Volume 27 (2021): Issue 4 (December 2021)

Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.

Introduction: Based on the tumor’s growth potential and aggressiveness, glioma is most often classified into low or high-grade groups. Traditionally, tissue sampling is used to determine the glioma grade. The aim of this study is to evaluate the efficiency of the Laplacian Re-decomposition (LRD) medical image fusion algorithm for glioma grading by advanced magnetic resonance imaging (MRI) images and introduce the best image combination for glioma grading.

Material and methods: Sixty-one patients (17 low-grade and 44 high-grade) underwent Susceptibility-weighted image (SWI), apparent diffusion coefficient (ADC) map, and Fluid attenuated inversion recovery (FLAIR) MRI imaging. To fuse different MRI image, LRD medical image fusion algorithm was used. To evaluate the effectiveness of LRD in the classification of glioma grade, we compared the parameters of the receiver operating characteristic curve (ROC).

Results: The average Relative Signal Contrast (RSC) of SWI and ADC maps in high-grade glioma are significantly lower than RSCs in low-grade glioma. No significant difference was detected between low and high-grade glioma on FLAIR images. In our study, the area under the curve (AUC) for low and high-grade glioma differentiation on SWI and ADC maps were calculated at 0.871 and 0.833, respectively.

Conclusions: By fusing SWI and ADC map with LRD medical image fusion algorithm, we can increase AUC for low and high-grade glioma separation to 0.978. Our work has led us to conclude that, by fusing SWI and ADC map with LRD medical image fusion algorithm, we reach the highest diagnostic accuracy for low and high-grade glioma differentiation and we can use LRD medical fusion algorithm for glioma grading.

Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications.

Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods.

Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume.

Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.

Introduction: The present study aimed to investigate the radiation protection properties of silicon-based composites doped with nano-sized Bi₂O₃, PbO, Sm₂O₃, Gd₂O₃, WO_3, and IrO₂ particles. Radiation shielding properties of Sm₂O₃ and IrO₂ nanoparticles were investigated for the first time in the current study.

Material and methods: The MCNPX (2.7.0) Monte Carlo code was utilized to calculate the linear attenuation coefficients of single and multi-nano structured composites over the X-ray energy range of 10–140 keV. Homogenous distribution of spherical nanoparticles with a diameter of 100 nm in a silicon rubber matrix was simulated. The narrow beam geometry was used to calculate the photon flux after attenuation by designed nanocomposites.

Results: Based on results obtained for single nanoparticle composites, three combinations of different nano-sized fillers Sm₂O₃+WO₃+Bi₂O_3, Gd₂O₃+WO₃+Bi₂O₃, and Sm₂O₃+WO₃+PbO were selected, and their shielding properties were estimated. In the energy range of 20-60 keV Sm₂O₃ and Gd₂O₃ nanoparticles, in 70-100 keV energy range WO₃ and for photons energy higher than 90 keV, PbO and Bi₂O₃ nanoparticles showed higher attenuation. Despite its higher density, IrO₂ had lower attenuation compared to other nanocomposites. The results showed that the nanocomposite containing Sm₂O_3, WO₃, and Bi₂O₃ nanoparticles provided better shielding among the studied samples.

Conclusions: All studied multi-nanoparticle nanocomposites provided optimum shielding properties and almost 8% higher attenuation relative to single nano-based composites over a wide range of photon energy used in diagnostic radiology. Application of these new composites is recommended in radiation protection. Further experimental studies are suggested to validate our findings.

Introduction: Recent studies have shown that the use of high-density nanoparticles (NPs) in concrete composition improves its radiation shielding properties. In the present study, the linear attenuation coefficients and photon scattering properties of newly developed high-density Nano-concretes have been calculated using the MCNPX Monte Carlo code.

Material and methods: The shielding properties of Nano-concretes containing 10%, 20%, and 30% weight percentage of Osmium, Iridium and Barite NPs (100 nm) as well as ordinary concrete were investigated. The 6 and 18 MV photon beams of Varian Linac and ⁶⁰Co photons were used for simulation. Photon scattering flux was calculated for all Nano-concretes with 30 wt% of NPs and ordinary concrete at different angles.

Results: In general, by adding Iridium, Osmium and Barite NPs to ordinary concrete, the linear attenuation coefficients increased. Despite a lower density relative to Iridium and Osmium, Nano-concretes containing Barite exhibited a higher linear attenuation coefficient due to their higher electron density.

Conclusions: The results revealed a dependence between the scattered photon flux and the effective atomic number of Nano-concretes. With increasing the atomic number of fillers, the intensity of the scattered photon flux enlarged. Also, the scattered flux was higher for all types of concretes at 180 degrees relative to other angles.

Purpose: This study was conducted to demonstrate the feasibility of X-ray output constancy quality assurance (QA) of a linear accelerator for various gantry angles using the Stealth Chamber.

Methods: The X-ray output constancy of a Varian TrueBeam STx was evaluated under various gantry angles and a 10 × 10 cm² field size using a Stealth Chamber. Specifically, 10X and 10X-flattening-filter-free beams with dose rates of 600 and 2400 monitor units (MU)/min, respectively, were used. The Stealth Chamber was attached to the gantry head, and irradiation was performed every 45° for gantry angles of 0-315°. To evaluate the variations in the output constancy with respect to the gantry angle, the acquired values were normalized to the value corresponding to a 0° gantry angle. The obtained results were utilized to determine the correction factors for all gantry angles. To verify the correction factors, additional measurements were performed for five days.

Results: The maximum variation in the output constancy measurement relative to the output constancy at a 0° gantry angle was found to be approximately 4.0% for both energy beams at a gantry angle of 180°. Furthermore, the measured values were dependent on the gantry angle. Upon applying the correction factor, the variation in the output constancy with respect to the gantry angle was less than 0.5%.

Conclusions: Output constancy QA using the Stealth Chamber for various gantry angles was found to be feasible with the application of a correction factor.

Introduction: The accuracy of the cross-calibration procedure depends on ionization chamber type, both used as reference one and under consideration. Also, the beam energy and phantom medium could influence the precision of cross calibration coefficient, resulting in a systematic error in dose estimation and thus could influence the linac beam output checking. This will result in a systematic mismatch between dose calculated in treatment planning system and delivered to the patient.

Material and methods: The usage of FC65-G, CC13 and CC01 thimble reference chambers as well as 6, 9, and 15 MeV electron beams has been analyzed. A plane-parallel PPC05 chamber was calibrated since scarce literature data are available for this dosimeter type. The influence of measurement medium and an effective point of measurement (EPOM) on obtained results are also presented.

Results: Dose reconstruction precision of ~0.1% for PPC05 chamber could be obtained when cross-calibration is based on a thimble CC13 chamber. N_d,w,Qcross obtained in beam ≥ 9MeV gives 0.1 – 0.5% precision of dose reconstruction.

Without beam quality correction, 15 MeV N_d,w,Qcross is 10% lower than Co-60 N_d,w,0. Various EPOM shifts resulted in up to 0.6% discrepancies in N_d,w,Qcross values.

Conclusions: Ionization chamber with small active volume and tissue-equivalent materials supplies more accurate cross-calibration coefficients in the range of 6 – 15 MeV electron beams. In the case of 6 and 9 MeV beams, the exact position of an effective point of measurement is of minor importance. In-water cross-calibration coefficient can be used in a solid medium without loss of dose accuracy.

Introduction: This study presents an empirical method to model the electron beam percent depth dose curve (PDD) using the primary and tail functions in radiation therapy. The modeling parameters N and n can be used to derive the depth relative stopping power of the electron energy in radiation therapy.

Methods and Materials: The electrons PDD curves were modeled with the primary-tail function in this study. The primary function included exponential function and main parameters of N, µ while the tail function was composed by a sigmoid function with the main parameter of n. The PDD for five electron energies were modeled by the primary and tail function by adjusting the parameters of N, µ and n. The R₅₀ and R_p can be derived from the modeled straight line of 80% to 20% region of PDD. The same electron energy with different cone sizes was also modeled with the primary-tail function. The stopping power for different electron energies at different depths can also be derived from the parameters of N, µ and n. Percent ionization depth curve can then be derived from the percent depth dose by dividing its depth relevant stopping power for comparing with the original water phantom measurement.

Results: The main parameters N, n increase, but µ decreases in primary-tail function when electron energy increased. The relationship of parameters n, N and LN(-µ) with electron energy are n = 31.667 E₀ - 88, N = 0.9975 E₀ - 2.8535, LN(-µ) = -0.1355 E₀ - 6.0986, respectively. Stopping power of different electron energy can be derived from n and N with the equation: stopping power = (−0.042 ln N_E0 + 1.072)e^{(−n−_E0·5·10−5+0.0381·d)}, where d is the depth in water. Percent depth dose was derived from the percent reading curve by multiplying the stopping power relevant to the depth in water at certain electron energy.

Conclusion: The PDD of electrons at different energies and field sizes can be modeled with an empirical model to deal with the stopping power calculation. The primary-tail equation provides a uncomplicated solution than a pencil beam or other numerical algorism for investigators to research the behavior of electron beam in radiation therapy.