Cargando…
Adaptive Autoregressive Model for Reduction of Noise in SPECT
This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared wit...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450303/ https://www.ncbi.nlm.nih.gov/pubmed/26089966 http://dx.doi.org/10.1155/2015/494691 |
_version_ | 1782373990254247936 |
---|---|
author | Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti |
author_facet | Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti |
author_sort | Takalo, Reijo |
collection | PubMed |
description | This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects. |
format | Online Article Text |
id | pubmed-4450303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-44503032015-06-18 Adaptive Autoregressive Model for Reduction of Noise in SPECT Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti Comput Math Methods Med Research Article This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects. Hindawi Publishing Corporation 2015 2015-05-18 /pmc/articles/PMC4450303/ /pubmed/26089966 http://dx.doi.org/10.1155/2015/494691 Text en Copyright © 2015 Reijo Takalo et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title | Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title_full | Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title_fullStr | Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title_full_unstemmed | Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title_short | Adaptive Autoregressive Model for Reduction of Noise in SPECT |
title_sort | adaptive autoregressive model for reduction of noise in spect |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450303/ https://www.ncbi.nlm.nih.gov/pubmed/26089966 http://dx.doi.org/10.1155/2015/494691 |
work_keys_str_mv | AT takaloreijo adaptiveautoregressivemodelforreductionofnoiseinspect AT hyttiheli adaptiveautoregressivemodelforreductionofnoiseinspect AT ihalainenheimo adaptiveautoregressivemodelforreductionofnoiseinspect AT sohlbergantti adaptiveautoregressivemodelforreductionofnoiseinspect |