Biomedical subjects

Partha Pratim Mondal

Publications and source records attributed to Partha Pratim Mondal.

3 recordsLinked to original sources

PubMed2006

Fuzzy logic and maximum a posteriori-based image restoration for confocal microscopy.

We propose a maximum a posteriori image restoration approach to 3D confocal microscopy. The image field is suitably modeled as a Markov random field, resulting in a Gibbs distributed image. A fuzzy-logic-based potential is employed in the Gibbs prior. Unlike other potentials, the fuzzy potential distinguishes intensity variation due to genuine edges and noise. The proposed approach has generated artifact-free restored confocal microscopy images.

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PubMed2005

Kynurenate treatment of autaptic hippocampal microcultures affect localized voltage-dependent calcium diffusion in the dendrites.

It is not clear how different spatial compartments in the neuron are affected during epileptiform activity. In the present study we have examined the spatial and temporal profiles of depolarization induced changes in the intracellular Ca(2+) concentration in the dendrites of cultured autaptic hippocampal pyramidal neurons rendered epileptic experimentally by treatment with kynurenate (2 mM) and Mg(2+) (11.3 mM) in culture (treated neurons). This was examined with simultaneous somatic patch-pipette recording and Ca(2+) imaging experiments using the Ca(2+) indicator Oregon Green 488 BAPTA-1. Neurons stimulated by depolarization under whole-cell voltage clamp conditions revealed Ca(2+) entry at localized sites in the dendrites. Ca(2+) transients were observed even in the presence of NMDA and AMPA receptor antagonists suggesting that the opening of voltage gated calcium channels primarily triggered the local Ca(2+) changes. Peak Ca(2+) transients in the dendrites of treated neurons were larger compared to the signals recorded from the control neurons. Dendritic Ca(2+) transients in treated neurons showed a distance dependent scaling. Estimation of dendritic local Ca(2+) diffusion coefficients indicated higher values in the treated neurons and a higher availability of free Ca(2+). Simulation studies of Ca(2+) dynamics in these localized dendritic compartments indicate that local Ca(2+) buffering and removal mechanisms may be affected in treated neurons. Our studies indicate that small dendritic compartments are rendered more vulnerable to changes in intracellular Ca(2+) following induction of epileptiform activity. This can have important cellular consequences including local membrane excitability through mechanisms that remain to be elucidated.

Animals↗

PubMed2005

Neural network-based image reconstruction for positron emission tomography.

Positron emission tomography (PET) is one of the key molecular imaging modalities in medicine and biology. Penalized iterative image reconstruction algorithms frequently used in PET are based on maximum-likelihood (ML) and maximum a posterior (MAP) estimation techniques. The ML algorithm produces noisy artifacts whereas the MAP algorithm eliminates noisy artifacts by utilizing availableprior information in the reconstruction process. The MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class and irrespective of the strength of interaction between the nearest neighbors. A Hebbian neural learning scheme is proposed to model the nature of interpixel interaction to reconstruct artifact-free edge preserving reconstruction. A key motivation of the proposed approach is to avoid oversmoothing across edges that is often the case with MAP algorithms. It is assumed that local correlation plays a significant role in PET image reconstruction, and proper modeling of correlation weight (which defines the strength of interpixel interaction) is essential to generate artifact-free reconstruction. The Hebbian learning-based approach modifies the interaction weight by adding a small correction that is proportional to the product of the input signal (neighborhood pixels) and output signal. Quantitative analysis shows that the Hebbian learning-based adaptive weight adjustment approach is capable of producing better reconstructed images compared with those reconstructed by conventional ML and MAP-based algorithms in PET image reconstruction.

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