The assignee for this patent application is The
Reporters obtained the following quote from the background information supplied by the inventors: "Perfusion refers to capillary-level blood flow in tissues and describes the process of blood delivery through capillary beds of a volume of tissue over time. To non-invasively measure tissue perfusion, a tracer is typically injected and an imaging modality such as positron emission tomography (PET), magnetic resonance imaging (MRI), or computed tomography (CT), is used to detect the tracer. Perfusion parametric maps (the correlation of the imaging data to the biological feature or function) are generated using dynamic evaluation curves. A dynamic evaluation curve represents the tracking of the tracer in a certain region along a dynamic imaging sequence as a function of time.
"For PET imaging, the dynamic evaluation curve is the time-activity curve (PET-TAC); for MRI imaging, it is the time-intensity curve (TIC); and for CT imaging, it is the time-attenuation curve (CT-TAC). In the various imaging modalities, the dynamic evaluation curves generally involve the tracer kinetics of baseline, wash-in, wash-out and steady state (the 'tracer kinetic model'), which are presented according to the imaging modalities, imaging protocols, and tracer properties. A tracer kinetic model can be used to estimate biological parameters through fitting a mathematical model to the dynamic evaluation curve of a pixel or a region of interest (ROI), for example, based on the change of pixel intensities over the dynamic imaging sequence.
"The perfusion parametric maps generated by the dynamic evaluation curves of an imaging modality demonstrate blood distribution and tracer clearance rate with parameters such as tissue blood flow (TBF), blood volume (TBV) and mean transit time (MTT). TBF is defined as volume of blood moving through a given vascular network in a tissue per unit time, with a unit of milliliters of blood per 100 g of tissue per minute (ml/min/100 g). TBV is defined as total volume of flowing blood within vascular network, with a unit of milliliters of blood per 100 g of tissue (ml/100 g). MTT is defined as average transit time of all blood elements entering arterial input and leaving at venous output of vascular network, with a unit of second (s).
"The quantitative analysis of parametric perfusion maps relies on accurate determination of the Arterial Input Function (AIF), which indicates the concentration of a tracer in a blood pool within blood feeding areas to the voxels of interest at a certain time. A blood pool refers to an amount of blood in a region. A blood feeding area refers to arteries, veins, and the like, which enable blood transport. A voxel refers to a volumetric pixel, which is effectively a three-dimensional (3D) pixel represented, for example, as a cube in 3D space.
"Currently, most medical practitioners and researchers select AIF areas manually, by visual inspection of the dynamic evaluation curves in the regions containing the blood pool. However, the manual selection process requires specially trained operations and the results may vary with observers. Moreover, the complicated structures in some tissues--such as brain--can make the detection of the AIF areas difficult due to the scattered distribution of arteries. In addition, manual selection of a global AIF in 3D can be even harder because practitioners and researchers have to select the AIF in each single slice and then combine the selections together. This process can easily lose consistency across the entire 3D volume as well as causing a large effort and cost of time and labor.
"Accordingly, an automated AIF determination would be helpful in assessing results of a perfusion study."
In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors' summary information for this patent application: "Embodiments of the invention provide tools and techniques for automated arterial input function (AIF) selection used in producing parametric perfusion maps displayed for assisting diagnosis of physiological changes of a patient.
"According to one aspect, any imaging modality providing perfusion imaging data containing characteristic parameters associated with a dynamic evaluation curve can be used.
"According to an embodiment, a dynamic evaluation curve for each pixel in each slice of imaging data is produced to extract characteristic parameters. The characteristic parameters can include time to peak, maximum slope, and maximum enhancement. In some embodiments, the characteristic parameters being extracted can further include wash-out slope and time to wash-out. Based on the extracted parameters (e.g., time to peak, maximum slope, maximum enhancement, and, optionally, wash-out slope and time to wash-out), pattern recognition and classification can be carried out.
"The pattern recognition can include generating two-dimensional (2D) plots based on the extracted parameters. The 2D plots can include a plot of maximum slope vs. time to peak (S vs. T); maximum enhancement vs. time to peak (E vs. T); and, optionally, wash-out slope vs. time to wash-out (W vs. T). For classification, a peak and valley determination can be made with respect to the 2D plots. The data points related to the peaks and valleys can then be used to select the pixels indicating AIF areas.
"In one embodiment, the pixels can be selected as indicating AIF areas if the maximum enhancement is greater than the mean enhancement at a point of a peak in a phase of interest on the E vs. T curve; and the maximum slope is greater than the mean slope at a point of a peak in a phase of interest on S vs. T curve; and, when included as part of the characteristic parameters, a wash-out slope is greater than a mean wash-out slope at a point of a peak on the W vs. T curve; and a time to peak is within the peaks on the E vs. T curve and the S vs. T curve; and a time to wash-out is within the peak on the W vs. T curve.
"This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
"FIG. 1 shows a process flow for perfusion analysis in which an AIF selector according to an embodiment of the invention can operate.
"FIGS. 2A-2C show example dynamic evaluation curves for PET (FIG. 2A), MRI (FIG. 2B), and CT (FIG. 2C).
"FIG. 3 shows a process flow diagram of a method of selecting AIF areas according to an embodiment of the invention.
"FIGS. 4A and 4B show an example time-attenuation curve for a CT study, indicating extraction of characteristic parameters.
"FIGS. 5A-5C show detailed process flow diagrams of an example method of selecting AIF areas.
"FIG. 6 shows an example AIF selection using parameters extracted from imaging data.
"FIGS. 7A and 7B show the difference between the CT-TAC for AIF areas and the surrounding tissues for two example cases.
"FIGS. 8A and 8B show an example S vs. T curve and E vs. T curve, respectively.
"FIG. 9A illustrates an example of the refined potential peaks selected through the peak validator and the potential valleys determined by the upward zero-crossing method.
"FIG. 9B illustrates an example of the real peaks and real valleys selected through the peaks and valleys determiner.
"FIG. 10 shows an example computing system for a perfusion analysis system in which embodiments of the invention may be carried out.
"FIGS. 11A and 11B respectively show a 2D plot of S v. T and E v. T for a before-infarcted study of an experiment.
"FIGS. 11C and 11D respectively show a 2D plot of S v. T and E v. T for an after-infarcted study of an experiment.
"FIGS. 12A and 12B respectively illustrate the automated selection of potential peaks and valleys (FIG. 12A) and the real peaks and valleys (FIG. 12B) for the before-infarcted study of an experiment.
"FIGS. 12C and 12D respectively illustrate the automated selection of potential peaks and valleys (FIG. 12C) and the real peaks and valleys (FIG. 12D) for the after-infarcted study of an experiment.
"FIGS. 13A and 13B show binary images of the results of the automated detection of AIF pixels for the before-infarcted study and after-infarcted study, respectively.
"FIGS. 14A and 14B show the average TACs of selected AIF pixels for the before-infarcted study and after-infarcted study, respectively.
"FIGS. 15A and 15B show example original anatomical images for the before-infarcted study and the after-infarcted study, respectively.
"FIGS. 16A and 16B show perfusion maps for the before-infarcted study and the after-infarcted study, respectively.
"FIGS. 16C and 16D show 3D perfusion volumes for the before-infarcted study and the after-infarcted study, respectively.
"FIGS. 17A-17C respectively show a 2D plot of S vs. T, E vs. T, and W vs. T for an abdominal perfusion study experiment.
"FIGS. 18A-18B show an example of the automated process on an S vs. T curve.
"FIGS. 19A-19B show an example of the automated process on an E vs. T curve.
"FIGS. 20A-20B show an example of the automated process for a W vs. T curve.
"FIG. 21 shows a 3D AIF region of an artery resulting from the automated process of the example.
"FIG. 22 shows an average PET-TAC for pixels in an AIF region.
"FIGS. 23A and 23B show perfusion maps of the kidneys and upper GI.
"FIGS. 24A and 24B show the fused perfusion maps with CT anatomy images.
"FIG. 25 shows a 3D perfusion volume."
For more information, see this patent application: LENOX, MARK W.;
Keywords for this news article include: Perfusion, The
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