Abstract: A method for automatically measuring interventricular septum thickness is provided. The method in one embodiment comprises acquiring a series of ultrasound images of a heart, acquiring a septum mask by applying a septum segmentation algorithm on the series of ultrasound images, localizing a mitral valve tip using a valve tip localization algorithm and calculating the thickness of the interventricular septum using an interventricular septum thickness algorithm. The interventricular septum thickness algorithm uses the septum mask and the localized mitral valve tip as inputs.

Abstract: A method for generating a positron emission tomography (PET) attenuation correction map. The method includes obtaining a magnetic resonance (MR) image dataset of a subject of interest, obtaining a positron emission tomography (PET) emission dataset of the subject of interest, segmenting the MR image dataset to identify at least one object of interest, determining a volume of the object of interest, and generating a PET attenuation correction map using the determined volume.

Abstract: Exemplary embodiments of the present disclosure are directed to correcting lung density variations in positron emission tomography (PET) images of a subject using a magnetic resonance (MR) image. A pulmonary vasculature and an outer extent of a lung cavity can be identified in a MR image corresponding to a thoracic region of the subject in response to an intensity associated with pixels in the MR image. The pixels within the outer extent of the lung cavity are classified as corresponding to the pulmonary vasculature or the lung tissue. Exemplary embodiments of the present disclosure can apply attenuation coefficients to a reconstruction of the PET image based on the classification of the pixels within the outer extent of the lung cavity.

Abstract: A method for generating an image is provided. The method comprises: acquiring a first set of image data using a first imaging modality; sorting the first set of image data into a plurality of gates to generate a plurality of gated data sets; reconstructing each gated data set to generate a respective gated image for each gated data set; registering the respective gated images to generate a plurality of registered images; and generating a median image from the plurality of registered images, wherein each voxel of the median image is a respective median value of the corresponding voxels of the plurality of registered images.

Abstract: In one embodiment, a method includes performing a magnetic resonance (MR) imaging sequence to acquire MR image slices or volumes of a first station representative of a portion of a patient; applying a first phase field algorithm to the first station to determine a body contour of the patient in the first station; identifying a contour of a first anatomy of interest within the body contour of the first station using the first phase field algorithm or a second phase field algorithm; segmenting the first anatomy of interest based on the identified contour of the first anatomy of interest; correlating first attenuation information to the segmented first anatomy of interest; and modifying a positron emission tomography (PET) image based at least on the first correlated attenuation information.

Abstract: A method for automatic mismatch correction is presented. The method includes identifying a feature of interest in a reference image volume and a target image volume. Furthermore, the method includes computing a cost matrix based on one or more pairs of image slices in the reference image volume and the target image volume. The method also includes identifying one or more longest common matching regions in the reference image volume and the target image volume based on the computed cost matrix. In addition, the method includes aligning the reference image volume and the target image volume based on the identified one or more longest common matching regions. A non-transitory computer readable medium including one or more tangible media, where the one or more tangible media include code adapted to perform the method for automatic mismatch correction is also presented.

Abstract: A method for automatic mismatch correction is presented. The method includes identifying a feature of interest in a reference image volume and a target image volume. Furthermore, the method includes computing a cost matrix based on one or more pairs of image slices in the reference image volume and the target image volume. The method also includes identifying one or more longest common matching regions in the reference image volume and the target image volume based on the computed cost matrix. In addition, the method includes aligning the reference image volume and the target image volume based on the identified one or more longest common matching regions. A non-transitory computer readable medium including one or more tangible media, where the one or more tangible media include code adapted to perform the method for automatic mismatch correction is also presented.

Abstract: A method for generating an image is provided. The method comprises: acquiring a first set of image data using a first imaging modality; sorting the first set of image data into a plurality of gates to generate a plurality of gated data sets; reconstructing each gated data set to generate a respective gated image for each gated data set; registering the respective gated images to generate a plurality of registered images; and generating a median image from the plurality of registered images, wherein each voxel of the median image is a respective median value of the corresponding voxels of the plurality of registered images.

Abstract: A method for automatically measuring interventricular septum thickness is provided. The method in one embodiment comprises acquiring a series of ultrasound images of a heart, acquiring a septum mask by applying a septum segmentation algorithm on the series of ultrasound images, localizing a mitral valve tip using a valve tip localization algorithm and calculating the thickness of the interventricular septum using an interventricular septum thickness algorithm. The interventricular septum thickness algorithm uses the septum mask and the localized mitral valve tip as inputs.

Abstract: An ultrasound system includes a transducer array comprising a multiplicity of transducer elements configured to acquire image data of an object, a display system for displaying an image of the object based on the acquired image data, and an image processor module. The image processor module is programmed to calculate the curvature of the image, and identify an object feature based on the calculated curvature and based on known feature tendencies of the object.

Abstract: A method for segmenting a digitized medical image including providing a digitized image comprising a plurality of intensities corresponding to a domain of points on a n-dimensional grid, modeling the segmentation phases of said image with 2k weighting functions defined as a product of k unknown auxiliary functions, minimizing an energy functional of said weighting functions and said auxiliary functions to derive k gradient flow equations for said auxiliary functions, numerically integrating said gradient flow equations wherein solutions for the k unknown auxiliary functions are obtained, and partitioning said image into segments based on a membership of each image point in each of said weighting functions.

Abstract: A method for segmenting a digitized medical image including providing a digitized image comprising a plurality of intensities corresponding to a domain of points on a n-dimensional grid, modeling the segmentation phases of said image with 2k weighting functions defined as a product of k unknown auxiliary functions, minimizing an energy functional of said weighting functions and said auxiliary functions to derive k gradient flow equations for said auxiliary functions, numerically integrating said gradient flow equations wherein solutions for the k unknown auxiliary functions are obtained, and partitioning said image into segments based on a membership of each image point in each of said weighting functions.