Abstract: A method for assessing a treatment in a trial includes obtaining images generated from image data acquired at different times during a trial time period for a same region of interest of a subject. The treatment is administered to the subject for the trial. The method further includes co-registering the images and mapping the co-registered images to a reference image representing the region of interest. The method further includes generating a trial image of the region of interest showing at least one of structural or functional physiological changes that occurred during the trial time period based on the mapped co-registered images, and displaying the trial image.

Abstract: A method for assessing a treatment in a trial includes obtaining images generated from image data acquired at different times during a trial time period for a same region of interest of a subject. The treatment is administered to the subject for the trial. The method further includes co-registering the images and mapping the co-registered images to a reference image representing the region of interest. The method further includes generating a trial image of the region of interest showing at least one of structural or functional physiological changes that occurred during the trial time period based on the mapped co-registered images, and displaying the trial image.

Abstract: A method for assessing a treatment in a trial includes obtaining images generated from image data acquired at different times during a trial time period for a same region of interest of a subject. The treatment is administered to the subject for the trial. The method further includes co-registering the images and mapping the co-registered images to a reference image representing the region of interest. The method further includes generating a trial image of the region of interest showing at least one of structural or functional physiological changes that occurred during the trial time period based on the mapped co-registered images, and displaying the trial image.

Abstract: A method includes generating a kinetic parameter value for a VOI in a functional image of a subject based on motion corrected projection data using an iterative algorithm, including determining a motion correction for projection data corresponding to the VOI based on the VOI, motion correcting the projection data corresponding to the VOI to generate the motion corrected projection data, and estimating the at least one kinetic parameter value based on the motion corrected projection data or image data generated with the motion corrected projection data. In another embodiment, a method includes registering functional image data indicative of tracer uptake in a scanned patient with image data from a different imaging modality, identifying a VOI in the image based on the registered images, generating at least one kinetic parameter for the VOI, and generating a feature vector including the at least one generated kinetic parameter and at least one bio-marker.

Abstract: A nuclear imaging system includes a scanner (8), such as a PET scanner. A patient is injected with a [13N]ammonia radioisotope tracer which is contaminated with a small percent of 18F contamination. The scanner receives radiation from the injected tracer and a reconstruction processor (28) reconstructs the detected radiation into image representations. A calibration processor (16) generates an estimated decay curve based on the proton bombardment and a priori information about the tracer. An activity meter (42) measures radiation emitted from a sample of the tracer and a dose calibrator (44) determines a decay curve from the measured radiation. The detected radiation is corrected with one of the decay curves during reconstruction or a correction processor (50) corrects reconstructed images with one or both of the decay curves. A display (14) displays uncorrected reconstructed images and the decay curve and/or the corrected images.

Abstract: When detecting and classifying hypo-metabolic regions in the brain to facilitate dementia diagnosis, a patient's brain scan image, generated using an FDG-PET scan, is compared to a plurality of hypo-metabolic region patterns in brain scan images associated with a plurality of types of dementia. In a fully automated mode, the patient's scan is compared to all scans stored in a knowledge base, and a type of dementia associated with a most likely match is output to a user along with a highlighted image of the patient' s brain. In a semi-automated mode, a user specifies two or more types of dementia, and the patient's scan is compared to scans typical of the specified types. Diagnosis information including respective likelihoods for each type is then output to the user. Additionally, the user can adjust a threshold significance level to increase or decrease a number of voxels that are included in hypo-metabolic regions highlighted in the patient' brain scan image.

Abstract: A nuclear imaging system includes a scanner (8), such as a PET scanner. A patient is injected with a [13N]ammonia radioisotope tracer which is contaminated with a small percent of 18F contamination. The scanner receives radiation from the injected tracer and a reconstruction processor (28) reconstructs the detected radiation into image representations. A warning generator (12) generates warnings to the clinician concerning the effects of the 18F contamination. A calibration processor (16) generates an estimated decay curve based on a time since the end of the proton bombardment which created the tracer and a priori information about the tracer. An activity meter (42) measures radiation emitted from a sample of the tracer and a dose calibrator (44) determines a decay curve from the measured radiation.

Abstract: A method includes generating a kinetic parameter value for a VOI in a functional image of a subject based on motion corrected projection data using an iterative algorithm, including determining a motion correction for projection data corresponding to the VOI based on the VOI, motion correcting the projection data corresponding to the VOI to generate the motion corrected projection data, and estimating the at least one kinetic parameter value based on the motion corrected projection data or image data generated with the motion corrected projection data. In another embodiment, a method includes registering functional image data indicative of tracer uptake in a scanned patient with image data from a different imaging modality, identifying a VOI in the image based on the registered images, generating at least one kinetic parameter for the VOI, and generating a feature vector including the at least one generated kinetic parameter and at least one bio- marker.

Abstract: A method for assessing a treatment in a trial includes obtaining images generated from image data acquired at different times during a trial time period for a same region of interest of a subject. The treatment is administered to the subject for the trial. The method further includes co-registering the images and mapping the co-registered images to a reference image representing the region of interest. The method further includes generating a trial image of the region of interest showing at least one of structural or functional physiological changes that occurred during the trial time period based on the mapped co-registered images, and displaying the trial image.

Abstract: When detecting and classifying hypo-metabolic regions in the brain to facilitate dementia diagnosis, a patient's brain scan image, generated using an FDG-PET scan, is compared to a plurality of hypo-metabolic region patterns in brain scan images associated with a plurality of types of dementia. In a fully automated mode, the patient's scan is compared to all scans stored in a knowledge base, and a type of dementia associated with a most likely match is output to a user along with a highlighted image of the patient' s brain. In a semi-automated mode, a user specifies two or more types of dementia, and the patient's scan is compared to scans typical of the specified types. Diagnosis information including respective likelihoods for each type is then output to the user. Additionally, the user can adjust a threshold significance level to increase or decrease a number of voxels that are included in hypo-metabolic regions highlighted in the patient' brain scan image.