Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.Comment: 19 pages, 2 tables, and 5 figure

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

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

Compressed Sensing Based Reconstruction Algorithm for X-ray Dose Reduction in Synchrotron Source Micro Computed Tomography

Synchrotron computed tomography requires a large number of angular projections to reconstruct tomographic images with high resolution for detailed and accurate diagnosis. However, this exposes the specimen to a large amount of x-ray radiation. Furthermore, this increases scan time and, consequently, the likelihood of involuntary specimen movements. One approach for decreasing the total scan time and radiation dose is to reduce the number of projection views needed to reconstruct the images. However, the aliasing artifacts appearing in the image due to the reduced number of projection data, visibly degrade the image quality. According to the compressed sensing theory, a signal can be accurately reconstructed from highly undersampled data by solving an optimization problem, provided that the signal can be sparsely represented in a predefined transform domain. Therefore, this thesis is mainly concerned with designing compressed sensing-based reconstruction algorithms to suppress aliasing artifacts while preserving spatial resolution in the resulting reconstructed image. First, the reduced-view synchrotron computed tomography reconstruction is formulated as a total variation regularized compressed sensing problem. The Douglas-Rachford Splitting and the randomized Kaczmarz methods are utilized to solve the optimization problem of the compressed sensing formulation. In contrast with the first part, where consistent simulated projection data are generated for image reconstruction, the reduced-view inconsistent real ex-vivo synchrotron absorption contrast micro computed tomography bone data are used in the second part. A gradient regularized compressed sensing problem is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The wavelet image denoising algorithm is used as the post-processing algorithm to attenuate the unwanted staircase artifact generated by the reconstruction algorithm. Finally, a noisy and highly reduced-view inconsistent real in-vivo synchrotron phase-contrast computed tomography bone data are used for image reconstruction. A combination of prior image constrained compressed sensing framework, and the wavelet regularization is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The prior image constrained compressed sensing framework takes advantage of the prior image to promote the sparsity of the target image. It may lead to an unwanted staircase artifact when applied to noisy and texture images, so the wavelet regularization is used to attenuate the unwanted staircase artifact generated by the prior image constrained compressed sensing reconstruction algorithm. The visual and quantitative performance assessments with the reduced-view simulated and real computed tomography data from canine prostate tissue, rat forelimb, and femoral cortical bone samples, show that the proposed algorithms have fewer artifacts and reconstruction errors than other conventional reconstruction algorithms at the same x-ray dose