A statistical model for the determination of the optimal metric in factor analysis of medical image sequences (FAMIS)

Abstract

A statistical model is added to the conventional physical model underlying factor analysis of medical image sequences (FAMIS). It allows a derivation of the optimal metric to be used for the orthogonal decomposition involved in FAMIS. The oblique analysis of FAMIS is extended to take this optimal metric into account. The case of scintigraphic image sequences is used. We derive in this case that the optimal decomposition is obtained by correspondence analysis. A scintigraphic dynamic study illustrates the practical consequences of the use of the optimal metric in FAMIS.

Estimation of Response Functions Based on Variational Bayes Algorithm in Dynamic Images Sequences

“…The data are expected to contain three sources of activity: (i) parenchyma, the outer part of a kidney where the tracer is accumulated at the first, (ii) pelvis, the inner part of a kidney where the accumulation has physiological delay, and (iii) background tissues which is typically active at the beginning of the sequence. Since the noise in scintigraphy is Poisson distributed, the assumption of homogeneous Gaussian noise (6) can be achieved by asymptotic scaling known as the correspondence analysis [19] which transforms the original data D orig as $\begin{array}{c}T1d_{ij}=\frac{d_{ij,\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{g}}}{\sqrt{{false\sum }_{i=\mathrm{1}}^p{d}_{ij,\mathrm{normalo}\mathrm{normalr}\mathrm{normali}\mathrm{normalg}}{false\sum }_{j=\mathrm{1}}^n{d}_{ij,\mathrm{normalo}\mathrm{normalr}\mathrm{normali}\mathrm{normalg}}}}.\end{array}$First, we applied the methods from Sections 3.1–3.3 on dataset number 84 as a typical noncontroversial case. The results are shown in Figure 5 using the estimated source images (columns 1–3), the estimated related response functions (columns 4–6), and the estimated input function (column 7).…”

A comparative study of scatter correction methods for scintigraphic images

“…Orthogonal analysis: A correspondence analysis was used to ensure an optimal separation of signal and noise in the scintigraphic data [18].…”

Scatter and cross-talk corrections in simultaneous Tc-99m/I-123 brain SPECT using constrained factor analysis and artificial neural networks

“…S is obtained by performing an orthogonal decomposition with the appropriate metrics for Poisson noise, i.e., a correspondence analysis [14]. In our study, the orthogonal decomposition yields a basis of 4 orthogonal eigenvectors which span the noise free study space S. -Oblique analysis estimates the fundamental spectra s4 and the corresponding factor images a4 in the study subspace using constraints derived from a priori knowledge about the expected spectra.…”