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3D PET image reconstruction based on the maximum likelihood estimation method (MLEM) algorithm

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3D PET image reconstruction based on the maximum likelihood estimation method (MLEM) algorithm

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dc.contributor.author Słomski, Artur [USOS40166] pl
dc.contributor.author Rudy, Zbigniew [SAP11010608] pl
dc.contributor.author Bednarski, Tomasz [USOS62432] pl
dc.contributor.author Białas, Piotr [SAP11015544] pl
dc.contributor.author Czerwiński, Eryk [SAP13037062] pl
dc.contributor.author Kapłon, Łukasz [USOS61609] pl
dc.contributor.author Kochanowski, Andrzej [SAP11007569] pl
dc.contributor.author Korcyl, Grzegorz [SAP14010657] pl
dc.contributor.author Kowal, Jakub [USOS62813] pl
dc.contributor.author Kowalski, Paweł pl
dc.contributor.author Kozik, Tomasz [SAP11008791] pl
dc.contributor.author Krzemień, Wojciech [SAP12018791] pl
dc.contributor.author Molenda, Marcin [SAP11018139] pl
dc.contributor.author Moskal, Paweł [SAP11015350] pl
dc.contributor.author Niedźwiecki, Szymon [SAP14009545] pl
dc.contributor.author Pałka, Marek [SAP14007322] pl
dc.contributor.author Pawlik-Niedźwiecka, Monika [USOS133128] pl
dc.contributor.author Raczyński, Lech pl
dc.contributor.author Salabura, Piotr [SAP11011883] pl
dc.contributor.author Gupta-Sharma, Neha [USOS182180] pl
dc.contributor.author Silarski, Michał [SAP13036814] pl
dc.contributor.author Smyrski, Jerzy [SAP11011139] pl
dc.contributor.author Strzelecki, Adam [USOS118402] pl
dc.contributor.author Wiślicki, Wojciech pl
dc.contributor.author Zieliński, Marcin [SAP13036834] pl
dc.contributor.author Zoń, Natalia pl
dc.date.accessioned 2015-05-20T13:50:35Z
dc.date.available 2015-05-20T13:50:35Z
dc.date.issued 2014 pl
dc.identifier.issn 1895-9091 pl
dc.identifier.uri http://ruj.uj.edu.pl/xmlui/handle/item/7600
dc.language eng pl
dc.rights Dodaję tylko opis bibliograficzny *
dc.rights.uri *
dc.title 3D PET image reconstruction based on the maximum likelihood estimation method (MLEM) algorithm pl
dc.type JournalArticle pl
dc.description.physical 1-7 pl
dc.abstract.en A positron emission tomography (PET) scan does not measure an image directly. Instead, a PET scan measures a sinogram at the boundary of the field-of-view that consists of measurements of the sums of all the counts along the lines connecting the two detectors. Because there is a multitude of detectors built in a typical PET structure, there are many possible detector pairs that pertain to the measurement. The problem is how to turn this measurement into an image (this is called imaging). Significant improvement in PET image quality was achieved with the introduction of iterative reconstruction techniques. This was realized approximately 20 years ago (with the advent of new powerful computing processors). However, three-dimensional imaging still remains a challenge. The purpose of the image reconstruction algorithm is to process this imperfect count data for a large number (many millions) of lines of response and millions of detected photons to produce an image showing the distribution of the labeled molecules in space. pl
dc.subject.en image reconstruction pl
dc.subject.en positron emission tomography pl
dc.description.volume 10 pl
dc.description.number 1 pl
dc.description.publication 0,5 pl
dc.identifier.doi 10.1515/bams-2013-0106 pl
dc.identifier.eissn 1896-530X pl
dc.title.journal Bio-Algorithms and Med-Systems pl
dc.language.container eng pl
dc.affiliation Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki im. Mariana Smoluchowskiego pl
dc.affiliation Wydział Chemii : Zakład Technologii Chemicznej pl
dc.affiliation Wydział Fizyki, Astronomii i Informatyki Stosowanej : Zakład Technologii Gier pl
dc.subtype Article pl
dc.rights.original bez licencji pl
.pointsMNiSW [2014 B]: 6


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