REGULARIZATION OF IMAGES

摘要

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.

主权项

1. A computer implemented method for reconstructing positron emission tomography (PET) or single-photon emission computed tomography (SPECT) image data, the method comprising:
(a) generating a PET or a SPECT scan acquisition comprising a plurality voxels corresponding to an anatomical structure of a subject administered with a radioligand, wherein the scan acquisition comprises:
(i) a transmission sinogram comprising the plurality voxels corresponding to the anatomical structure of the subject;(ii) an emission sinogram comprising detected coincidence events emitted from the anatomical structure of the subject; and(iii) a normalization scan; (b) calculating a weight for each voxel of the scan acquisition using iterative reconstruction to generate a 3D weight map; and (c) performing at least one of a Total Variation-Point Spread Function-Maximum Likelihood Expectation Maximization (TV-PSF-MLEM) reconstruction, or a Total Variation-gradual Point Spread Function-Maximum Likelihood Expectation Maximization (TV-gPSF-MLEM) reconstruction with the 3D weight map to generate a reconstructed image.