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An important postprocessing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved using a quasi-Monte Carlo method that takes into...
An efficient, two-dimensional, analytic, Spline Reconstruction Technique (SRT) has been presented earlier in the literature. This technique involves the Hilbert transform of the sinogram which is approximated in terms of natural cubic splines. The aim of this study is to evaluate the SRT algorithm using Monte-Carlo simulated sinograms and real PET data, in comparison with three commonly used reconstruction...
In this paper, a novel structured noises-reduction technique for OI data is proposed. Canonical correlation analysis (CCA) technique is exploited to separate the underlying independent sources among which the neural response signal is picked out by the correlation analysis. The white noise (WN) criterion is applied to discern the structured components from the unstructured ones. The energy of structured...
We discuss here the use of spectral estimation algorithms for biomedical ultrasound imaging. B-mode ultrasound imagery is based on computing the envelope of acoustic individual radio-frequency (RF) lines. An envelope estimator is thus needed to improve spatial resolution and reduce speckle. There is an analogy between spectral analysis and envelope estimation. Indeed, the signal envelope can be seen...
Traditional magnetic resonance imaging (MRI) studies are based on image contrast and qualitative analysis. However, there is an increasing interest in quantifying the physical parameters of the object such as the free induction decay rate, T*2 . In this paper, a new Bayesian algorithm is proposed for the estimation of T*2 from gradient echo MRI scans. Current estimation methods use a simple signal...
Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult. In this paper a Bayesian denoising algorithm for Poisson data is presented where two...
This report evaluates several methods to estimate blood perfusion and residue functions in dynamic contrast enhanced (DCE) MRI. Among these are model-dependent and model-independent techniques. All methods were applied to series of Monte Carlo simulations to evaluate the accuracy in order to reproduce different underlying vascular residue functions and blood perfusions. Of the model-independent approaches...
Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses...
Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing...
In this paper, we investigate the performance of time-of-flight (TOF) PET in improving lesion detectability. We present a theoretical approach to compare lesion detectability of TOF versus non-TOF systems. Computer simulations are performed to validate the theoretical predictions. A TOF PET tomograph is simulated using the SimSET software. Images are reconstructed from list-mode data using a maximum...
Functional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our...
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