Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

Abstract: Aspects of the invention relate to generating an emission activity image as well as an emission attenuation map using an iterative updation based on both the raw emission projection data and the raw radiography projection data, and an optimization function. The outputs include an optimized emission activity image, and at least one of an optimized emission attenuation map or an optimized radiography image. In some aspects an attenuated corrected emission activity image is obtained using the optimized emission activity image, and the optimized emission attenuation map.

Abstract: Systems and methods of classifying component tissues of magnetic resonance images, where the method includes performing a proton density weighted, short echo-time magnetic resonance imaging measurement over a first volume field-of-view region of interest (ROI), repeating a series refining the first volume field-of-view ROI into a plurality of subsequent smaller ROI volumes having respective smaller resolutions, reconstructing a complex image from the plurality of magnetic resonance imaging measurements, performing a bias correction on at least one of the plurality of subsequent smaller ROI volumes, and classifying the ROI volumes by tissue type based on the bias-corrected image signal, wherein at least one tissue type is bone. A non-transitory medium containing processor instructions and a system are disclosed.

Abstract: Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

Abstract: Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

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: Systems and methods of classifying component tissues of magnetic resonance images, where the method includes performing a proton density weighted, short echo-time magnetic resonance imaging measurement over a first volume field-of-view region of interest (ROI), repeating a series refining the first volume field-of-view ROI into a plurality of subsequent smaller ROI volumes having respective smaller resolutions, reconstructing a complex image from the plurality of magnetic resonance imaging measurements, performing a bias correction on at least one of the plurality of subsequent smaller ROI volumes, and classifying the ROI volumes by tissue type based on the bias-corrected image signal, wherein at least one tissue type is bone. A non-transitory medium containing processor instructions and a system are disclosed.

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.