Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autor(en): Sanders J, Ritt P, Kuwert T, Vija AH, Maier A
Zeitschrift: IEEE Transactions on Medical Imaging
Jahr der Veröffentlichung: 2016
Band: 35
Heftnummer: 11
Seitenbereich: 2425-2435
ISSN: 0278-0062


Abstract


We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 ± 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.



FAU-Autoren / FAU-Herausgeber

Kuwert, Torsten Prof. Dr.
Lehrstuhl für Klinische Nuklearmedizin
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Ritt, Philipp Dr.-Ing.
Nuklearmedizinische Klinik
Sanders, James
Lehrstuhl für Informatik 5 (Mustererkennung)


Autor(en) der externen Einrichtung(en)
Siemens AG, Healthcare Sector


Zitierweisen

APA:
Sanders, J., Ritt, P., Kuwert, T., Vija, A.H., & Maier, A. (2016). Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps. IEEE Transactions on Medical Imaging, 35(11), 2425-2435. https://dx.doi.org/10.1109/TMI.2016.2576899

MLA:
Sanders, James, et al. "Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps." IEEE Transactions on Medical Imaging 35.11 (2016): 2425-2435.

BibTeX: 

Zuletzt aktualisiert 2018-05-10 um 07:19

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