Wisconsin Alumni Research Foundation

Richard Kijowski

Alan McMillan

Darryl Gerard Thelen

Hyungseok Jang

Tyler Bradshaw

Fang Liu

Kwang Won Choi

Chihwa Song

The present disclosure includes systems and methods for creating positron emission tomography (PET) images. The method includes receiving at least one PET image of a subject created from PET data acquired from the subject, creating an attenuation correction map using the at least one PET image, and reconstructing PET data using the attenuation correction map and the at least one PET image to generate an attenuation corrected PET image.

Deep learning based data-driven approach for attenuation correction of pet data

Systems and methods for segmenting a medical image are provided. In some embodiments, the method includes receiving an image acquired from at least a portion of a subject's anatomy, selecting a first point within a target structure identified on the image, and determining a second and a third point associated with extents of a target biological tissue, the first, second and third points defining a sector described by the first point, a central angle and a subtended arc. The method also includes determining a plurality of arc points along the subtended arc, and extending a plurality of linear segments along directions determined by the first point and the plurality of arc points. The method further includes determining an intensity profile along each linear segment to identify boundaries of the target biological tissue, and generating a contour of the target biological tissue using the identified boundaries.

Systems and methods for semi-automated segmentation of medical images

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Verona, WI, United States of America

Richard Kijowski