Background Obesity results from an imbalance between energy intake and expenditure. In rodents and newborn humans, brown adipose tissue helps regulate energy expenditure by thermogenesis mediated by the expression of uncoupling protein 1 (UCP1), but brown adipose tissue has been considered to have no physiologic relevance in adult humans. Methods We analyzed 3640 consecutive 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomographic and computed tomographic (PET–CT) scans performed for various diagnostic reasons in 1972 patients for the presence of substantial depots of putative brown adipose tissue. Such depots were defined as collections of tissue that were more than 4 mm in diameter, had the density of adipose tissue according to CT, and had maximal standardized uptake values of 18F-FDG of at least 2.0 g per milliliter, indicating high metabolic activity. Clinical indexes were recorded and compared with those of date-matched controls. Immunostaining for UCP1 was performed on biopsy specimens from t...

/pdf/identification-and-importance-of-brown-adipose-tissue-in-550n982569.pdf

Identification and Importance of Brown Adipose Tissue in Adult Humans

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.

/pdf/recent-developments-in-geant4-3pgbpf1ko3.pdf

Recent developments in GEANT4

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020191145

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

The text consists of two sections, one for those studying or practicing diagnostic radiology, nuclear medicine and radiation oncology; the other for those engaged in the study or clinical practice of radiation oncology--a new chapter, on radiologic terrorism, is specifically for those in the radiation sciences who would manage exposed individuals in the event of a terrorist event. The 17 chapters in Section I represent a general introduction to radiation biology and a complete, self-contained course especially for residents in diagnostic radiology and nuclear medicine that follows the Syllabus in Radiation Biology of the RSNA. The 11 chapters in Section II address more in-depth topics in radiation oncology, such as cancer biology, retreatment after radiotherapy, chemotherapeutic agents and hyperthermia.

Radiobiology for the Radiologist

The FLUKA Code: Developments and Challenges for High Energy and Medical Applications

PET/CT Treatment Verification after Proton Therapy in Abdominopelvic Tumor Sites

Site-Specific Range Uncertainties Due to by Dose Calculation Algorithms for Proton Therapy

Dose-rate effects when estimating risks for second malignancies: in regard to Fontenot et al (Int J Radiat Oncol Biol Phys 2009;74:616-622).

Purpose: In proton dose calculation, clinically compatible speeds are now achieved with Monte Carlo codes (MC) that combine 1) adequate simplifications in the physics of transport and 2) the use of hardware architectures enabling massive parallel computing (like GPUs). However, the uncertainties related to the transport algorithms used in these codes must be kept minimal. Such algorithms can be checked with the so-called “Fano cavity test”. We implemented the test in two codes that run on specific hardware: gPMC on an nVidia GPU and MCsquare on an Intel Xeon Phi (60 cores). Methods: gPMC and MCsquare are designed for transporting protons in CT geometries. Both codes use the method of fictitious interaction to sample the step-length for each transport step. The considered geometry is a water cavity (2×2×0.2 cm{sup 3}, 0.001 g/cm{sup 3}) in a 10×10×50 cm{sup 3} water phantom (1 g/cm{sup 3}). CPE in the cavity is established by generating protons over the phantom volume with a uniform momentum (energy E) and a uniform intensity per unit mass I. Assuming no nuclear reactions and no generation of other secondaries, the computed cavity dose should equal IE, according to Fano's theorem. Both codes were tested for initial proton energies ofmore » 50, 100, and 200 MeV. Results: For all energies, gPMC and MCsquare are within 0.3 and 0.2 % of the theoretical value IE, respectively (0.1% standard deviation). Single-precision computations (instead of double) increased the error by about 0.1% in MCsquare. Conclusion: Despite the simplifications in the physics of transport, both gPMC and MCsquare successfully pass the Fano test. This ensures optimal accuracy of the codes for clinical applications within the uncertainties on the underlying physical models. It also opens the path to other applications of these codes, like the simulation of ion chamber response.« less

SU-E-T-180: Fano Cavity Test of Proton Transport in Monte Carlo Codes Running On GPU and Xeon Phi

Purpose: Monte Carlo (MC) simulation is considered the most accurate method for proton therapy dose calculations. To improve its efficiency, we have previously developed a MC proton dose calculation package, gPMC, on a graphics‐processing unit (GPU) platform. This abstract reports our recent progress towards developing a new version gPMC v2.0, which aims at improving its clinical usability and evaluating it using proton therapy treatment plans. Methods: gPMC v2.0 supports loading source particles from a phase space file. Source particles are first placed to different energy bins according to their energies. Simulation is conducted to transport particles in each bin sequentially. This enforces that particles transported at the same time have similar energies and hence similar computational thread lengths, ensuring computational efficiency in parallel processing. Realistic treatment geometry is supported. A multi‐dose‐counter technique is developed, where multiple dose counters are allocated. Each dose deposition event is randomly assigned to one of the counters. This alleviates memory‐writing conflict problem, when multiple threads attempts to deposit dose to the same voxel. The final dose distribution and statistical uncertainty are obtained based on results at those counters. gPMC v2.0 also supports scoring dose‐to‐water. We have tested the new version in head‐and‐neck patient cases. Results: Computation time is ∼5 seconds for a typical patient case, where 2.1 million protons in a phase space file are simulated. It takes additional ∼8 seconds to load the phase space file. The dose distribution agrees well with that from TOPAS/Geant4. Inside a region of interest defined by the 50% iso‐dose line, gamma passing rate is ∼98% (3mm/3%) and ∼95% (2mm/2%) compared with TOPAS/Geant4 Result. The average relative uncertainty is 1.1%. Conclusion: gPMC has achieved accuracy and efficiency well beyond what was previously accomplished. It supports the recalculation of proton therapy treatment plans, making it feasible for routine clinical applications. This work is supported in part by the University of California Lab Fees Research Program and in part by R01 CA140735

WE‐F‐105‐03: Development of GPMC V2.0, a GPU‐Based Monte Carlo Dose Calculation Package for Proton Radiotherapy

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