Database Plan Quality Impact on Knowledge-based Radiation Therapy Treatment Planning of Prostate Cancer

Purpose: Knowledge-based planning (KBP) leverages plan data from a database of previously treated patients to inform the plan design of a new patient. This work investigated bladder and rectum dose-volume prediction improvements in a common KBP method using a Pareto plan database in VMAT planning for prostate cancer. Methods: We formed an anonymized retrospective patient database of 124 VMAT plans for prostate cancer treated at our institution. From these patient data, two plan databases were compiled. The clinical plan database (CPD) contained planning data from each patient’s clinical plan, which were manually optimized by various planners. The multi-criteria optimization database (MCOD) contained Pareto plan data from plans created using a standardized MCO protocol. Overlap volume histograms, incorporating fractional OAR volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D_10, D_30, D_50, D_65, and D_80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was verified through a re-planning study on a subset of 31 randomly selected database patients using the lowest KBP predictions, regardless of plan database origin, as planning goals. Results: MCOD model predictions were significantly lower (p \u3c 0.001) than CPD model predictions for all five bladder dose-volumes and rectum D_50 (p = 0.004) and D_65 (p \u3c 0.001), while CPD model predictions for rectum D_10 (p = 0.005) and D_30 (p \u3c 0.001) were significantly less than MCOD model predictions. KBP model predictions were statistically equivalent to re-planned values for all predicted dose-volumes, excluding D_10 of bladder (p = 0.03) and rectum (p = 0.04). Compared to clinical plans, re-plans showed significant average reductions in D_mean for bladder (7.8 Gy; p \u3c 0.001) and rectum (9.4 Gy; p \u3c 0.001), while maintaining statistically similar PTV, femoral head, and penile bulb dose. Conclusion: KBP dose-volume predictions derived from Pareto plans were lower overall than those resulting from manually optimized clinical plans. A re-planning study showed the KBP dose-volume predictions were achievable and led to significant reductions in bladder and rectum dose

An Investigation Of Uncertainties In Intensity Modulated Radiation Therapy

Introduction: The treatment of cancer using intensity modulated radiation therapy (IMRT) is complex, involving many sub-processes to commission a treatment platform and treat patients. Uncertainties within the individual sub-process can lead to inaccurate treatments and sub-optimal patient outcomes. This research focused on minimizing uncertainties throughout IMRT commissioning and treatments. Methods: Five sub-processes were selected for uncertainty reduction: 1) optimizing radiation-imaging coincidence of the treatment machine, 2) improving the statistical model used to analyze IMRT commissioning data, 3) reducing the effect of ion recombination in IMRT quality assurance measurements, 4) improving the correlation between IMRT quality assurance results and patient-specific delivery inaccuracies, 5) commissioning of cranial stereotactic radiosurgery treatments. Results: Uncertainty reduction was achieved in all five sub-processes. Although the effect of this work on any single patient’s treatment is difficult to quantify, it is expected that the improvement in the sub-process accuracy will have a beneficial effect on the overall IMRT treatment delivery accuracy. Conclusion: Reducing uncertainties is an important aspect of radiotherapy quality assurance and should continue to be investigated for current and future treatment techniques

Adaptive Radiation Therapy for Lung Cancer

Prognosis for lung cancer patients remains poor. For those receiving radiation therapy, local control and survival have been shown to improve with increased doses; however, deliverable dose is often limited by associated toxicity. Therefore, methods that reduce dose to normal tissues and allow isotoxic escalation are desirable. Adaptive radiation therapy seeks to improve treatment by modifying the initial plan throughout delivery, and has been shown to decrease normal tissue dose. Studies to date suggest a trend of increasing benefit with increases in replanning frequency; however, replanning is costly in terms of workload and past studies implement at most weekly adaptation. The purpose of this thesis is to quantify the benefit associated with daily replanning and characterize the tradeoff between replanning frequency and adaptive benefit. A software tool is developed to facilitate planning studies and to introduce complimentary methods for evaluating adaptive treatments. Synthetic images and contours are xii generated for each fraction of a typical fractionation schedule using principal component analysis and a novel method of sampling coefficients that preserves temporal trends in the data (e.g. tumor regression). Using the synthetic datasets, a series of adaptive schedules ranging from no adaption to daily replanning are simulated and compared to quantify adaptive benefits and characterize tradeoffs with frequency. Daily replanning resulted in significant reductions in all normal tissue planning metrics when compared to no adaptation, and incremental reductions were observed with each increase in replanning frequency while the magnitude of average reductions decreased with each step. Modest correlation between absolute change in planning target volume over the course of treatment and reductions in both mean lung dose and mean esophageal dose were observed

Automation of the Monte Carlo simulation of medical linear accelerators

La consulta íntegra de la tesi, inclosos els articles no comunicats públicament per drets d'autor, es pot realitzar prèvia petició a l'Arxiu de la UPCThe main result of this thesis is a software system, called PRIMO, which simulates clinical linear accelerators and the subsequent dose distributions using the Monte Carlo method. PRIMO has the following features: (i) it is self- contained, that is, it does not require additional software libraries or coding; (ii) it includes a geometry library with most Varian and Elekta linacs; (iii) it is based on the general-purpose Monte Carlo code PENELOPE; (iv) it provides a suite of variance-reduction techniques and distributed parallel computing to enhance the simulation efficiency; (v) it is graphical user interfaced; and (vi) it is freely distributed through the website http://www.primoproject.net In order to endow PRIMO with these features the following tasks were conducted: - PRIMO was conceived with a layered structure. The topmost layer, named the GLASS, was developed in this thesis. The GLASS implements the GUI, drives all the functions of the system and performs the analysis of results. Lower layers generate geometry files, provide input data and execute the Monte Carlo simulation. - The geometry of Elekta linacs from series SU and MLCi were coded in the PRIMO system. - A geometrical model of the Varian True Beam linear accelerator was developed and validated. This model was created to surmount the limitations of the Varian distributed phase-space files and the absence of released information about the actual geometry of that machine. This geometry model was incorporated into PRIMO. - Two new variance-reduction techniques, named splitting roulette and selective splitting, were developed and validated. In a test made with an Elekta linac it was found that when both techniques are used in conjunction the simulation efficiency improves by a factor of up to 45. - A method to automatically distribute the simulation among the available CPU cores of a computer was implemented. The following investigations were done using PRIMO as a research tool : - The configu ration of the condensed history transport algorithm for charged particles in PENELOPE was optimized for linac simulation. Dose distributions in the patient were found to be particularly sensitive to the values of the transport parameters in the linac target. Use of inadequate values of these parameters may lead to an incorrect determination of the initial beam configuration or to biased dose distributions. - PRIMO was used to simulate phase-space files distributed by Varian for the True Beam linac. The results were compared with experimental data provided by five European radiotherapycenters. It was concluded thatthe latent variance and the accuracy of the phase-space files were adequate for the routine clinical practice. However, for research purposes where low statistical uncertainties are required the phase-space files are not large enough. To the best of our knowledge PRIMO is the only fully Monte Carlo-based linac and dose simulation system , addressed to research and dose verification, that does not require coding tasks from end users and is publicly available.El principal resultado de esta tesis es un sistema informático llamado PRIMO el cual simula aceleradores lineales médicos y las subsecuentes distribuciones de dosis empleando el método de Monte Carlo. PRIMO tiene las siguiente características: (i) es auto contenido, o sea no requiere de librerías de código ni de programación adicional ; (ii) incluye las geometrías de los principales modelos de aceleradores Varían y Elekta; (iii) está basado en el código Monte Carlo de propósitos generales PENELOPE; (iv) contiene un conjunto de técnicas de reducción de varianza y computación paralela distribuida para mejorar la eficiencia de simulación; (v) tiene una interfaz gráfica de usuario; y (vi) se distribuye gratis en el sitio web http://vvww.primoproject.net. Para dotar a PRIMO de esas características, se realizaron las tareas siguientes: - PRIMO se concibió con una estructura de capas. La capa superior, nombrada GLASS, fue desarrollada en esta tesis. GLASS implementa la interfazgráfica de usuario, controla todas las funciones del sistema y realiza el análisis de resultados. Las capas inferiores generan los archivos de geometría y otros datos de entrada y ejecutan la simulación Monte Carlo. - Se codificó en el sistema PRIMO la geometría de los aceleradores Elekta de las series SLi y MLC. - Se desarrolló y validó un modelo geométrico del acelerador TrueBeam de Varian. Este modelo fue creado para superar las limitaciones de los archivos de espacio de fase distribuidos por Varian, así como la ausencia de información sobre la geometría real de esta máquina. Este modelo geométrico fue incorporado en PRIMO. - Fueron desarrolladas y validadas dos nuevas técnicas de reducción de varianza nombradas splitting roulette y selective splitting. En pruebas hechas en un acelerador Elekta se encontró que cuando ambas técnicas se usan en combinación, la eficiencia de simulación mejora 45 veces. - Se implementó un método para distribuir la simulación entre los procesadores disponibles en un ordenador. Las siguientes investigaciones fueron realizadas usando PRIMO como herramienta: - Fue optimizada la configuración del algoritmo de PENELOPE para el transporte de partículas cargadas con historia condensada en la simulación del linac. Se encontró que las distribuciones de dosis en el paciente son particularmente sensibles a los valores de los parámetros de transporte usados para el target del linac. El uso de va lores inadecuados para esos parámetros puede conducir a una incorrecta determinación de la configuración del haz inicial o producir sesgos en las distribuciones de dosis. - Se utilizó PRIMO para simular archivos de espacios de fase distribuidos por Varian para el linac TrueBeam. Los resultados se compararon con datos experimentales aportados por cinco centros de radioterapia europeos. Se concluyó que la varianza latente y la exactitud de estos espacios de fase son adecuadas para la práctica clínica de rutina. Sin embargo estos espacios de fase no son suficientemente grandes para emplearse en investigaciones que requieren alcanzar una baja incertidumbre estadística. Hasta donde conocemos, PRIMO es el único sistema Monte Carlo que simula completamente el acelerador lineal y calcula la dosis absorbida, dirigido a la investigación y la verificación de dosis que no requiere del usuario tareas de codificación y está disponible públicamentePostprint (published version

Linking Procedural Performance with Radiobiological Analysis in Radiation Therapy

Radiation therapy is a sophisticated complex process. Systematic methods are needed to quantitatively evaluate the quality of a complex process and hence radiation therapy treatments. An ideal result for a complex process must be established to determine if the complex process is completed acceptably. For radiation therapy, this can be accomplished through complication free tumor control probability functions. A theoretical mathematical function can be created from a treatment plan to represent its complication free tumor control probability quantitatively by exporting Dose Volume Histograms from treatment plans to create complication free tumor control probability functions. By creating a baseline treatment plan (or assuming an ideal course of treatment), a relative comparison can be performed to assess how variations in the tasks that contribute to the treatment plan affect the complication free tumor control probability. By creating a systematic method to outline how and when all tasks are performed, variations in complication free tumor control over the entire course of treatment can be established and analyzed as to their impact on the quality of treatment

Segmentation Of Intracranial Structures From Noncontrast Ct Images With Deep Learning

Presented in this work is an investigation of the application of artificially intelligent algorithms, namely deep learning, to generate segmentations for the application in functional avoidance radiotherapy treatment planning. Specific applications of deep learning for functional avoidance include generating hippocampus segmentations from computed tomography (CT) images and generating synthetic pulmonary perfusion images from four-dimensional CT (4DCT).A single institution dataset of 390 patients treated with Gamma Knife stereotactic radiosurgery was created. From these patients, the hippocampus was manually segmented on the high-resolution MR image and used for the development of the data processing methodology and model testing. It was determined that an attention-gated 3D residual network performed the best, with 80.2% of contours meeting the clinical trial acceptability criteria. After having determined the highest performing model architecture, the model was tested on data from the RTOG-0933 Phase II multi-institutional clinical trial for hippocampal avoidance whole brain radiotherapy. From the RTOG-0933 data, an institutional observer (IO) generated contours to compare the deep learning style and the style of the physicians participating in the phase II trial. The deep learning model performance was compared with contour comparison and radiotherapy treatment planning. Results showed that the deep learning contours generated plans comparable to the IO style, but differed significantly from the phase II contours, indicating further investigation is required before this technology can be apply clinically. Additionally, motivated by the observed deviation in contouring styles of the trial’s participating treating physicians, the utility of applying deep learning as a first-pass quality assurance measure was investigated. To simulate a central review, the IO contours were compared to the treating physician contours in attempt to identify unacceptable deviations. The deep learning model was found to have an AUC of 0.80 for left, 0.79 for right hippocampus, thus indicating the potential applications of deep learning as a first-pass quality assurance tool. The methods developed during the hippocampal segmentation task were then translated to the generation of synthetic pulmonary perfusion imaging for use in functional lung avoidance radiotherapy. A clinical data set of 58 pre- and post-radiotherapy SPECT perfusion studies (32 patients) with contemporaneous 4DCT studies were collected. From the data set, 50 studies were used to train a 3D-residual network, with a five-fold validation used to select the highest performing model instances (N=5). The highest performing instances were tested on a 5 patient (8 study) hold-out test set. From these predictions, 50th percentile contours of well-perfused lung were generated and compared to contours from the clinical SPECT perfusion images. On the test set the Spearman correlation coefficient was strong (0.70, IQR: 0.61-0.76) and the functional avoidance contours agreed well Dice of 0.803 (IQR: 0.750-0.810), average surface distance of 5.92 mm (IQR: 5.68-7.55) mm. This study indicates the potential applications of deep learning for the generation of synthetic pulmonary perfusion images but requires an expanded dataset for additional model testing

Maximising the mutual interoperability of an MRI scanner and a cancer therapy particle accelerator

The work described in this PhD thesis was undertaken as part of a much larger research project: The Australian MRI-Linac program. The goal of this program is to merge two existing medical technologies – an MRI scanner and a Linear Accelerator (Linac) – thereby creating an advanced form of cancer treatment incorporating cutting edge anatomical and physiological imaging techniques. An overview of the background information necessary to understand the work presented in this thesis is provided in chapters 1 (overview of radiotherapy) and 2 (overview of electromagnetism and accelerator physics). The work in the remainder of this thesis can be split into two distinct sections, corresponding to the two quite different (but ultimately related) projects I worked on throughout this thesis: modelling the impact of external magnetic fields on electron beam transport within the linear accelerator, and the implementation of patient rotation in radiotherapy. The former project is the focus of Chapters 3-6. In Chapter 3 a finite element model of a clinical gridded electron gun is developed based on 3D laser scanning and electrical measurements, and the sensitivity of this gun in magnetic fields characterised. The results complement the existing literature in showing that conventional linear accelerator components are very sensitive to external magnetic fields – in fact this gun is over twice as sensitive to axial magnetic fields than the less realistic models existing in the literature. A first order approach to overcoming this sensitivity is to use magnetic shielding – however magnetic shielding of the linear accelerator can negatively impact on the performance of the MRI scanner. This magnetic shielding problem is explored in Chapter 4, where the fundamental principles of passive magnetic shielding are explored, and magnetic shields are implemented for the two possible MRI-linac configurations (in-line and perpendicular) for the 1.0 Tesla MRI magnet used in the Australian MRI Linac program. The efficacy of the shielding and the impact on the MRI is quantified, with the conclusion that passive shielding could be successfully implemented to allow acceptable operation of the linac without overly degrading the magnet performance of the MRI scanner. An alternative approach to magnetic shielding which would not have any impact on the magnet is to redesign the linear accelerator such that it functions robustly in an MRI environment without the need for shielding. This approach is explored in chapter 5, where a novel electron accelerator concept based on an RF-electron gun configuration is detailed. It is shown via particle in cell simulations that such a design would be able to operate in a wide range of axial magnetic fields with minimal current loss. In chapter 6, an experimental beam line based on this concept was constructed at Stanford Linear Accelerator Center (SLAC). This project is ongoing but progress so far is described in Chapter 6. In the second part of this thesis, a completely different project is explored, patient rotation. Patient rotation would be very beneficial for MRI-Linac systems as it would eliminate the complicated engineering that is used in conventional systems to rotate the beam around the patient, and the MRI could be used to adapt in real time for the resultant anatomic deformation. Patient rotation would also minimise some of the sources of electromagnetic interference explored in chapters 3-7. The two major obstacles to patient rotation are (1) Page 11 patient tolerance to rotation, and (2) anatomical deformation due to rotation. To quantify patient rotation, a clinical study of 15 patients was carried out and is detailed in chapter 7. The results of this study suggest that patient tolerance to rotation may not be a major issue, although this result needs to be verified in larger patient cohorts. In chapter 8, the design and construction of an MRI-compatible patient rotation device is detailed. This device is the first of its kind, and will allow data on anatomic deformation under rotation to be collected, enabling strategies to adapt for this motion to be developed. Thus far, MRI compatibility has been assessed and a volunteer imaging study undertaken, in which pelvic images were acquired under rotation angles of 360⁰ every 45⁰. In summary: In chapters 3-5, the impact of magnetic fields on conventional accelerator components was quantified; and two independent approaches to compensating for these effects (magnetic shielding and bespoke accelerator design) were explored. In chapter 6, an experimental beam is constructed to verify and support the findings of chapter 6. In chapter 7, a clinical study was undertaken quantifying patient tolerance of slow, single arc rotation. Finally, in chapter 8 a unique medical device was designed, constructed and tested, and through this device MRI images of anatomical distortion under lying rotation were collected and quantified

Uniform framework for the objective assessment and optimisation of radiotherapy image quality

Image guidance has rapidly become central to current radiotherapy practice. A uniform framework is developed for evaluating image quality across all imaging modalities by modelling the ‘universal phantom’: breaking any phantom down into its constituent fundamental test objects and applying appropriate analysis techniques to these through the construction of an automated analysis tree. This is implemented practically through the new software package ‘IQWorks’ and is applicable to both radiotherapy and diagnostic imaging. For electronic portal imaging (EPI), excellent agreement was observed with two commercial solutions: the QC-3V phantom and PIPS Pro software (Standard Imaging) and EPID QC phantom and epidSoft software (PTW). However, PIPS Pro’s noise correction strategy appears unnecessary for all but the highest frequency modulation transfer function (MTF) point and its contrast to noise ratio (CNR) calculation is not as described. Serious flaws identified in epid- Soft included erroneous file handling leading to incorrect MTF and signal to noise ratio (SNR) results, and a sensitivity to phantom alignment resulting in overestimation of MTF points by up to 150% for alignment errors of only ±1 pixel. The ‘QEPI1’ is introduced as a new EPI performance phantom. Being a simple lead square with a central square hole it is inexpensive and straightforward to manufacture yet enables calculation of a wide range of performance metrics at multiple locations across the field of view. Measured MTF curves agree with those of traditional bar pattern phantoms to within the limits of experimental uncertainty. An intercomparison of the Varian aS1000 and aS500-II detectors demonstrated an improvement in MTF for the aS1000 of 50–100% over the clinically relevant range 0.4–1 cycles/mm, yet with a corresponding reduction in CNR by a factor of p 2. Both detectors therefore offer advantages for different clinical applications. Characterisation of cone-beam CT (CBCT) facilities on two Varian On-Board Imaging (OBI) units revealed that only two out of six clinical modes had been calibrated by default, leading to errors of the order of 400 HU for some modes and materials – well outside the ±40 HU tolerance. Following calibration, all curves agreed sufficiently for dose calculation accuracy within 2%. CNR and MTF experiments demonstrated that a boost in MTF f50 of 20–30% is achievable by using a 5122 rather than a 3842 matrix, but with a reduction in CNR of the order of 30%. The MTF f50 of the single-pulse half-resolution radiographic mode of the Varian PaxScan 4030CB detector was measured in the plane of the detector as 1.0±0.1 cycles/mm using both a traditional tungsten edge and the new QEPI1 phantom. For digitally reconstructed radiographs (DRRs), a reduction in CT slice thickness resulted in an expected improvement in MTF in the patient scanning direction but a deterioration in the orthogonal direction, with the optimum slice thickness being 1–2 mm. Two general purposes display devices were calibrated against the DICOM Greyscale Standard Display Function (GSDF) to within the ±20% limit for Class 2 review devices. By providing an approach to image quality evaluation that is uniform across all radiotherapy imaging modalities this work enables consistent end-to-end optimisation of this fundamental part of the radiotherapy process, thereby supporting enhanced use of image-guidance at all relevant stages of radiotherapy and better supporting the clinical decisions based on it