P P Bruyant1, J Sau, J J Mallet. 1. Nuclear Spectroscopy and Image Processing Research Group, Biophysics Laboratory, Claude Bernard University, Lyon, France.
Abstract
UNLABELLED: Factor analysis of dynamic structures (FADS) facilitates the extraction of relevant data, usually with physiologic meaning, from a dynamic set of images. The result of this process is a set of factor images and curves plus some residual activity. The set of factor images and curves can be used to retrieve the original data with reduced noise using an inverse factor analysis process (iFADS). This improvement in image quality is expected because the inverse process does not use the residual activity, assumed to be made of noise. The goal of this work is to quantitate and assess the efficiency of this method on gated cardiac images. METHODS: A computer simulation of a planar cardiac gated study was performed. The simulated images were added with noise and processed by the FADS-iFADS program. The signal-to-noise ratios (SNRs) were compared between original and processed data. Planar gated cardiac studies from 10 patients were tested. The data processed by FADS-iFADS were subtracted to the original data. The result of the substraction was studied to evaluate its noisy nature. RESULTS: The SNR is about five times greater after the FADS-iFADS process. The difference between original and processed data is noise only, i.e., processed data equals original data minus some white noise. CONCLUSION: The FADS-iFADS process is successful in the removal of an important part of the noise and therefore is a tool to improve the image quality of cardiac images. This tool does not decrease the spatial resolution (compared with smoothing filters) and does not lose details (compared with frequential filters). Once the number of factors is chosen, this method is not operator dependent.
UNLABELLED: Factor analysis of dynamic structures (FADS) facilitates the extraction of relevant data, usually with physiologic meaning, from a dynamic set of images. The result of this process is a set of factor images and curves plus some residual activity. The set of factor images and curves can be used to retrieve the original data with reduced noise using an inverse factor analysis process (iFADS). This improvement in image quality is expected because the inverse process does not use the residual activity, assumed to be made of noise. The goal of this work is to quantitate and assess the efficiency of this method on gated cardiac images. METHODS: A computer simulation of a planar cardiac gated study was performed. The simulated images were added with noise and processed by the FADS-iFADS program. The signal-to-noise ratios (SNRs) were compared between original and processed data. Planar gated cardiac studies from 10 patients were tested. The data processed by FADS-iFADS were subtracted to the original data. The result of the substraction was studied to evaluate its noisy nature. RESULTS: The SNR is about five times greater after the FADS-iFADS process. The difference between original and processed data is noise only, i.e., processed data equals original data minus some white noise. CONCLUSION: The FADS-iFADS process is successful in the removal of an important part of the noise and therefore is a tool to improve the image quality of cardiac images. This tool does not decrease the spatial resolution (compared with smoothing filters) and does not lose details (compared with frequential filters). Once the number of factors is chosen, this method is not operator dependent.
Authors: Hendrik J Harms; Paul Knaapen; Stefan de Haan; Rick Halbmeijer; Adriaan A Lammertsma; Mark Lubberink Journal: Eur J Nucl Med Mol Imaging Date: 2011-01-27 Impact factor: 9.236
Authors: Stergios Tsartsalis; Benjamin B Tournier; Christophe E Graf; Nathalie Ginovart; Vicente Ibáñez; Philippe Millet Journal: PLoS One Date: 2018-09-05 Impact factor: 3.240