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Post-processingof Monte Carlo calculated dose distributions
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Efterbehandling av dosfördelningar beräknade med Monte Carlo simulering (Swedish)
Abstract [en]

This Master Thesis focuses on denoising of Monte Carlo calculated dose distributions of radiosurgery treatment plans. The objective of this project is to implement a Denoising Autoencoder (DAE) and investigate its denoising performance when it has been trained on Monte Carlo calculated dose distributions generated with lower number of photon showers. The DAE is trained in a supervised setting to learn the mapping between corrupted observations and clean ones. The questions this thesis aims to answer are: (i) Can a DAE be used to denoise Monte Carlo calculated dose distributions, and thus predict the dose prior to a full simulation? Additionally, (ii) does incorporating prior knowledge of shot position increase the denoising performance? The results in this investigation have shown that the network successfully predicts the dose for low number of photon showers. In very heavy noise inputs the network denoising was in general successful, and the network could fill in missing data. The results indicated that the DAE could reduce the level of noise with an amount comparable with simulations that were done with 102 times more samples.

Abstract [sv]

Denna masteruppsats fokus är på att brusreducera Monte Carlo-beräknade dosdis-tributioner för behandlingsplaner i hjärnstereotaktisk radiokirurgi. Projektets avsikt är att implementera en brusreducerande Autoencoder (DAE) samt undersöka dess brusreducerande egenskaper, när nätverket har tränats på Monte Carlo-beräknade dosdistributioner genererade med få fotonsimulationer. Den brusreducerande Au-toencodern har genomgått övervakad träning, där den lär sig en avbildning mellan brusiga till brusfria distributioner. Frågorna som denna uppsats ämnar besvara är;(i) Kan en brusreducerande Autoencoder användas för att brusreducera Monte Carlo-beräknade dosdistributioner, och därmed förutspå dosen på förhand? Dessutom, (ii) förbättras nätverkets brusreducerande prestanda när ytterligare information angående skottpositionerna tillförs till nätverket? Resultaten i denna undersökning pekade på att nätverket framgångsrikt förutspår dosdistributionerna, baserat på dosdistribu-tioner som simulerats med få fotonsimulationer. I de fall då bruset i indata är väldigt kraftigt lyckas fortfarande nätverket att brusreducera, samt lyckat fylla i data som saknas. Resultaten indikerade att den brusreducerande Autoencodern kunde reduc-era brus i en mängd som kan jämföras med en simulation som gjorts med en faktor 102 fler fotonsimulationer.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:028
National Category
Mathematical Analysis
Identifiers
URN: urn:nbn:se:kth:diva-244314OAI: oai:DiVA.org:kth-244314DiVA, id: diva2:1290090
External cooperation
Elekta
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-02-19 Created: 2019-02-19 Last updated: 2019-02-19Bibliographically approved

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