Abstract: A method for imaging a region of interest (ROI) within a body. The method includes applying ultrasound energy to the ROI, receiving ultrasound data for the ROI in response to the applied ultrasound energy, executing a moving window analysis on the received ultrasound data to generate a plurality of windows of information, applying a Renyi entropy signal receiver to each of the generated windows to generate Renyi entropy data, and presenting an image of the ROI based on the Renyi entropy data.

Abstract: A method for imaging a region of interest (ROI) within a body. The method includes applying ultrasound energy to the ROI, receiving ultrasound data for the ROI in response to the applied ultrasound energy, executing a moving window analysis on the received ultrasound data to generate a plurality of windows of information, applying a Renyi entropy signal receiver to each of the generated windows to generate Renyi entropy data, and presenting an image of the ROI based on the Renyi entropy data.

Abstract: A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2N frames, and the iteration is performed N?1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

Abstract: A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2N frames, and the iteration is performed N?1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

Abstract: A computationally efficient and accurate procedure for calculating radiation dose distributions within a volume of interest in a patient incorporating variations in patient density and source particle energy, position, direction, and type. The procedure iteratively simulates energy depositions from source particles using the Monte Carlo radiation transport method and then applies methods for reducing noise inherent in Monte Carlo results to obtain an accurate and computationally efficient representation of an actual radiation dose distribution compared to that produced by Monte Carlo methods alone. The invention makes an analogy between real measured data and transformed Monte Carlo generated computer data and then applies data denoising techniques to reduce the noise in the Monte Carlo dose images. Conversely, the present invention can produce a dose distribution having a predetermined noise level in a reduced amount of computation time.

Abstract: A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

Abstract: A computationally efficient and accurate procedure for calculating radiation dose distributions within a volume of interest in a patient incorporating variations in patient density and source particle energy, position, direction, and type. The procedure iteratively simulates energy depositions from source particles using the Monte Carlo radiation transport method and then applies methods for reducing noise inherent in Monte Carlo results to obtain an accurate and computationally efficient representation of an actual radiation dose distribution compared to that produced by Monte Carlo methods alone. The invention makes an analogy between real measured data and transformed Monte Carlo generated computer data and then applies data denoising techniques to reduce the noise in the Monte Carlo dose images. Conversely, the present invention can produce a dose distribution having a predetermined noise level in a reduced amount of computation time.