Abstract:
A method of imaging is presented. The method includes reconstructing image data acquired at a plurality of time intervals to obtain a plurality of images. Further, the method includes generating a mean image using the plurality of images. The method also includes correcting motion in the mean image or the plurality of images or both the mean image and the plurality of images by iteratively determining convergence of the mean image or the plurality of images or both the mean image and the plurality of images to generate a converged mean image, a converged plurality of images, or both a converged mean image and a converged plurality of images.
Abstract:
A method for determining the effectiveness of an image transformation process includes acquiring a four-dimensional (4D) image data set, sorting the 4D image data set into separate field-of-view bins using a temporal gating system generating a plurality of deformation vectors using the sorted 4D image data set, and using the plurality of deformation vectors to generate a transformation effectiveness value that is representative of the effectiveness of the image transformation process. The method further includes acquiring a respiratory signal, calculating a power spectrum of the respiratory signal, calculating a power spectrum for each of the plurality of deformation vectors, and comparing the power spectrum of the respiratory signal to the power spectrum of the plurality of deformation vectors to generate the transformation effectiveness value.
Abstract:
A method for determining the effectiveness of an image transformation process includes acquiring a four-dimensional (4D) image data set, sorting the 4D image data set into separate field-of-view bins using a temporal gating system generating a plurality of deformation vectors using the sorted 4D image data set, and using the plurality of deformation vectors to generate a transformation effectiveness value that is representative of the effectiveness of the image transformation process. The method further includes acquiring a respiratory signal, calculating a power spectrum of the respiratory signal, calculating a power spectrum for each of the plurality of deformation vectors, and comparing the power spectrum of the respiratory signal to the power spectrum of the plurality of deformation vectors to generate the transformation effectiveness value.
Abstract:
A method for imaging is presented. The method includes receiving a first image data set and at least one other image data set. Further the method includes adaptively selecting corresponding regions of interest in each of the first image data set and the at least one other image data set based upon apriori information associated with each of the first image data set and the at least one other image data set. Additionally, the method includes selecting a customized registration method based upon the selected regions of interest and the apriori information corresponding to the selected regions of interest. The method also includes registering each of the corresponding selected regions of interest from the first image data set and the at least one other image data set employing the selected registration method.
Abstract:
Methods and systems for processing a set of images are described. In accordance with this disclosure, images are registered and an analysis is performed in view of one or more constraints (such as constraints based upon anatomical or physiological considerations). Weighting factors are determined based on the analysis. The weighting factors are used in subsequent processing of the registered (and/or unregistered) images and/or to formulate a visualization that conveys the degree of confidence in the motion estimation used in the registration process.
Abstract:
Methods and systems for processing a set of images are described. In accordance with this disclosure, images are registered and an analysis is performed in view of one or more constraints (such as constraints based upon anatomical or physiological considerations). Weighting factors are determined based on the analysis. The weighting factors are used in subsequent processing of the registered (and/or unregistered) images and/or to formulate a visualization that conveys the degree of confidence in the motion estimation used in the registration process.