Physical and Engineering Sciences in Medicine最新文献

The aim of this study was to evaluate the point doses using a distribution of the size-specific dose estimate (SSDE) from axial CT images of in-house phantoms having diameters from 8 to 40 cm. In-house phantoms made of polyester-resin (PESR) mixed with methyl ethyl ketone peroxide (MEKP) were used. The phantoms were built with different diameter sizes of 8, 16, 24, 32, and 40 cm. The phantoms were scanned by Siemens a SOMATOM Perspective-128 slice CT scanner with constant input parameters. The point doses were interpolated from the central SSDE (SSDEc) and the peripheral SSDE (SSDEp). The SSDEc and SSDEp were calculated from the SSDE with h- and k-factors. The point doses were compared to the direct measurements using the nanoDot™ optically-stimulated luminescence dosimeter (OSLD) in dedicated holes on the phantoms. It was found that the point dose decreases as the phantom diameter increased. The doses obtained using two approaches differed by 11% on average. The highest difference was 40% and the lowest difference was < 1%. It was found that dose based on the SSDE concept tended to be higher compared to the measured dose by OSLD. Point dose estimation using the concept of SSDE distribution can be considered an alternative for accurate and simple estimation. This approach still requires improvements to increase its accuracy and its application to estimate the organ dose needs further investigation.

Photoplethysmography, a widely embraced tool for non-invasive blood pressure (BP) monitoring, has demonstrated potential in BP prediction, especially when machine learning techniques are involved. However, predictions with a singular model often fall short in terms of accuracy. In order to counter this issue, we propose an innovative ensemble model that utilizes Light Gradient Boosting Machine (LightGBM) as the base estimator for predicting systolic and diastolic BP. This study included 115 women and 104 men, with experimental results indicating mean absolute errors of 5.63 mmHg and 9.36 mmHg for diastolic and systolic BP, in line with level B and C standards set by the British Hypertension Society. Additionally, our research confronts data imbalance in medical research which can detrimentally affect classification. Here we demonstrate an effective use for the Synthetic Minority Over-sampling Technique (SMOTE) with three nearest neighbors for handling moderate imbalanced datasets. The application of this method outperformed other methods in the field, achieving an F1 score of 81.6% and an AUC value of 0.895, emphasizing the potential value of SMOTE for addressing imbalanced datasets in medical research.

Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant risk of causing severe damage to healthy tissues. It underscores the critical need for precise and meticulous precision in GKRS. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improve planning efficiency and homogeneity, streamline clinical workflows, and reduce patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. To overcome these challenges, we have developed a "Cascaded-Deep-Supervised" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision and a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative assessments and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15% ± 1.36% (3 mm/3%, 10% threshold), surpassing the previous best performance by 2.53% using the 3D Dense U-Net model. When evaluated against more stringent criteria (2 mm/3%, 10% threshold, and 1 mm/3%, 10% threshold), the overall GPRs still achieved 96.53% ± 1.08% and 95.03% ± 1.18%. Furthermore, the average target coverage (TC) was 98.33% ± 1.16%, dose selectivity (DS) was 0.57 ± 0.10, gradient index (GI) was 2.69 ± 0.30, and homogeneity index (HI) was 1.79 ± 0.09. Compared to the 3D Dense U-Net, CDS-CNN predictions demonstrated a 3.5% improvement in TC, and CDS-CNN's dose prediction yielded better outcomes than the 3D Dense U-Net across all evaluation criteria. The experimental results demonstrated that the proposed CDS-CNN model outperformed other models in predicting GKRS dose distributions, with predictions closely matching the TPS doses.

Velocity-selective (VS) magnetization preparation has shown great promise for non-contrast-enhanced (NCE) magnetic resonance angiography (MRA) with the ability to generate positive angiographic contrast directly using a single 3D acquisition. However, existing VS-MRA methods have an issue of aliased saturation around a certain velocity, known as velocity field-of-view (vFOV), which can cause undesired signal loss in arteries. This study aimed to develop a new version of the VS preparation pulse sequence that overcomes the aliased saturation problem in conventional VS preparation. Utilizing the fact that an excitation profile is the Fourier transform of excitation k-space sampling, we sampled the k-space in a non-uniform fashion by scaling gradient pulses accordingly to have aliased excitation diffused over velocity. The variable density sampling function was numerically optimized to maximize the average of the velocity passband signal while minimizing its variance. The optimized variable density VS magnetization was validated through Bloch simulations and applied to peripheral NCE MRA in healthy subjects. The in-vivo experiments showed that the proposed variable density VS-MRA significantly lowered arterial signal loss observed in conventional VS-MRA, as evidenced by a higher arterial signal-to-noise ratio (58.50 ± 14.29 vs. 55.54 ± 12.32; p < 0.05) and improved artery-to-background contrast-to-noise ratio (22.75 ± 7.57 vs. 20.60 ± 6.51; p < 0.05).

The stability of dosiomics features (DFs) and dose-volume histogram (DVH) parameters for detecting disparities in helical tomotherapy planned dose distributions was assessed. Treatment plans of 18 prostate patients were recalculated using the followings: field width (WF) (2.5 vs. 5), pitch factor (PF) (0.433 vs. 0.444), and modulation factor (MF) (2.5 vs. 3). From each of the eight plans per patient, ninety-three original and 744 wavelet-based DFs were extracted, using 3D-Slicer software, across six regions including: target volume (PTV), pelvic lymph nodes (PTV-LN), PTV + PTV-LN (PTV-All), one cm rind around PTV-All (PTV-Ring), rectum, and bladder. For the resulting DFs and DVH parameters, the coefficient of variation (CV) was calculated, and using hierarchical clustering, the features were classified into low/high variability. The significance of parameters on instability was analyzed by a three-way analysis of variance. All DF's were stable in PTV, PTV-LN, and PTV-Ring (average CV ( $\overline{\mathrm{CV}})$  ≤ 0.36). Only one feature in the bladder ( $\overline{\mathrm{CV}}$  = 0.9), rectum ( $\overline{\mathrm{CV}}$  = 0.4), and PTV-All ( $\overline{\mathrm{CV}}$  = 0.37) were considered unstable due to change in MF in the bladder and WF in the PTV-All. The value of $\overline{\mathrm{CV}}$ for the wavelet features was much higher than that for the original features. Out of 225 unstable wavelet features, 84 features had $\overline{\mathrm{CV}}$  ≥ 1. The CVs for all the DVHs remained very small ( $\overline{\mathrm{CV}}$ < 0.06). This study highlights that the sensitivity of DFs to changes in tomotherapy planning parameters is influenced by the region and the DFs, particularly wavelet features, surpassing the effectiveness of DVHs.

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

An improved Finite Element Model(FEM) is applied to compare the biomechanical stability of plates with three different options in the treatment of distal fibula fractures in this study. The Computed Tomography(CT) scan of the knee to ankle segment of a volunteer was performed. A 3D fibula FEM was reconstructed based on the CT data. Three different loads (uni-pedal standing, torsion, and twisting) were applied, the same as in the experiments in the literature. The stresses and strains of the three options were compared under the same loads, using a 4-hole locking plate (Option A), a 5-hole locking plate (Option B), and a 6-hole locking plate (Option C) in a standard plate for lateral internal fixation. The simulation results show that all three options showed a stress masking effect. Option C had the best overall biomechanical performance and could effectively distribute the transferred weight. This is because option C has greater torsional stiffness and better biomechanical stability than options A and B, and therefore, option C is the recommended internal fixation method for distal fibula fractures. The Finite Element Analysis(FEA) method developed in this work applies to the stress analysis of fracture treatment options in other body parts.

There is little evidence regarding radiation dose perturbation caused by the self-expandable metallic stents (SEMSs) used for transpapillary biliary decompression. We aimed to compare SEMSs with plastic stents (PSs) and clarify their dosimetric characteristics. Fifteen SEMSs (10 braided and 5 lasercut type) and six PSs (diameter: 2.3-3.3 mm) were inserted into a water-equivalent solid phantom. In total, 13 SEMSs had radiopaque markers, whereas the other two did not. Using radiochromic films, the dose difference adjacent to the stents at locations proximal, distal, and arc delivery to the radiation source was evaluated based on comparison to measurement of the dose delivery in phantom without any stent in place. The median values of the dose difference for each stent were used to compare the SEMS and PS groups.Results: The dose difference (median (minimum/maximum)) was as follows: proximal, SEMSs + 2.1% (1.8 / 4.7) / PSs + 5.4% (4.1 / 6.3) (p < 0.001); distal, SEMSs -1.0% (-1.6 /-0.4) / PSs -8.9% (-11.7 / -7.4) (p < 0.001); arc delivery, SEMSs 1.2% (0.9 / 2.3) / PSs 2.2% (1.6 / 3.6) (p = 0.005). These results demonstrated that the dose differences of SEMSs were significantly smaller than those of PSs. On the other hand, the dose difference was large at surface of the radiopaque markers for SEMSs: proximal, 10.3% (7.2 / 20.9); distal, -8.4% (-16.3 / -4.2); arc delivery, 5.5% (4.2 / 9.2). SEMSs for biliary decompression can be safely used in patients undergoing radiotherapy, by focusing on the dose distribution around the stents and by paying attention to local changes in the dose distribution of radiopaque markers.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1 ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.