Methods: A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells.
Results: The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times.
Conclusions: A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.
MATERIAL AND METHODS: The RFA of a spherical tumor of 2.0 cm diameter along with 0.5 cm clinical safety margin was simulated using Finite Element Analysis software. A total of 86 points inside one-eighth of the tumor volume along the axial, sagittal and coronal planes were selected as the target sites for electrode-tip placement. The angle of the electrode insertion in both craniocaudal and orbital planes ranged from -90° to +90° with 30° increment. The RFA electrode was simulated to pass through the target site at different angles in combination of both craniocaudal and orbital planes before being advanced to the edge of the tumor.
RESULTS: Complete tumor ablation was observed whenever the electrode-tip penetrated through the epicenter of the tumor regardless of the angles of electrode insertion in both craniocaudal and orbital planes. Complete tumor ablation can also be achieved by placing the electrode-tip at several optimal sites and angles.
CONCLUSIONS: Identification of the tumor epicenter on the central slice of the axial images is essential to enhance the success rate of complete tumor ablation during RFA procedures.
PURPOSE: The purpose of this study is twofold. First, it aimed to measure the renal length and calculate the renal volume of normal Thai children using 2-dimensional ultrasonography (2D-US) and study their correlations with somatic parameters. Second, it aimed to compare the age-specific renal size of normal Thai children with the published data of their Western and Chinese counterparts.
METHODS: A total of 321 children (150 boys, 171 girls; age, 6-15 years) with a normal renal profile were prospectively recruited. All subjects underwent 2D-US by an experienced pediatric radiologist and the renal length, width, and depth were measured. Renal volume was calculated using the ellipsoid formula as recommended. The data were compared between the left and right kidneys, the sexes, and various somatic parameters. The age-specific renal lengths were compared using a nomogram derived from a Western cohort that is currently referred by many Thailand hospitals, while the renal volumes were compared with the published data of a Chinese cohort.
RESULTS: No statistically significant difference (P<0.05) was found between sexes or the right and left kidneys. The renal sizes had strong correlations with height, weight, body surface area, and age but not with body mass index. The renal length of the Thai children was moderately correlated (r=0.59) with that of the Western cohort, while the age-specific renal volume was significantly smaller (P<0.05) than that of the Chinese children.
CONCLUSION: Therefore, we concluded that the age-specific renal length and volume obtained by 2D-US would vary between children in different regions and may not be suitably used as an international standard for diagnosis, although further studies may be needed to confirm our findings.
METHODS: Negatively charged acrylic microspheres were labeled with 152Sm ions through electrostatic interactions. In another formulation, the Sm-labeled microsphere was treated with sodium carbonate solution to form the insoluble 152Sm carbonate (152SmC) salt within the porous structures of the microspheres. Both formulations were neutron-activated in a research reactor. Physicochemical characterization, gamma spectrometry, and radiolabel stability tests were carried out to study the performance and stability of the microspheres.
RESULTS: The Sm- and SmC-labeled microspheres remained spherical and smooth, with a mean size of 35 µm before and after neutron activation. Fourier transform infrared (FTIR) spectroscopy indicated that the functional groups of the microspheres remained unaffected after neutron activation. The 153Sm- and 153SmC-labeled microspheres achieved activity of 2.53 ± 0.08 and 2.40 ± 0.13 GBq·g-1, respectively, immediate after 6 h neutron activation in the neutron flux of 2.0 × 1012 n·cm-2·s-1. Energy-dispersive X-ray (EDX) and gamma spectrometry showed that no elemental and radioactive impurities were present in the microspheres after neutron activation. The retention efficiency of 153Sm in the 153SmC-labeled microspheres was excellent (~99% in distilled water and saline; ~97% in human blood plasma), which was higher than the 153Sm-labeled microspheres (~95% and ~85%, respectively).
CONCLUSION: 153SmC-labeled microspheres have demonstrated excellent properties for potential application as theranostic agents for hepatic radioembolization.
MATERIAL AND METHOD: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.
RESULTS: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.
CONCLUSIONS: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.