Dynamic optical imaging.

The main direction in modern imaging is increasing the spatial resolution and selectivity for pathology pattern recognition at the microscale. Dynamic optical imaging (DOl) has enormous potential in the selectivity of description of living tissue state at cellular and subcellular levels. However, multiple light scattering creates considerable difficulties in revealing the tissue microstructure in its depth.

3D functional mapping of left ventricular dynamics.

The heart is an organ which functions by a periodic change of the three dimensional (3D) spatially distributed parameters; malfunctions of the heart's operating systems are manifested by changes of the spatio-temporal heart shape dynamics. A comprehensive quantitative study of this dynamic shape-function relationship is restricted by the partial character of the available data sets obtained by conventional imaging technologies and by limitations of the image analysis tools. This paper attempts to present a set of image analysis tools aimed at a thorough study of the left ventricular (LV) shape-function relationship based on Cine CT data.

Signal reconstruction from noisy-phase and -magnitude data.

Signal reconstruction based on knowing either the magnitude or the phase of the Fourier transform of the signal is important in numerous applications, and the problem of signal reconstruction from noisy-phase and noisy-magnitude data is addressed. The proposed procedure relates to the deviations of the available magnitude and phase estimates from their exact values in the reconstruction algorithm by use of spectral prototype constraint sets. The properties of these new constraint sets for the magnitude and the phase of the Fourier transform are analyzed, and the corresponding projection operators are constructed.

Constrained detection of left ventricular boundaries from cine CT images of human hearts.

Detection of the left ventricular (LV) endocardial (inner) and epicardial (outer) boundaries in cardiac images, provided by fast computer tomography (cine CT), magnetic resonance (MR), or ultrasound (echocardiography), is addressed. The automatic detection of the LV boundaries is difficult due to background noise, poor contrast, and often unclear differentiation of the tissue characteristics of the ventricles, papillary muscles, and surrounding tissues. An approach to the automatic ventricular boundary detection that employs set-theoretic techniques, and is based on incorporating a priori knowledge of the heart geometry, its brightness, spatial structure, and temporal dynamics into the boundaries detection algorithm is presented.