Abstract: Methods and systems are provided for interpolating acquired data of a tracer distribution. In one embodiment, a method comprises reconstructing the acquired data, and interpolating the reconstructed data with a surface, wherein a slope of the surface at a data point of the reconstructed data is limited by a value of the data point. In this way, interpolation artifacts around objects with high tracer density may be avoided.

Abstract: Methods and systems are provided for interpolating acquired data of a tracer distribution. In one embodiment, a method comprises reconstructing the acquired data, and interpolating the reconstructed data with a surface, wherein a slope of the surface at a data point of the reconstructed data is limited by a value of the data point. In this way, interpolation artifacts around objects with high tracer density may be avoided.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: A system and method for segmenting and editing anatomical objects from medical images is disclosed. The system may be a medical diagnostic imaging system. A computer unit may execute computer software for segmenting anatomical objects from medical images. The computer software may extract an anatomical object from planar curves. The computer software may extract an anatomical object by computing the centerline of the anatomical object. A set of reformatted images may then be derived based on the centerline of the object. A user may then provide input in the form of user drawn contours on a plurality of reformatted images. In an embodiment the reformatted images may include a reformatted longitudinal view, a curved reformatted view, or a lumen view. The user drawn contours may correspond to the boundaries of the anatomical object to be segmented. The anatomical object may then be extracted based on the user drawn contours.