Development of advanced geometric models and acceleration techniques for Monte Carlo simulation in Medical Physics

Els programes de simulació Monte Carlo de caràcter general s'utilitzen actualment en una gran varietat d'aplicacions.Tot i això, els models geomètrics implementats en la majoria de programes imposen certes limitacions a la forma dels objectes que es poden definir. Aquests models no són adequats per descriure les superfícies arbitràries que es troben en estructures anatòmiques o en certs aparells mèdics i, conseqüentment, algunes aplicacions que requereixen l'ús de models geomètrics molt detallats no poden ser acuradament estudiades amb aquests programes.L'objectiu d'aquesta tesi doctoral és el desenvolupament de models geomètrics i computacionals que facilitin la descripció dels objectes complexes que es troben en aplicacions de física mèdica. Concretament, dos nous programes de simulació Monte Carlo basats en PENELOPE han sigut desenvolupats. El primer programa, penEasy, utilitza un algoritme de caràcter general estructurat i inclou diversos models de fonts de radiació i detectors que permeten simular fàcilment un gran nombre d'aplicacions. Les noves rutines geomètriques utilitzades per aquest programa, penVox, extenen el model geomètric estàndard de PENELOPE, basat en superfícices quàdriques, per permetre la utilització d'objectes voxelitzats. Aquests objectes poden ser creats utilitzant la informació anatòmica obtinguda amb una tomografia computeritzada i, per tant, aquest model geomètric és útil per simular aplicacions que requereixen l'ús de l'anatomia de pacients reals (per exemple, la planificació radioterapèutica). El segon programa, penMesh, utilitza malles de triangles per definir la forma dels objectes simulats. Aquesta tècnica, que s'utilitza freqüentment en el camp del disseny per ordinador, permet representar superfícies arbitràries i és útil per simulacions que requereixen un gran detall en la descripció de la geometria, com per exemple l'obtenció d'imatges de raig x del cos humà.Per reduir els inconvenients causats pels llargs temps d'execució, els algoritmes implementats en els nous programes s'han accelerat utilitzant tècniques sofisticades, com per exemple una estructura octree. També s'ha desenvolupat un paquet de programari per a la paral·lelització de simulacions Monte Carlo, anomentat clonEasy, que redueix el temps real de càlcul de forma proporcional al nombre de processadors que s'utilitzen.Els programes de simulació que es presenten en aquesta tesi són gratuïts i de codi lliures. Aquests programes s'han provat en aplicacions realistes de física mèdica i s'han comparat amb altres programes i amb mesures experimentals.Per tant, actualment ja estan llestos per la seva distribució pública i per la seva aplicació a problemes reals.Monte Carlo simulation of radiation transport is currently applied in a large variety of areas. However, the geometric models implemented in most general-purpose codes impose limitations on the shape of the objects that can be defined. These models are not well suited to represent the free-form (i.e., arbitrary) shapes found in anatomic structures or complex medical devices. As a result, some clinical applications that require the use of highly detailed phantoms can not be properly addressed.This thesis is devoted to the development of advanced geometric models and accelration techniques that facilitate the use of state-of-the-art Monte Carlo simulation in medical physics applications involving detailed anatomical phantoms. To this end, two new codes, based on the PENELOPE package, have been developed. The first code, penEasy, implements a modular, general-purpose main program and provides various source models and tallies that can be readily used to simulate a wide spectrum of problems. Its associated geometry routines, penVox, extend the standard PENELOPE geometry, based on quadric surfaces, to allow the definition of voxelised phantoms. This kind of phantoms can be generated using the information provided by a computed tomography and, therefore, penVox is convenient for simulating problems that require the use of the anatomy of real patients (e.g., radiotherapy treatment planning). The second code, penMesh, utilises closed triangle meshes to define the boundary of each simulated object. This approach, which is frequently used in computer graphics and computer-aided design, makes it possible to represent arbitrary surfaces and it is suitable for simulations requiring a high anatomical detail (e.g., medical imaging).A set of software tools for the parallelisation of Monte Carlo simulations, clonEasy, has also been developed. These tools can reduce the simulation time by a factor that is roughly proportional to the number of processors available and, therefore, facilitate the study of complex settings that may require unaffordable execution times in a sequential simulation.The computer codes presented in this thesis have been tested in realistic medical physics applications and compared with other Monte Carlo codes and experimental data. Therefore, these codes are ready to be publicly distributed as free and open software and applied to real-life problems.Postprint (published version

High spatial resolution inorganic scintillator detector for high energy X-ray beam at small field irradiation

International audiencePurpose: Small fielddosimetry for radiotherapy is one of the major challenges due to the size of most dosimeters,e.g. sufficient spatial resolution, accurate dose distribution and energy dependency of the detector. In this context, the purpose of this research is to develop a small size scintillating detector targeting small field dosimetry and compare its performance with other commercial detectors. Method: An inorganic scintillator detector (ISD) of about 200 μm outer diameter was developed and tested through different small fields dosimetric characterization under high energy photons (6 MV and 15 MV) delivered by an Elekta Linear Accelerator (LINAC). PDD and beam profile measurements were compared using dosimeters from PTW namely, microdiamond and PinPoint 3D detector. A background fiber method has been considered to quantifyand eliminate the minimal Cerenkov effect from the total optical signal magnitude. Measurements were performed inside a water phantom under IAEA Technical reports series recommendations (IAEA TRS 381 and TRS 483). Results:Small fields ranging from 3 x 3 cm2, down to 0.5 x 0.5 cm2 were sequentiallymeasured using the ISD and commercial dosimeters, and a good agreement was obtained among all measurements. The result also shows that, scintillating detector has good repeatabilityand reproducibility of the output signal with maximum deviation of 0.26% and 0.5% respectively. The Full Width Half Maximum (FWHM) was measured 0.55 cm for the smallest available square size field of 0.5 x 0.5 cm2, where the discrepancy of 0.05 cm is dueto the scattering effects inside the water and convolution effect between field and detector geometries. Percentage Depth Dose (PDD) factor dependence variation with water depth exhibits nearly the same behavior for all tested detectors. The ISD allows to perform dose measurements at a very high accuracy from low (50 cGy/min) to high dose rates (800 cGy/min) and found to be independent of dose rate variation. The detection system also showed an excellent linearity with dose; hence calibration was easily achieved. Conclusions: The developed detector can be used to accurately measure the delivered dose at small field during the treatment of small volume tumors. The author’s measurement shows that despite using a non-water equivalent detector, the detector can be a powerful candidate for beam characterization and quality assurance in e.g., radiosurgery, Intensity Modulated Radiotherapy (IMRT), and brachytherapy. Our detector can provide real-time dose measurement and good spatial resolution with immediate readout, simplicity, flexibility, and robustness

Translational Research in Cancer

Translational research in oncology benefits from an abundance of knowledge resulting from genome-scale studies concerning the molecular pathways involved in tumorigenesis. Translational oncology represents a bridge between basic research and clinical practice in cancer medicine. The vast majority of cancer cases are due to environmental risk factors. Many of these environmental factors are controllable lifestyle choices. Experimental cancer treatments are studied in clinical trials to compare the proposed treatment to the best existing treatment through translational research. The key features of the book include: 1) New screening for the development of radioprotectors: radioprotection and anti-cancer effect of β-Glucan (Enterococcus faecalis) 2) Translational perspective on hepatocellular carcinoma 3) Brachytherapy for endometrial cancer 4) Discovery of small molecule inhibitors for histone methyltransferases in cance

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal gray zone cases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates good knowledge of statistics, CNS & eye, pediatrics, biology, and physics but has limitations in bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs well in diagnosis, prognosis, and toxicity but lacks proficiency in topics related to brachytherapy and dosimetry, as well as in-depth questions from clinical trials. For the gray zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Most importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Because of the risk of hallucination, facts provided by ChatGPT always need to be verified