A matheuristic approach to solve the multi-objective beam angle optimisation problem in intensity modulated radiation therapy

Selecting a suitable set of beam angles is an important but difficult task in intensity modulated radiation therapy (IMRT) for cancer treatment. From a single objective point of view this problem, known as beam angle optimisation (BAO) problem, is solved by finding a beam angle configuration (BAC) that leads to the best dose distribution, according to some objective function. Because there exists a trade-off between the main goals in IMRT (to irradiate the tumour according to some prescription and to avoid surrounding healthy tissue) it makes sense to solve this problem from a multi-objective (MO) point of view. When doing so, a solution of the BAO problem is no longer a single BAC but instead a set of BACs which lead to a set of dose distributions that, depending on both dose prescription and physician preferences, can be selected as the preferred treatment. We solve this MO problem using a two-phase strategy. During the first phase, a deterministic local search algorithm is used to select a set of locally optimal BACs, according to a single objective function. During this search, an optimal dose distribution for each BAC, with respect to the single objective function, is calculated using an exact non-linear programming algorithm. During the second phase a set of non-dominated points is generated for each promising locally optimal BAC and a dominance analysis among them is performed. The output of the procedure is a set of (approximately) efficient BACs that lead to good dose distributions. To demonstrate the viability of the method, the two-phase strategy is applied to a prostate case

Intensity modulated radiation therapy and arc therapy: validation and evolution as applied to tumours of the head and neck, abdominal and pelvic regions

Intensiteitsgemoduleerde radiotherapie (IMRT) laat een betere controle over de dosisdistributie (DD) toe dan meer conventionele bestralingstechnieken. Zo is het met IMRT mogelijk om concave DDs te bereiken en om de risico-organen conformeel uit te sparen. IMRT werd in het UZG klinisch toegepast voor een hele waaier van tumorlocalisaties. De toepassing van IMRT voor de bestraling van hoofd- en halstumoren (HHT) vormt het onderwerp van het eerste deel van deze thesis. De planningsstrategie voor herbestralingen en bestraling van HHT, uitgaande van de keel en de mondholte wordt beschreven, evenals de eerste klinische resultaten hiervan. IMRT voor tumoren van de neus(bij)holten leidt tot minstens even goede lokale controle (LC) en overleving als conventionele bestralingstechnieken, en dit zonder stralingsgeïnduceerde blindheid. IMRT leidt dus tot een gunstiger toxiciteitprofiel maar heeft nog geen bewijs kunnen leveren van een gunstig effect op LC of overleving. De meeste hervallen van HHT worden gezien in het gebied dat tot een hoge dosis bestraald werd, wat erop wijst dat deze “hoge dosis” niet volstaat om alle clonogene tumorcellen uit te schakelen. We startten een studie op, om de mogelijkheid van dosisescalatie op geleide van biologische beeldvorming uit te testen. Naast de toepassing en klinische validatie van IMRT bestond het werk in het kader van deze thesis ook uit de ontwikkeling en het klinisch opstarten van intensiteitgemoduleerde arc therapie (IMAT). IMAT is een rotationele vorm van IMRT (d.w.z. de gantry draait rond tijdens de bestraling), waarbij de modulatie van de intensiteit bereikt wordt door overlappende arcs. IMAT heeft enkele duidelijke voordelen ten opzichte van IMRT in bepaalde situaties. Als het doelvolume concaaf rond een risico-orgaan ligt met een grote diameter, biedt IMAT eigenlijk een oneindig aantal bundelrichtingen aan. Een planningsstrategie voor IMAT werd ontwikkeld, en type-oplossingen voor totaal abdominale bestraling en rectumbestraling werden onderzocht en klinisch toegepast

SPARC 2017 retrospect & prospects : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2017 SPARC conference. This year we not only celebrate the work of our PGRs but also the 50th anniversary of Salford as a University, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 130 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to exploit this great opportunity to engage with researchers working in different subject areas to your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

A generative adversarial network approach to synthetic-CT creation for MRI-based radiation therapy

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), Universidade de Lisboa, Faculdade de Ciências, 2019This project presents the application of a generative adversarial network (GAN) to the creation of synthetic computed tomography (sCT) scans from volumetric T1-weighted magnetic resonance imaging (MRI), for dose calculation in MRI-based radio therapy workflows. A 3-dimensional GAN for MRI-to-CT synthesis was developed based on a 2-dimensional architecture for image-content transfer. Co-registered CT and T1 –weighted MRI scans of the head region were used for training. Tuning of the network was performed with a 7-foldcross-validation method on 42 patients. A second data set of 12 patients was used as the hold out data set for final validation. The performance of the GAN was assessed with image quality metrics, and dosimetric evaluation was performed for 33 patients by comparing dose distributions calculated on true and synthetic CT, for photon and proton therapy plans. sCT generation time was <30s per patient. The mean absolute error (MAE) between sCT and CT on the cross-validation data set was69 ± 10 HU, corresponding to a 20% decrease in error when compared to training on the original 2D GAN. Quality metric results did not differ statistically for the hold out data set (p = 0.09). Higher errors were observed for air and bone voxels, and registration errors between CT and MRI decreased performance of the algorithm. Dose deviations at the target were within 2% for the photon beams; for the proton plans, 21 patients showed dose deviations under 2%, while 12 had deviations between 2% and 8%. Pass rates (2%/ 2mm) between dose distributions were higher than 98% and 94% for photon and proton plans respectively. The results compare favorably with published algorithms and the method shows potential for MRI-guided clinical workflows. Special attention should be given when beams cross small structures and airways, and further adjustments to the algorithm should be made to increase performance for these regions

Optimizing microwave hyperthermia antenna systems

This thesis presents design and optimization of a microwave hyperthermia antennasystem for treatment of head and neck cancer as well as brain cancer. Hyperthermiahas shown the ability to enhance the performance of radiotherapy andchemotherapy in many clinical trials. The incidence of increased tissue toxicity asa result of high radiotherapy dose has made hyperthermia a safe complementing,treatment enhancing method to use in combination with radiotherapy. Althoughmany clinical studies have shown the effectiveness of hyperthermia for treatmentof the head-and-neck (H&amp;N) cancer, the presence of large vessels, tissue transitionsand critical tissues in the head and neck poses therapeutic challenges fortreatment of advanced tumors in this region. Late side-effects of conventionaltherapies in treatment of brain tumors in children have been made hyperthermiaan attractive method. However, heating tumors in the brain is even more challengingbecause of its high sensitivity, high thermal conductivity and high perfusion.In this thesis microwave hyperthermia applicators are presented for efficientheating of the H&amp;N and brain tumors. For this purpose, an ultra-wideband antennahas been designed, built and evaluated to act as the radiating element of microwavehyperthermia applicators. The time reversal focusing technique is used totarget electromagnetic energy into the tumor. To obtain more accurate treatmentplanning, the effect of frequency and virtual source positions, in the time-reversalmethod, are studied for different tumor sizes and tumor positions. The optimaldetailed design of the applicator, such as the number of antennas and the antennapositions are also investigated.In the second part of the thesis, the focus is on the applicators for treatment ofthe brain tumors in children. Helmet applicators are presented and the effect of thenumber of antennas and the frequency are investigated on the performance of theapplicators for heating large and deep-seated brain tumors. Finally, the optimumposition of antennas in helmet applicators are found by performing optimizationon simplified and realistic models of the child head

Evaluating the quality of radiotherapy treatment plans with uncertainty using data envelopment analysis

External beam radiation therapy is a common treatment method for cancer. Radiotherapy is planned with the aim of achieving conflicting goals: while a sufficiently high dose of radiation is necessary for tumour control, a low dose of radiation is desirable to avoid complications in normal, healthy, tissue. This thesis aims to support the radiotherapy treatment planning process for prostate cancer by evaluating the quality of proposed treatment plans relative to previous plans. We develop a variable selection technique, autoPCA, to select the most relevant variables for use in our Data Envelopment Analysis (DEA) models. This allows us to evaluate how well plans perform in terms of achieving the conflicting goals of radiotherapy. We develop the uncertain DEA problem (uDEA) for the case of box uncertainty and show that for small problems this can be solved exactly. This study of uncertainty is motivated by the inherently uncertain nature of the treatment process. Robust DEA, uDEA and simulation are applied to prostate cancer treatment plans to investigate this uncertainty. We identify plans that have the potential to be improved, which clinicians then replan for us. Small improvements were seen and we discuss the potential difference this could make to planning cases that are more complex. To aid this, we develop a prototype software, EvaluatePlan, that assesses the efficiency of a plan compared to past treatment plans