Automated processing of first-pass radionuclide angiocardiography by factor analysis of dynamic structures

Cavaillolés, F.; Bazin, J.; Capderou, A; Valette, Héric; Herbert, J L; Paola, R. Di

doi:10.1097/00006231-198705000-00009

Cited by 5 publications

(3 citation statements)

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“…FADS extracts five factors (Fig. 11) corresponding to the superior vena cava, the right heart, the lungs, the left heart and the extracardio-pulmonary circulation [11]. The factors obtained on the two series, before and after compression, are superimposed ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Various nuclear medicine dynamic series were compressed and results presented in this paper concern a cardiac sequence previously studied using Factor Analysis of Dynamic Structures (FADS) [11]: first pass radionuclide angiocardiography including 70 images 64*64 acquired at a rate of 2 frames per second, after injection of 99mTC redblood cells labelled in vitro.…”

Section: Methodsmentioning

confidence: 99%

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High compression of nuclear medicine dynamic studies

Chameroy

Paola

1990

Int J Cardiac Imag

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As data compression plays now an important role in the development of medical PACS, a technique has been developed for medical image sequences storage and transmission in order to obtain very high compression ratio: in dynamic nuclear medicine studies it can achieve a compression ratio as high as 100:1 without significant degradation. The implemented technique combines two methods which multiply their effects. In a first step, a principal component analysis (PCA) of the image series is performed. It extracts a limited number of principal components and their associated images. For data compression it is not necessary to perform an oblique factor analysis to estimate the so-called 'physiological functions' and their spatial distributions as in factor analysis of dynamic structures (FADS). In a second step, the principal images are compressed by means of a transform coding procedure: an adaptive block-quantization technique using the 2D discrete cosine transform (DCT) is implemented, followed by a statistical quantization method to encode the DCT coefficients. To reconstruct the principal images, an inverse DCT is applied. Then the original series is computed from the reconstructed images combined with the principal components which have been stored without any modification. The reconstructed series is compared to the original series, as well as the time activity curves generated on different regions of interest (ROI) and the factor estimates obtained using FADS performed on the two series. Method and evaluation are illustrated on an example of first pass radionuclide angiocardiography.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High compression of nuclear medicine dynamic studies

Chameroy

Paola

1990

Int J Cardiac Imag

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“…Indeed, factor analysis aim is to extract factors and their associated factors images from dynamic series under noise reduction and positivity constraints, and factors are meant to represent main physiological kinetics while factors images should restore their spatial distribution [2]. It has been first applied to various studies in dynamic scintigraphy, for instance in cardiology [3] and in oncology [4]. In this last study, quantitative indices were derived from factor images.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of factor analysis accuracy for myocardial perfusion in PET studies

Grégoire

Frouin²,

Lávenne³

et al.

1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record

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Factor Analysis of Medical Image Sequences (FAMIS) estimates kinetics ( factors) and corresponding spatial regions (factor images) from dynamic studies, taking into account statistical noise and spillover effects. Factor images obtained from 150-water clinical cardiac PET studies are less noisy than conventional subtraction images, and factors match physiologic kinetics. The aim of this work was to study FAMIS accuracy and precision depending on the application context. They were evaluated through kinetics parameters quantification. Numerical simulations and phantom experiments were carried out using a typical left ventricular pattern. This object was simulated in 2D with three noise levels and two kinds of kinetics : mono-exponentials which correspond to natural tracer decay and tissue perfusion kinetics obtained with a realistic vascular input function. Monoexponentials association was adapted to phantom experiments while perfusion kinetics standed for clinical cardiac studies. In both phantom experiments and simulations, the inner chamber was filled with 150-water and the myocardial space with Carbon-1 1. The different noise levels which were studied corresponded to ideal, normal and low quality scans. Using the factors estimated by FAMIS, decay constants and an index of flow (kl) were estimated by fitting or modelling. Relative bias to the true value and standard deviation were then estimated, and spatial correlation between factor images and original spatial pattern was computed. Factor images spatial correlation was very good, despite of large overlapping pattern. Oxygen-15 decay constant was assessed from factor with a small relative bias, for all noise levels and trixel sizes. However, Carbon-1 1 extraction was very sensitive to both noise and spillover in phantom and simulations. A reasonable bias was only achieved by including a spillover term which accounted for an overcorrection. On the contrary, factors associated to perfusion were well extracted and k l parameter was recovered with a low relative bias (r,b. < 6%), except for the higher noise level. It was already shown that FAMIS performences depend on the overlap of the spatial structures.This study demonstrates that factor analysis without a priori information performances depend on kinetics shape. Moreover, in the context of cardiac 150-water perfusion studies, FAMIS should provide accurate quantification.

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