Abstract: A method and apparatus is provided to correct for scatter in a positron emission tomography (PET) scanner, the scatter coming from both within and without a field of view (FOV) for true coincidences. For a region of interest (ROI), the outside-the-FOV scatter correction are based on attenuation maps and activity distributions estimated from short PET scans of extended regions adjacent to the ROI. Further, in a PET/CT scanner, these short PET scans can be accompanied by low-dose X-ray computed tomography (CT) scans in the extended regions. The use of short PET scans, rather than full PET scans, provides sufficient accuracy for outside-the-FOV scatter corrections with the advantages of a lower radiation dose (e.g., low-dose CT) and requiring less time. In the absence of low-dose CT scans, an atlas of attenuation maps or a joint-estimation method can be used to estimate the attenuation maps for the extended regions.

Abstract: A method and apparatus is provided to iteratively reconstruct an image from gamma-ray emission data by optimizing an objective function with a spatially-varying regularization term. The image is reconstructed using regularization term that varies spatially based on an activity-level map to spatially vary the regularization term in the objective function. For example, more smoothing (or less edge-preserving) can be imposed where the activity is lower. The activity-level map can be used to calculate a spatially-varying smoothing parameter and/or spatially-varying edge-preserving parameter. The smoothing parameter can be a regularization parameter ? that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter ? can depend on a sensitivity parameter. The edge-preserving parameter ? can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.

Abstract: An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.

Abstract: An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.

Abstract: A computer readable medium storing a program causing a computer to execute a process for adding image identification information is provided. The process includes: calculating first feature vectors for partial regions selected from a target image to be processed; and adding a piece of first identification information indicating content of the target image to the target image using a group of decision trees that are generated in advance on the basis of second feature vectors calculated for partial regions of a learning image and a piece of second identification information added to the entire learning image.

Abstract: A computer-readable medium storing a learning-model generating program causing a computer to execute a process is provided. The process includes: extracting feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image; generating learning models by using binary classifiers, the learning models being models for classifying the feature values and associating the identification information items and the feature values with each other; and optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased.

Abstract: A computer readable medium storing a program causing a computer to execute a process for adding image identification information is provided. The process includes: calculating first feature vectors for partial regions selected from a target image to be processed; and adding a piece of first identification information indicating content of the target image to the target image using a group of decision trees that are generated in advance on the basis of second feature vectors calculated for partial regions of a learning image and a piece of second identification information added to the entire learning image.