R. Di Paola

Handling of Dynamic Sequences in Nuclear Medicine

Nuclear Medicine is one of the first domains in which the analysis of image sequences was introduced. The development of this analysis was achieved parallel to the one of the computer systems linked to the scintillation cameras. The number of works that were performed in the research laboratories and have received an application in clinical routine is however limited. The authors indicate what could be the flow chart of the processing of dynamic sequences in scintigraphy and the kind of material that would be necessary to implement it. The possibilities of using the factor and compartmental analyses in clinical routine are particularly emphasized. The authors indicate why the factors and their associated images obtained by means of the factor analysis can have a physiological meaning.

Scatter correction in scintigraphy : the state of the art

In scintigraphy, the detection of scattered photons degrades both visual image analysis and quantitative accuracy. Many methods have been proposed and are still under investigation to cope with scattered photons. The main features of the problem of scattering in radionuclide imaging are presented first, to provide a sound foundation for a critical review of the existing scatter correction techniques. These are described using a classification relating to their aims and principles. Their theoretical potentials are analysed, as well as the difficulties of their practical implementation. Finally, the problems of their evaluation and comparison are discussed.

Target apex-seeking in factor analysis of medical image sequences

The aim of factor analysis of medical image sequences (FAMIS) is to estimate a limited number of physical or physiological fundamental functions. Its oblique rotation stage strongly affects the quality and the interpretation of the resulting estimates (factors and factor images). A new target apex-seeking method which integrates physical or physiological knowledge in this stage is described. This knowledge concerns some of the fundamental functions and reacts on the determination of all the factors. A simulated spectral study illustrates the method. We discuss its properties in comparison with the other approaches using a priori physical or physiological information.

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

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.

Statistical distribution of factors and factor images in factor analysis of medical image sequences

From a time or energy image sequence, factor analysis of medical image sequences (FAMIS) estimates factors, representing kinetics or spectra in a given physiological compartment, and associated factor images, showing the compartments corresponding to each curve. In this paper, we show that the statistical properties of factor images and associated factors can be determined using a well known result from elementary probability theory. Numerical experiments are conducted to demonstrate that the variance observed in factor images can be predicted when the statistical properties of the original data are known. It is shown how these theoretical results can be used to relax the non-negativity constraints during FAMIS oblique analysis and to improve the quantitative interpretation of the factor images by associating a confidence interval with each pixel value.

Space-Time Statistical Model for Functional MRI Image Sequences

Changes in cerebral blood oxygenation and flow during activation of human brain can be measured using functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation. Ideally, spatial and temporal correlations in the acquired data should all be taken into account to derive statistical parametric maps (SPM) and to identify significant changes in fMRI signal. This paper proposes a multivariate statistical model for brain activation detection accounting for both the spatial and temporal correlations. This model considers a space-time variant error and a spatial Markov random field process is used to yield an unbiased estimate of the SPM. As the number of pixels is large enough, the asymptotic theory is used to derive a threshold allowing the identification of activated areas in the SPM. The method is illustrated on sensorimotor experiments performed on normal subjects using 1.5T gradient-echo MRI.

Factor analysis as a means of determining response to chemotherapy in patients with osteogenic sarcoma.

The prognosis of localized osteogenic sarcoma (OS) has improved considerably since the introduction of neoadjuvant chemotherapy. However, there is a subset of patients who do not show full benefit from neoadjuvant chemotherapy because of chemoresistance. The early identification of poor responders to chemotherapy during neoadjuvant therapy remains difficult. In order to evaluate the role of bone scintigraphy we report our experience of dynamic technetium-99m hydroxymethylene diphosphonate bone scintigraphy in 19 cases of paediatric osteogenic sarcomas. Before the beginning of chemotherapy, a dynamic scan was recorded during 30 min followed by static images at 3 h. The procedure was repeated halfway through the course of chemotherapy (6th week). Histological grading of the response to chemotherapy was carried out in the 12th week, showing nine good responses and ten poor responses. Factor analysis of dynamic structures (FADS) applied to dynamic scans allowed us to identify three factors termed vascular, “soft tissue” and osseous factors. The effect of chemotherapy on each factor was evaluated. Using FADS we were able to detect all the poor histological responders with the combination of vascular and osseous factors. Six out of nine good histological responders were also classified as scintigraphic responders. FADS applied to dynamic bone scans allowed us to identify at an early stage all the poor histological responders to neoadjuvant chemotherapy. This method may have clinical relevance for the therapeutic strategy in patients with OS.

Comparison between factor analysis of dynamic structures and Fourier analysis in detection of segmental wall motion abnormalities: a clinical evaluation

To evaluate Factor Analysis of Dynamic Structures (FADS) versus or in association with other methods, a protocol was set up including as ‘gold standard’ investigation the left ventricular angiography (LVA) and processing by Fourier Analysis (FA), and FADS with different variants. To refine the diagnosis of Regional Wall Motion Abnormalities (RWMA), processing was done on a sectorial basis for more accurate spatial localization and functional description.53 patients were studied (8 normal, 45 with coronary artery disease). FADS gave better results than FA on a sectorial basis. Total agreement between FADS and LVA was obtained in 208/265 (78%), while FA was in agreement with LVA in only 167/265 segments (63%). Globally, FADS was significantly better than FA (Z-test: p<0.05). When only the diagnosis of maximal abnormality was considered, FA and FADS are statistically equivalent. The superiority of FADS vs FA is more obvious in the diagnosis of hypokinesia. Most FA discrepancies corresponded to underestimation of WMA.

Foundations of factor analysis of medical image sequences: a unified approach and some practical implications

Abstract Factor Analysis of Medical Image Sequences (FAMIS) is presently conducted either in the function space or in the image space. A unified approach jointly using these two spaces is presented. First, the solution of a statistical model for scintigraphic image sequences leads to the use of correspondence analysis which is the optimal orthogonal decomposition of this data. Then, two symmetrical hypotheses concerning either the underlying fundamental functions or the underlying fundamental spatial distributions are derived. These hypotheses are merged in an original method to solve FAMIS physical model. Using this unified approach, a priori knowledge about functions and images can be jointly taken into account to improve the estimation of the underlying structures. Some practical applications of the method are illustrated on simulated data.

Spectral Factor Analysis for Multi-isotope Imaging in Nuclear Medicine

In nuclear medicine, simultaneous dual-isotope imaging is used to determine the distribution of two radiotracers from a single acquisition and for emission/transmission (E/T) imaging in SPECT. However, no general solution to the cross-talk problem caused by scattered and unscattered photons has been found yet and accurate quantification cannot be performed. We describe a general method of spectral factor analysis (SFA) for multi-isotope acquisitions. SFA corrects for cross-talk due to unscattered and scattered photons in planar or SPECT imaging involving two or more radiotracers and for E/T scans. A Tc-99m/I-123 phantom study shows that quantitative accuracy is within 10% with SFA, while errors up to 170% are observed using conventional spectral windows.