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Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0002-6252-7815
2017 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 44, no 6, p. 2045-2053Article in journal (Refereed) Published
Abstract [en]

Purpose: To formulate convex planning objectives of treatment plan multicriteria optimization with explicit relationships to the dose-volume histogram (DVH) statistics used in plan quality evaluation. Methods: Conventional planning objectives are designed to minimize the violation of DVH statistics thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more closely relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. Results: We investigated the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives were generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume was observed in plans optimized within the proposed framework. Conclusion: The initial computational study indicates that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives than using the conventional approach.

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 44, no 6, p. 2045-2053
Keywords [en]
convex optimization, mean-tail-dose, planning objectives
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-213816DOI: 10.1002/mp.12146ISI: 000408033400003PubMedID: 28160520Scopus ID: 2-s2.0-85024484606OAI: oai:DiVA.org:kth-213816DiVA, id: diva2:1140259
Note

QC 20170911

Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2018-10-24Bibliographically approved
In thesis
1. Automated radiation therapy treatment planning by increased accuracy of optimization tools
Open this publication in new window or tab >>Automated radiation therapy treatment planning by increased accuracy of optimization tools
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Every radiation therapy treatment is preceded by a treatment planning phase. In this phase, a treatment plan that specifies exactly how to irradiate the patient is designed by the treatment planner. Since the introduction of intensity-modulated radiation therapy into clinical practice in the 1990's, treatment planning involves, and requires, the use of advanced optimization tools due to the largely increased degrees of freedom in treatment specifications compared to earlier radiation therapy techniques.

The aim of treatment planning is to create a plan that results in the, in some sense, best treatment---a treatment that at the same time reflects the patient-specific clinical goals, achieves the best possible quality, and adheres to other possible preferences of the oncologist or of the clinic. Despite dedicated treatment planning systems available with advanced optimization tools, treatment planning is often referred to as a complicated process involving many iterations with successively adjusted parameters. Over the years, a request has emerged from the clinical and treatment planners' side to make treatment planning less time-consuming and more straightforward, and the methods subsequently developed as a response have come to be referred to as methods for automated treatment planning.

In this thesis, a framework for automated treatment planning is proposed and its potential and flexibility investigated. The focus is placed on increasing the accuracy of the optimization tools, aiming at achieving a less complicated treatment planning process that is driven by intuition rather than, as currently, trial and error. The suggested framework is contrasted to a class of methods dominating in the literature, which applies a more classical view of automation to treatment planning and strives towards reducing any type of human interaction. To increase the accuracy of the optimization tools, the underlying so-called objective functions are reformulated to better correlate with measures of treatment plan quality while possessing mathematical properties favorable for optimization. An important step is to show that the suggested framework not only is theoretically desirable, but also useful in practice. An interior-point method is therefore tailored to the specific structure of the novel optimization formulation, and is applied throughout the thesis, to demonstrate tractability. Numerical studies support the idea of the suggested framework equipping the treatment planner with more accurate and thereby less complicated tools to more straightforwardly handle the intrinsically complex process that constitutes treatment planning. 

Abstract [sv]

Varje strålbehandling föregås av en dosplaneringsfas. Under dosplaneringsfasen skapas den strålbehandlingsplan som exakt beskriver hur strålbehandlingen ska genomföras. Sedan 1990-talet och den så kallade intensitetsmodulerade strålbehandlingens inträde i klinisk praxis har dosplanering kommit att betyda och rent av kräva användande av avancerade optimeringsverktyg -- en konsekvens av den kraftigt ökade mängden frihetsgrader jämfört med tidigare strålbehandlingstekniker.

Det övergripande målet med dosplanering är att skapa en plan som i någon mening ger den bästa strålbehandlingen. En sådan behandling ska i synnerhet spegla de kliniska mål som satts upp för den enskilda patienten, i allmänhet uppnå bästa möjliga kvalitet samt förhålla sig till eventuella övriga önskemål från onkologen eller kliniken. Utbudet av dosplaneringssystem med avancerade optimeringsverktyg är stort och användandet utbrett, men trots detta beskrivs ofta dosplanering som en komplicerad process där finjustering av parametrar utgör en väsentlig del. Därför har efterfrågan på hjälpmedel för mindre tidskrävande och mer rättfram dosplanering under det senaste årtiondet vuxit fram. De metoder som utvecklats som svar benämns som metoder för automatiserad dosplanering.

I det här arbetet föreslås och utvärderas ett ramverk för automatiserad dosplanering. Fokus har lagts på optimeringsverktygen och att förbättra noggrannheten i dessa, för att därigenom skapa förutsättningar för mindre komplicerad dosplanering där intuition snarare än ett tidskrävande experimenterande driver processen framåt. Ramverket som här föreslås ställs i kontrast till en annan, dominerande klass av föreslagna metoder för automatiserad dosplanering som bygger på en mer klassisk syn på automatisering, det vill säga, som strävar efter att minska människa-datorinteraktion i allmänhet. Förbättring av optimeringsverktygens noggrannhet uppnås genom att omformulera de bakomliggande så kallade målfunktionerna till alternativ som bättre korrelerar med givna kvalitetsmått och som samtidigt har matematiska egenskaper som är önskvärda vid optimering. Ett viktigt steg är dock att visa att det föreslagna ramverket inte bara är teoretiskt lämpligt, utan att det också är praktiskt hanterbart ur beräkningssynpunkt. En inrepunktsmetod anpassas till den specifika strukturen på det nya, storskaliga optimeringsproblemet för att visa just detta. Fallstudier stödjer idén om att det föreslagna ramverket ger mer noggranna och därmed lätthanterliga optimeringsverktyg, med vilka dosplaneringens ofrånkomliga komplexitet kan hanteras på ett mer effektivt sätt. 

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2018
Series
TRITA-SCI-FOU ; 2018:43
Keywords
Optimization, intensity-modulated radiation therapy, radiation therapy treatment planning, automated radiation therapy treatment planning, interior-point methods, Optimering, intensitetsmodulerad strålbehandling, dosplanering, automatiserad dosplanering, inrepunktsmetoder
National Category
Computational Mathematics
Research subject
Mathematics
Identifiers
urn:nbn:se:kth:diva-237243 (URN)978-91-7729-943-1 (ISBN)
Public defence
2018-11-23, F3, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-24 Last updated: 2018-10-25Bibliographically approved

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Engberg, LovisaForsgren, Anders

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