Columbia University

The State University of New York

Andrew F Laine

Ramin Parsey

Arthur Mikhno

Jerome Kalifa

Joseph John Mann

Elsa Angelini

Todd Ogden

Peter Esser

Elsa D Angelini

The present disclosure is directed to iterative regularized reconstruction methods. In certain embodiments, the methods incorporate locally-weighted total variation denoising to suppress artifacts induced by PSF modeling. In certain embodiments, the methods are useful for suppressing ringing artifacts while contrast recovery is maintained. In certain embodiments, the weighting scheme can be extended to noisy measures introducing a noise-independent weighting scheme. The present disclosure is also directed to a method for quantifying radioligand binding in a subject without collecting arterial blood. In certain embodiments, the methods incorporate using imaging data and electronic health records to predict one or more anchors, which are used to generate an aterial input function (AIF) for the radioligand.

Regularization of images

Treatment and mitigation or reduction of noise effects in noisy image data and data sets is described. Various aspects include treatment of noisy data with brushlet transforms and thresholding operations along with a favorable sequence of spatial and temporal processing and thresholding. Hard and minimax thresholding operators mitigate the noise in the image data. In medical applications this can be useful in removing noise that impairs diagnosis and treatment of patient conditions. In one application, cardiac function is better studied and understood through improved imaging of the heart and cardiac structures. In an exemplary case, a favorable sequence including spatial filtering using a brushlet filter, spatial thresholding of brushlet coefficients, then temporal filtering (first in the time domain then in the frequency domain) and thresholding of temporal coefficients yields an acceptable denoised image data set.

Spatio-temporal treatment of noisy images using brushlets

Techniques for tomographic reconstruction using non-linear diagonal estimators are disclosed. In a preferred embodiment, tomographic data from a tomographic device is decomposed in a wavelet vector family. Regularization of the tomographic data is accomplished by performing a thresholding operation on coefficients of the decomposed data. The regularized data is reconstructed into image data that may he displayed. In another preferred embodiment, a wavelet packet vector family is used to decompose the tomographic data. The wavelet packet is selected from a dictionary of wavelet packets using the best basis algorithm.

Method for tomographic reconstruction with non-linear diagonal estimators

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New York, NY, United States of America

Andrew F Laine