Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks

Login for users with Unibas email account...

Login for registered users without Unibas email account...

 
Compressed Sensing on Multi-Pinhole Collimator SPECT Camera for Sentinel Lymph Node Biopsy
ConferencePaper (Artikel, die in Tagungsbänden erschienen sind)
 
ID 4413088
Author(s) Seppi, Carlo; Nahum, Uri; von Niederhäusern, Peter A.; Pezold, Simon; Rissi, Michael; Haerle, Stephan; Cattin, Philippe C.
Author(s) at UniBasel Cattin, Philippe Claude
Year 2017
Title Compressed Sensing on Multi-Pinhole Collimator SPECT Camera for Sentinel Lymph Node Biopsy
Book title (Conference Proceedings) Medical Image Computing and Computer Assisted Interventions (MICCAI)
Place of Conference Quebec
Publisher Springer
Place of Publication Cham
Pages 415-423
ISSN/ISBN 978-3-319-66184-1 ; 978-3-319-66185-8
Abstract State-of-the-art imaging devices for sentinel lymph node biopsy are either a 1-dimensional gamma probe or more recently 2-dimensional gamma cameras that locate the sentinel lymph node. These devices, however, share difficulties when multiple lymph nodes are close-by and do not allow the estimation of the distance to the lymph nodes, as the tracer activation is projected either to a 1- or 2-dimensional image plane. We propose a method, which reconstructs the tracer distribution using a single image of the detector resulting from a multi-pinhole collimator. Applying standard image processing tools on the detector’s image leads to a reduced, sparse system. Thus, we propose an efficient and reliable compressed sensing strategy, to reconstructs the 3-dimensional tracer distribution using a multi-pinhole collimator and a single detector image. This approach enables better estimation of lymph nodes position and improves the differentiation of close-by lymph nodes.
Series title Lecture Notes in Computer Science book series (LNCS)
Number 10434
edoc-URL https://edoc.unibas.ch/63977/
Full Text on edoc No
Digital Object Identifier DOI 10.1007/978-3-319-66185-8_47
ISI-Number INSPEC:17192306
Document type (ISI) inproceedings
 
   

MCSS v5.8 PRO. 0.346 sec, queries - 0.000 sec ©Universität Basel  |  Impressum   |    
11/05/2024