Improved sparse reconstruction for fluorescence molecular tomography with Poisson noise modeling

Yansong Zhu, Abhinav Kumar Jha, Dean Foster Wong, Arman Rahmim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a maximum-likelihood-expectation-maximization (MLEM)-based method that models Poisson noise for improved reconstruction in fluoroscence molecular tomography with sparse fluroscence distribution.

Original languageEnglish (US)
Title of host publicationClinical and Translational Biophotonics, TRANSLATIONAL 2018
PublisherOSA - The Optical Society
VolumePart F91-TRANSLATIONAL 2018
ISBN (Electronic)9781557528209
DOIs
StatePublished - Jan 1 2018
EventClinical and Translational Biophotonics, TRANSLATIONAL 2018 - Hollywood, United States
Duration: Apr 3 2018Apr 6 2018

Other

OtherClinical and Translational Biophotonics, TRANSLATIONAL 2018
CountryUnited States
CityHollywood
Period4/3/184/6/18

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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