Abstract: The speed of sound data corresponding to transmission of ultrasound through a cancerous lesion is different than the speed of sound data corresponding to transmission of ultrasound through a benign lesion. The system can assign a coloration to a speed of sound image according to the speed of sound through the tissue as obtained from quantitative transmission ultrasound. The shape indicative of a lesion can be identified through the reflection data with the type of lesion identifiable by the coloration from the speed of sound data.

Abstract: The speed of sound data corresponding to transmission of ultrasound through a cancerous lesion is different than the speed of sound data corresponding to transmission of ultrasound through a benign lesion. The system can assign a coloration to a speed of sound image according to the speed of sound through the tissue as obtained from quantitative transmission ultrasound. The shape indicative of a lesion can be identified through the reflection data with the type of lesion identifiable by the coloration from the speed of sound data.

Abstract: A method for quantitative assessment of breast density can include separating breast voxels and exterior voxels from an image of a patient's breast; and for the breast voxels identified in the image, identifying tissue having high speed value from other tissue. The estimated breast density can be calculated based on the percentage of non-skin breast voxels corresponding to fibroglandular tissue density within the breast.

Abstract: Treatment for a variety of diseases often requires guidance for the delivery of either a drug or radiation to the disease site. Positron Emission Tomography (PET) can provide three dimensional positioning of the location of positron emitting radioisotopes that can mark a disease site. However, the inversion of the raw emission projection data into a 3D volume is computationally intensive, and this results in a low update or frame rate. In order to be useful in either guiding a surgeon, or some other automated feedback approach, the update/frame rate must be of sufficient speed that the user can effectively control the process. This approach provides a substantial improvement to the frame rate by taking advantage of iterative reconstruction methodologies to shortcut the reconstruction process.

Abstract: Automatic arterial input function (AIF) area determination is provided that can be used to facilitate the generation of parametric maps for perfusion studies based on various imaging modalities and covering a variety of tissues. Automatic AIF determination can be accomplished by extracting characteristic parameters such as maximum slope, maximum enhancement, time to peak, time to wash-out, and wash-out slope. Characteristic parameter maps are generated to show relationships among the extracted characteristic parameters, and the characteristic parameter maps are converted to a plurality of two-dimensional plots. Automated segmentation of non-AIF tissues and determination of AIF areas can be accomplished by automatically finding peaks and valleys of each phase of AIF areas on the plurality of two-dimensional plots.

Abstract: Systems and methods of performing parametric imaging are provided. According to an embodiment, data from multiple sources are combined using parametric equations to assign a probability value to each of a known set of diagnoses in a database. The system receives available data and applies the parametric equations based on the available data. As more data becomes available, the parametric equations can be reapplied to update the parameterized image. The parameterized image can be displayed overlaid on an anatomical image within a graphical interface.

Abstract: A PET event position calculation method using a combination angular and radial event map wherein identification of the radial distance of the event from the centroid of the scintillation crystal with which the event is associated as well as angular information is performed. The radial distance can be converted to a statistical confidence interval, which information can be used in downstream processing. More sophisticated reconstruction algorithms can use the confidence interval information selectively, to generate higher fidelity images with higher confidence information, and to improve statistics in dynamic imaging with lower confidence information.

Abstract: A representative positron emission tomography (PET) calibration system includes a PET scanner having a ring detector, a phantom that is placed at approximately the center of the ring detector, and a time alignment calibration manager that is coupled to the PET scanner. The time alignment calibration manager detects coincidence events from the phantom, calculates position of time of flight events from the ring detector based on the detected coincidence events, and calculates time offsets for the ring detector using a mean value calculation based on the calculated position of the time of flight events.

Abstract: A representative positron emission tomography (PET) calibration system includes a PET scanner having a ring detector, a phantom that is placed at approximately the center of the ring detector, and a time alignment calibration manager that is coupled to the PET scanner. The time alignment calibration manager detects coincidence events from the phantom, calculates position of time of flight events from the ring detector based on the detected coincidence events, and calculates time offsets for the ring detector using a mean value calculation based on the calculated position of the time of flight events.

Abstract: A PET event position calculation method using a combination angular and radial event map wherein identification of the radial distance of the event from the centroid of the scintillation crystal with which the event is associated as well as angular information is performed. The radial distance can be converted to a statistical confidence interval, which information can be used in downstream processing. More sophisticated reconstruction algorithms can use the confidence interval information selectively, to generate higher fidelity images with higher confidence information, and to improve statistics in dynamic imaging with lower confidence information.

Abstract: A crystal lookup table used to define a matching relationship between a signal position of a detected event in a PET scanner and a corresponding detector pixel location is generated using a neural network-based algorithm, and is implemented by a FPGA.

Abstract: A PET event position calculation method using a combination angular and radial event map wherein identification of the radial distance of the event from the centroid of the scintillation crystal with which the event is associated as well as angular information is performed. The radial distance can be converted to a statistical confidence interval, which information can be used in downstream processing. More sophisticated reconstruction algorithms can use the confidence interval information selectively, to generate higher fidelity images with higher confidence information, and to improve statistics in dynamic imaging with lower confidence information.

Abstract: Emission contamination data are collected in a shifted mock scan simultaneous with the collection of transmission data during a transmission scan of a patient with a collimated gamma point source, the transmission data are corrected with the emission contamination data, and the corrected transmission data are used for attenuation correction of emission data for reconstruction of an emission image of the patient. In a preferred implementation, when the point source is at a particular axial location and illuminates an axial beamwidth of “Fz” over the gamma detector, emission contamination data are collected from the gamma detector over an axial separated region “Fz?” having about the same axial extent but axially displaced by about half of the axial field of view (FOV).

Abstract: A crystal lookup table used to define a matching relationship between a signal position of a detected event in a PET scanner and a corresponding detector pixel location is generated using a neural network-based algorithm, and is implemented by a FPGA.