Abstract: A method for reducing, in an image, motion related imaging artifacts includes obtaining an image dataset of a region of interest, generating a plurality of intermediate images using the image dataset, applying a multivariate data analysis technique to the plurality of the intermediate images to generate motion information, sorting the intermediate images into a plurality of bins based on the motion information, and generating an image of the region of interest using at least one of the plurality of bins.

Abstract: The invention relates to a system and method for enhancing image data obtained from a positron emission tomography (PET) scan. In various embodiments, the method comprises transforming an original image data set to provide a first modified image data set by performing a masked volume-wise principal component analysis (MVW-PCA) on the original image data set. The first modified image data set is then transformed to provide a second modified image data set by performing a masked volume-wise independent component analysis (MVW-ICA) on the first modified image data set, the second modified image data set thereby comprising enhanced image data.

Abstract: A method for reducing, in an image, motion related imaging artifacts includes obtaining an image dataset of a region of interest, generating a plurality of intermediate images using the image dataset, applying a multivariate data analysis technique to the plurality of the intermediate images to generate motion information, sorting the intermediate images into a plurality of bins based on the motion information, and generating an image of the region of interest using at least one of the plurality of bins.

Abstract: A method and system are provided to mask out the background in dynamic PET images, to perform pre-normalization on the masked dynamic PET images, and to apply multivariate image analysis (e.g., principal component analysis PCA) on the masked pre-normalized dynamic PET images in order to improve the quality of the dynamic PET images and the PET study. A masking operation applies PCA to untreated dynamic PEET images before any pre-normalization in order to mask out the background pixels. This masking operation uses the Otsu method. A first normalization method is background noise pre-normalization where pixel values are corrected for background noise. A second normalization method is kinetic pre-normalization where the contrast within an image is improved. Multivariate analysis such as PCA may be applied on the whole volume to find the largest variance in the structure.

Abstract: A method and system are provided for improving the quality in positron emission tomography (PET) images. PET input data is masked using raw dynamic PET data (sinograms) as input for primary component analysis (PCA) that generates primary components which in turn are used to create a mask. This mask can be used to allow object pixel data to be extracted from the sinograms into masked sinograms where background pixels outside the reference object are set to zero. A volume-wise approach to PCA uses masked sinograms as input data. Pixel-wise noise pre-normalization may then be performed generating pre-normalized sinograms from the masked PET input data. PCA is then performed on the pre-normalized sinograms resulting in PCA sinograms recreated into PCA-modified sinograms by adding background pixel values of zero. These PCA-modified sinograms may optionally be scaled and may then be reconstructed into dynamic PET images with improved image quality.

Abstract: The invention relates to a system and method for enhancing image data obtained from a positron emission tomography (PET) scan. In various embodiments, the method comprises transforming an original image data set to provide a first modified image data set by performing a masked volume-wise principal component analysis (MVW-PCA) on the original image data set. The first modified image data set is then transformed to provide a second modified image data set by performing a masked volume-wise independent component analysis (MVW-ICA) on the first modified image data set, the second modified image data set thereby comprising enhanced image data.

Abstract: In a method and system for outlining a region of interest in a Positron Emission Tomography (PET) scan study, a processor may, based on application of Masked Volume Wise Principal Component Analysis (MVW-PCA) to a plurality of scan images, generate a PC2 image showing kinetic behavior of a particular part of a subject, in particular, the grey matter of the cerebellar cortex of the subject, and may outline, in the PC2 image, a region of the PC2 image having highest pixel intensity values of the PC2 image or of a portion thereof as a region of interest, and, in particular, as a reference region. The processor may generate a PC3 image showing kinetic behavior of a different part of the subject, in particular, blood vessels of the subject, import the outline into the PC3 image to determine the correctness of the outline, and modify the outline if it is incorrect.

Abstract: A method and system are provided for improving the quality in positron emission tomography (PET) images. Image quality may be improved by pre-normalizing dynamic PET images and then applying a multivariate analysis tool on the images to generate improved quality dynamic PET images. The dynamic PET images are the images reconstructed from the raw dynamic PET data in the image domain of the PET study. A first normalization method is a data treatment (also referred to as noise pre-normalization) for the negative values that may result from the image reconstruction and/or from random variations in detector readings. A second normalization method is background noise pre-normalization where background pixel values are masked. A third normalization method is kinetic pre-normalization where the contrast is improved to allow greater visualization of the activity in the image. Multivariate analysis such as PCA may then be applied to each slice of the dynamic PET images.

Abstract: A method and system are provided for improving the quality in positron emission tomography (PET) images. PET input data is masked using raw dynamic PET data (sinograms) as input for primary component analysis (PCA) that generates primary components which in turn are used to create a mask. This mask can be used to allow object pixel data to be extracted from the sinograms into masked sinograms where background pixels outside the reference object are set to zero. A volume-wise approach to PCA uses masked sinograms as input data. Pixel-wise noise pre-normalization may then be performed generating pre-normalized sinograms from the masked PET input data. PCA is then performed on the pre-normalized sinograms resulting in PCA sinograms recreated into PCA-modified sinograms by adding background pixel values of zero. These PCA-modified sinograms may optionally be scaled and may then be reconstructed into dynamic PET images with improved image quality.

Abstract: A method and system are provided to mask out the background in dynamic PET images, to perform pre-normalization on the masked dynamic PET images, and to apply multivariate image analysis (e.g., principal component analysis PCA) on the masked pre-normalized dynamic PET images in order to improve the quality of the dynamic PET images and the PET study. A masking operation applies PCA to untreated dynamic PEET images before any pre-normalization in order to mask out the background pixels. This masking operation uses the Otsu method. A first normalization method is background noise pre-normalization where pixel values are corrected for background noise. A second normalization method is kinetic pre-normalization where the contrast within an image is improved. Multivariate analysis such as PCA may be applied on the whole volume to find the largest variance in the structure.