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Erdogan Cesmeli

Publications and source records attributed to Erdogan Cesmeli.

2 recordsLinked to original sources

Gauging effective spatial resolution in multirow helical cardiac computed tomography with a dynamic phantom.

PURPOSE: To devise a numerical indicator of image quality for multirow helical cardiac computed tomography (CT) and its relation to temporal resolution. MATERIALS AND METHODS: A pulsatile cardiac assist device was used to simulate cardiac wall motion by mechanically transmitting the device dynamics to a piece of tungsten wire. Wire motion induced by different device rates was captured with an 8-row subsecond helical CT scanner operating with various scanning parameters. Image artifacts were visually assessed and compared with the image point spread function (PSF) using the full width at half maximum (FWHM) area as a numerical estimate of spatial accuracy. RESULTS: At rest, the FWHM area was determined as 1.3 mm2. At a device rate of 60 bpm, the FWHM area ranged from 1.51 mm2 to 21.62 mm2, depending on the time of image reconstruction. Mean reproducibility of the FWHM area measurements was determined as 0.05, whereas visual estimates of motion artifact were highly variable between different readers (kappa = 0.19). Visually determined image quality correlated closely with the FWHM area metric (Spearman's rank correlation, P = 0.0001, rho = 0.841). At a device rate of 100 bpm, the minimum FWHM area was 2.00 mm2 using a single-sector algorithm, 1.41 mm2 using a 2-segment algorithm, and 1.37 mm2 using a 4-segment algorithm. CONCLUSIONS: Use of a pulsatile cardiac assist device could serve as an in vitro test bed for cardiac CT imaging methods. Area FWHM of the PSF correlates well with visually determined image quality of a dynamic phantom, but provides better reproducibility than visual analysis.

Algorithms↗

An oscillatory correlation model of visual motion analysis.

We describe and evaluate a model of motion perception based on the integration of information from two parallel pathways: a motion pathway and a luminance pathway. The motion pathway has two stages. The first stage measures and pools local motion across the input animation sequence and assigns reliability indices to these pooled measurements. The second stage groups locations on the basis of these measurements. In the luminance pathway, the input scene is segmented into regions on the basis of similarities in luminance. In a subsequent integration stage, motion and luminance segments are combined to obtain the final estimates of object motion. The neural network architecture we employ is based on LEGION (locally excitatory globally inhibitory oscillator networks), a scheme for feature binding and region labeling based on oscillatory correlation. Many aspects of the model are implemented at the neural network level, whereas others are implemented at a more abstract level. We apply this model to the computation of moving, uniformly illuminated, two-dimensional surfaces that are either opaque or transparent. Model performance replicates a number of distinctive features of human motion perception.

Humans↗