Ultrasound imaging has been widely applied to screen fatty liver disease. Fatty liver disease is a condition where large vacuoles of triglyceride fat accumulate in liver cells, thereby altering the arrangement of scatterers and the corresponding backscattered statistics. In this study, we used ultrasound Nakagami imaging to explore the effects of fatty infiltration in human livers on the statistical distribution of backscattered signals. A total of 107 patients volunteered to participate in the experiments. The livers were scanned using a clinical ultrasound scanner to obtain the raw backscattered signals for ultrasound B-mode and Nakagami imaging. Clinical scores of fatty liver disease for each patient were determined according to a well-accepted sonographic scoring system. The results showed that the Nakagami image can visualize the local backscattering properties of liver tissues. The Nakagami parameter increased from 0.62 ± 0.11 to 1.02 ± 0.07 as the fatty liver disease stage increased from normal to severe, indicating that the backscattered statistics vary from pre-Rayleigh to Rayleigh distributions. A significant positive correlation (correlation coefficient ρ = 0.84; probability value (p value) < 0.0001) exists between the degree of fatty infiltration and the Nakagami parameter, suggesting that ultrasound Nakagami imaging has potentials in future applications in fatty liver disease diagnosis.
Aim: As the number of proton therapy facilities has steadily increased, the need for the tool to provide precise dose simulation for complicated clinical and research scenarios also increase. In this study, the treatment head of Mevion HYPERSCAN pencil beam scanning (PBS) proton therapy system including energy modulation system (EMS) and Adaptive Aperture™ (AA) was modelled using TOPAS (TOolkit for PArticle Simulation) Monte Carlo (MC) code and was validated during commissioning process. Materials and methods: The proton beam characteristics including integral depth doses (IDDs) of pristine Bragg peak and in-air beam spot sizes were simulated and compared with measured beam data. The lateral profiles, with and without AA, were also verified against calculation from treatment planning system (TPS). Results: All beam characteristics for IDDs and in-air spot size agreed well within 1 mm and 10% separately. The full width at half maximum and penumbra of lateral dose profile also agree well within 2 mm. Finding: The TOPAS MC simulation of the MEVION HYPERSCAN PBS proton therapy system has been modelled and validated; it could be a viable tool for research and verification of the proton treatment in the future.
Background: This study presents the Monte Carlo N-Particles Transport Code, Extension (MCNPX) simulation of proton dose distributions in a water phantom. Methods:In this study, fluence and dose distributions from an incident proton pencil beam were calculated as a function of depth in a water phantom. Moreover, lateral dose distributions were also studied to understand the deviation among different MC simulations and the pencil beam algorithm. MCNPX codes were used to model the transport and interactions of particles in the water phantom using its built-in "repeated structures" feature. Mesh Tally was used in which the track lengths were distributed in a defined cell and then converted into doses and fluences. Two different scenarios were studied including a proton equilibrium case and a proton disequilibrium case. Results:For the proton equilibrium case, proton fluence and dose in depths beyond the Bragg peak were slightly perturbed by the choice of the simulated particle types. The dose from secondary particles was about three orders smaller, but its simulation consumed significant computing time. This suggests that the simulation of secondary particles may only be necessary for radiation safety issues for proton therapy. For the proton disequilibrium case, the impacts of different multiple Coulomb scattering (MCS) models were studied. Depth dose distributions of a 70 MeV proton pencil beam in a water phantom obtained from MCNPX, Geometry and Track, version 4, and the pencil beam algorithm showed significant deviations between each other, because of different MCS models used. Conclusions: Careful modelling of MCS is necessary when proton disequilibrium exists.( Biomed J 2015;38:414-420)
Purpose To evaluate the impact of a digital whiteboard system integrated with data from the oncology information system (OIS) on the urgency of physics quality assurance (QA) tasks in the radiation oncology department. Methods Quality check list (QCL) items in the Mosaiq OIS corresponding to eight discrete, sequential steps in the treatment planning process were created. A whiteboard to graphically display active QCLs automatically and in real time was implemented in March 2020 using R shiny. QCL data with completion status were collected in two 12‐month time periods before and after whiteboard implementation: January 2019–December 2019 and July 2020–June 2021. For all plans requiring patient‐specific QA, we recorded when each plan was available for physics QA and which treatments started the following day. We further classified those plans into four categories (urgency levels 1–4 with 4 being the most urgent) depending on how much time was available to perform QA. We compared the proportion of these next‐day QAs in each category between time periods accounting for plan type, day of the week, and time of year. Results Overall QA numbers were similar between time periods with 797 and 765 QAs total. The total proportion of next‐day QA decreased by 27% and the proportions of urgency levels 1 and 4 both showed significant decreases after whiteboard implementation of 29.2% and 54.9%, respectively (p<0.05$p<0.05$). All plan types had reduced proportions of next‐day QAs, especially nonstereotactic body radiation therapy (non‐SBRT) (30.3% decrease, p<0.05$p < 0.05$). Fridays and the months of October–December had the highest proportion of next‐day QAs but showed significant reductions of 19.1% and 40.6% in the proportion of next‐day QAs, respectively (p<0.05$p<0.05$). Conclusions The integrated whiteboard system significantly reduced the proportion of last‐minute physics work, increasing patient safety. Advantages of the integrated whiteboard were low cost, low overhead with automatic interface to the OIS, and concurrent user support.
The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1 × 10 6 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.
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