries and comprised eight composite indices and two high-level comparative indicators (mortality-to-cancer incidence ratio [MIR] and 5-year cancer prevalence-to-incidence ratio [PCIR]) representing NCCP outcomes. Two large databases and six annual composite index reports were consulted. Linear regression analysis and Pearson correlation coefficients were used to establish a relationship between indicators and NCCP outcomes. A multiple regression machine learning model was generated to further improve the accuracy of NCCP outcome prediction.

Results: High-income countries had the highest cancer incidence, whereas low-income countries had the highest MIR. Linear regression analysis indicated a negative trend between all composite indicators and MIR, whereas a positive trend was observed with PCIR. The Human Development Index and the Legatum Prosperity Index had the highest adjusted R ² values for MIR (0.74 and 0.73) and PCIR (0.86 and 0.81), respectively. Multiple linear regression modeling was performed, and the results indicated a low mean squared error score (-0.02) and a high R ² score (0.86), suggesting that the model accurately predicts NCCP outcomes.

Conclusions: Overall, composite indicators can be an effective tool for evaluating NCCP, and the results of this study can aid in the development and keeping track of NCCP progress for better cancer control.

Aim: Agar-based phantoms are popular in high intensity focused ultrasound (HIFU) studies, with magnetic resonance imaging (MRI) preferred for guidance since it provides temperature monitoring by proton resonance frequency (PRF) shift magnetic resonance (MR) thermometry. MR thermometry monitoring depends on several factors, thus, herein, the PRF coefficient of agar phantoms was estimated.

Materials and methods: Seven phantoms were developed with varied agar (2, 4, or 6% w/v) or constant agar (6% w/v) and varied silica concentrations (2, 4, 6, or 8% w/v) to assess the effect of the concentration on the PRF coefficient. Each phantom was sonicated using varied acoustical power for a 30 s duration in both a laboratory setting and inside a 3T MRI scanner. PRF coefficients were estimated through linear trends between phase shift acquired using gradient sequences and thermocouple-based temperatures changes.

Results: Linear regression (R ² = 0.9707-0.9991) demonstrated a proportional dependency of phase shift with temperature change, resulting in PRF coefficients between -0.00336 ± 0.00029 and -0.00934 ± 0.00050 ppm/°C for the various phantom recipes. Weak negative linear correlations of the PRF coefficient were observed with increased agar. With silica concentrations, the negative linear correlation was strong. For all phantoms, calibrated PRF coefficients resulted in 1.01-3.01-fold higher temperature changes compared to the values calculated using a literature PRF coefficient.

Conclusions: Phantoms developed with a 6% w/v agar concentration and doped with 0%-8% w/v silica best resemble tissue PRF coefficients and should be preferred in HIFU studies. The estimated PRF coefficients can result in enhanced MR thermometry monitoring and evaluation of HIFU protocols.

Purpose: The metal present in the implant creates artifacts during the treatment simulation, which impacts the treatment planning and delivery of the prescribed dose to the target and sparing normal tissues. This retrospective study evaluated the uncertainties in the planning and delivery of doses for prosthesis cases with dedicated phantom.

Materials and methods: In this retrospective study, 11 patients with a hip prosthesis having cervix carcinoma were selected. Two treatment plans were generated on treatment planning system (TPS) for each case. Plan_No_Res was without any beam restriction, and Plan_exit_only was the plan with restricted beam entry through the metallic implant. An indigenous phantom was utilized to verify the accuracy of the treatment. In the phantom, some groves were present, which could be filled by implants that mimic the patient's geometries, like left, right and bilateral femur implants. The delivered doses were recorded using optically stimulated luminescence dosimeters (OSLDs), which were placed at different positions in the phantom. The plans were further calculated using megavoltage computed tomography (MVCT) scans acquired during treatment.

Results: The patient data showed no significant dose changes between the two planning methods. The treatment time increases from 412.18 ± 86.65 to 427.36 ± 104.80 with P = 0.03 for Plan_No_Res and Plan_exit_only, respectively. The difference between planned and delivered doses of various points across phantom geometries was within ± 9.5% in each case as left, right, and bilateral implant. The variations between OSLDs and MVCT calculated doses were also within ± 10.8%.

Conclusion: The study showed the competency of tomotherapy planning for hip prosthesis cases. The phantom measurements demonstrate the errors in dosimetry near the implant material, suggesting the need for precise methods to deal with artifact-related issues.

Aims: The dosimetric leaf gap (DLG) is a parameter for correcting radiation transmission through the round leaf end of multileaf collimators. The purpose of this study was to determine and investigate the optimal DLG correction factor for 6 MV volumetric-modulated arc radiotherapy (VMAT) plan dose calculations in Mobius3D.

Materials and methods: Seventeen VMAT plans were selected for the DLG correction factor optimization process. The optimal DLG correction factor was defined as the minimum difference between the measured dose and the Mobius3D-calculated dose on the Mobius Verification Phantom™ with different DLG correction factors. Subsequently, the optimal DLG correction factor was applied for Mobius3D dose calculation, and accuracy was assessed by comparing the measured and calculated doses. For verification and validation, the 17 previous plans and 10 newly selected plans underwent Mobius3D calculations with the optimal DLG correction factor, and gamma analysis was performed to compare them to the treatment planning system (TPS). Gamma analysis was also performed between the electronic portal imaging device (EPID) and the TPS for cross-comparison between systems.

Results: The DLG correction factor was optimized to -1.252, which reduced the average percentage differences between measured and Mobius3D-calculated doses from 2.23% ±1.21% to 0.03% ±1.82%. The cross-comparison between Mobius3D/TPS and EPID/TPS revealed a similar trend in gamma passing rate (>95%) in both the verification and validation plans.

Conclusion: The DLG correction factor strongly influences the accuracy of Mobius3D-calculated doses. Applying the optimal DLG correction factor can increase dose agreement and gamma passing rate between calculation and delivered doses of VMAT plans, which emphasizes the importance of optimizing this factor during the commissioning process.

Aim: To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt^® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems.

Materials and methods: TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt^® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results.

Results: The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt^® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt^® and Velocity™.

Conclusion: The DIR algorithms of SmartAdapt^® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.

Recent decades have witnessed transformative advances in radiation physics and computer technology, revolutionizing the precision of radiation therapy. The adoption of intricate treatment techniques such as three-dimensional conformal radiotherapy, intensity-modulated radiotherapy, volumetric-modulated arc therapy, and image-guided radiotherapy necessitates robust quality assurance (QA) programs. This study introduces the SunCHECK™ Machine (SCM), a web-based QA platform, presenting early results from its integration into a comprehensive QA program. linear accelerators (LINAC) demand QA programs to uphold machine characteristics within accepted tolerances. The increasing treatment complexity underscores the need for streamlined procedures. The selection of QA tools is vital, requiring efficiency, accuracy, and alignment with clinic needs, as per recommendations such as the AAPM task group 142 report. The materials and methods section details SCM implementation in various QA aspects, encompassing daily QA (DQA), imaging QA with Catphan, conventional output assessment with a water phantom, and LINAC isocenter verification through the Winston-Lutz test. Challenges in QA processes, such as manual data transcription and limited device integration, are highlighted. Early results demonstrate SCM's significant reduction in QA time, ensuring accuracy and efficiency. Its automation eliminates interobserver variation and human errors, contributing to time savings and near-immediate result publication. SCM's role in consolidating and storing DQA data within a single platform is emphasized, offering potential in resource optimization, especially in resource-limited settings. In conclusion, SCM shows promise for efficient and accurate mechanical and dosimetric QA in radiation therapy. The study underscores SCM's potential to address contemporary QA challenges, contributing to improved resource utilization without compromising quality and safety standards.

Aim: The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models.

Materials and methods: This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images.

Results: For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique.

Conclusion: The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.

Purpose: We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy.

Materials and methods: Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated.

Results: The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively.

Conclusions: It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.