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Improved Spatial Resolution in Segmented Silicon Strip Detectors
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Förbättrad spatiell upplösning i segmenterade kiselstrippdetektorer (Swedish)
Abstract [en]

Semiconductor detectors are attracting interest for use in photon-counting spectral computed tomography. In order to obtain a high spatial resolution, it is of interest to find the photon interaction position. In this work we investigate if machine learning can be used to obtain a sub-pixel spatial resolution in a photon-counting silicon strip detector with pixels of 10 µm. Simulated charge distributions from events in one, three, and seven positions in each of three pixels were investigated using the MATLAB® Classification Learner application to determine the correct interaction position. Different machine learning models were trained and tested in order to maximize performance. With pulses originating from one and seven positions within each pixel, the model was able to find the originating pixel with an accuracy of 100% and 88.9% respectively. Further, the correct position within a pixel was found with an accuracy of 54.0% and 29.4% using three and seven positions per pixel respectively. These results show the possibility of improving the spatial resolution with machine learning.

Abstract [sv]

Halvledardetektorer är av stigande intresse inom forskning för användning i fotonräknande datortomografi med spektral upplösning. För att erhålla en hög spatiell upplösning är det av intresse att hitta fotonens ursprungliga interaktionsposition. I detta arbete undersöks om maskininlärning kan användas för att erhålla en spatiell upplösning på subpixelnivå i en fotonräknande kiselstrippdetektor med 10 µm pixlar. Laddningsfördelningen från simulerade interaktioner i en, tre, och sju positioner inom var och en av tre pixlar undersöktes med hjälp av applikationen Classification Learner i MATLAB® för att bestämma den korrekta interaktionspositionen. Olika maskininlärningsmodeller tränades och testades för att maximera prestandan. När pulser från en och sju positioner inom pixeln användes, kunde modellen hitta den korrekta pixeln med en noggrannhet på 100% respektive 88.9%. Vidare kunde den korrekta positionen inom en pixel bestämmas med en noggrannhet på 54.0% och 29.4% när tre respektive sju positioner inom varje pixel användes. Resultaten visar att det skulle vara möjligt att förbättra den spatiella upplösningen med hjälp av maskininlärning.

Place, publisher, year, edition, pages
2019.
Series
TRITA-CBH-GRU ; 2019:063
Keywords [en]
computed tomography, photon-counting, silicon strip detector, spatial resolution, machine learning, classification
Keywords [sv]
datortomografi, fotonräknande, kiselstrippdetektor, spatiell upplösning, maskininlärning, klassificering
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-257953OAI: oai:DiVA.org:kth-257953DiVA, id: diva2:1378818
External cooperation
Prismatic Sensors AB
Subject / course
Medical Engineering
Educational program
Master of Science in Engineering - Medical Engineering
Supervisors
Examiners
Available from: 2020-01-09 Created: 2019-12-14 Last updated: 2020-01-09Bibliographically approved

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4142434445464744 of 112
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