Abstract:
A method of evaluating tissue of an organ includes performing at least one of classification processing and clustering processing to obtain a processed dataset to visualize at least one of the imaging agent, blood, the contrast agent, and the biomedical agent distribution in the tissue, a relative regional uptake of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent in the tissue, relative regional flow of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent and the clearance or persistence of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent within the tissue and the characterization of elemental components of disease.

Description:
BACKGROUND OF THE INVENTION 
     This invention relates generally to methods and apparatus for Diagnostic Imaging (DI), and more particularly to methods and apparatus that provide for 3D and 2D relative perfusion viability. 
     When a patient comes into an emergency room or other clinical setting and is being evaluated for a possible heart attack there are usually three possible outcomes concerning myocardial health. 1. No disease (and a doctor is not going to treat the patient), 2. Positive disease (minor disease, and the doctor will typically treat with medication(s)), and 3. Positive disease (major disease, and the doctor typically treats with revascularization such as Coronary Artery Bypass Grafting (CABG) or angioplasty (AP)). 
     A simple Computed Tomography (CT) angiography can determine if there is no disease present. However, additional information is needed to determine the severity of the disease if disease is present. 
     CT uses both anatomical and functional methods to determine the perfusion and viability of the myocardium. CT also provides functional information about flow through microvasculature within the myocardium imaging following the injection of a contrast agent. This allows for the visualization of the perfusion or blood flow through regions of myocardium that may be affected. Regions lacking microvasculature flow show up as hypo-enhancement due to the lack of contrast agent flowing through that area. The large or moderate perfusion defects can be measured using nuclear imaging, but also can be evaluated with greater resolution using a simple low dose technique. This technique is based on the uptake of the contrast agent in the myocardium. Normal myocardium shows a more rapid uptake of contrast agent with a fairly rapid washout. The slightly damaged and less perfused tissue will gradually reach maximum uptake of contrast agent. However, there will be a time differential between the normal myocardium and the slightly injured myocardium. The more injured myocardium will never have maximum uptake of contrast agent due to lack of perfusion and it will take a greater amount of time to washout. 
     Additionally, a technique called delayed hyper-enhancement CT can be employed to reveal the extent of injured myocardium in dysfunctional myocardial tissue, hence the capability of recovering contractile function once blood flow delivering oxygen and substrates is restored, either spontaneously or following revascularization. In delayed hyper-enhancement, an additional agent is infused either continuously or as a bolus via an intravenous route and an image is taken 10-15 minutes following infusion. In normal myocardium, the infused contrast agent is excluded from intracellular compartments, however, in injured myocardium, the sarcolemmal membrane of myocytes become permeable allowing contrast agent to accumulate, which results in the observed hyper-enhancement. Thus, lack of contractile function (hypokinesia) and absence of hyper-enhancement (preserved integrity of the sarcolemmal membrane of myocytes) may indicate the presence of hibernating myocardium, which is likely to improve after revascularization of the artery supplying that particular territory. CT imaging using the above, described combination of anatomical and functional methods may reliably differentiate areas of hibernating (viable) from infracted (non-viable) myocardium following a heart attack. 
     Measuring the signal intensity changes allows one to evaluate for a possible perfusion deficit or hyper-enhancement, thereby indicating abnormal tissue. 
     When one images the myocardium, the peak uptake of the normal myocardium, which is measured by the timing bolus, there is the greatest differential between normal myocardium and damaged myocardium. For those pixels that have an intensity level in an overlap region as explained in greater detail below, the clustering algorithm herein described helps to discriminate which bin it belongs to. In other words, an isolated pixel or two in the overlap region may not be statistically significant. 
     BRIEF DESCRIPTION OF THE INVENTION 
     In one aspect, a method of evaluating tissue of an organ is provided. The method includes accessing image data from an imaging modality acquisition system wherein the image data includes at least one of a three dimensional single phase dataset and a three dimensional multi-phase dataset of a feature of interest in an organ including tissue wherein the data is acquired in conjunction with at least one of an imaging agent, blood, a contrast agent, and a biomedical agent. The method also includes performing at least one of statistical processing and clustering processing to obtain a processed dataset to visualize at least one of the imaging agent, blood, the contrast agent, and the biomedical agent distribution in the tissue, a relative regional uptake of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent in the tissue, relative regional flow of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent and the clearance or persistence of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent within the tissue. 
     In another aspect, a method of evaluating components of a disease is provided. The method includes accessing image data from any imaging modality system wherein the image data includes either a three dimensional, single or multi-phase dataset of data. The method also includes statistical or clustering processing to visualize and characterize the components of the tissue or organ for evaluation of disease. 
     In still another aspect, an imaging system includes an acquisition device configured to receive at least one of transmitted energy through a patient and emitted energy from a patient, and a computer coupled to the acquisition device. The computer is configured to do at least one of a) and b), wherein a) includes accessing image data from the acquisition device, wherein the image data includes a three dimensional, single or multi-phase dataset of data, and statistical or clustering processing to visualize and characterize the components of the tissue or organ for evaluation of disease. Wherein b) includes accessing image data from an imaging modality acquisition system wherein the image data includes at least one of a three dimensional single phase dataset and a three dimensional multi-phase dataset of a feature of interest in an organ including tissue wherein the data is acquired in conjunction with at least one of an imaging agent, blood, a contrast agent, and a biomedical agent, and perform at least one of statistical processing and clustering processing to obtain a processed dataset to visualize at least one of the imaging agent, blood, the contrast agent, and the biomedical agent distribution in the tissue, a relative regional uptake of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent in the tissue, relative regional flow of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent and the clearance or persistence of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent within the tissue. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an imaging modality acquisition system with an associated display. 
         FIG. 2  illustrates the different peak uptake of agent/blood in greatly injured myocardium, slightly injured myocardium, and normal myocardium. 
         FIG. 3  illustrates the overlap in CT number (in Hounsfield Units HU) from the different tissues as set forth with respect to  FIG. 2 . 
         FIG. 4  illustrates in part (a) a cluster image of a right coronary artery (RCA) occlusion in accordance with the herein described methods and apparatus, and in part (b) a standard axial image. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     There are herein provided clustering and classification methods and apparatus useful for imaging systems such as, for example, but not limited to a Computed Tomography (CT) System. The apparatus and methods are illustrated with reference to the figures wherein similar numbers indicate the same elements in all figures. Such figures are intended to be illustrative rather than limiting and are included herewith to facilitate explanation of an exemplary embodiment of the apparatus and methods of the invention. Although, described in the setting of CT, it is contemplated that the benefits of the invention accrue to all DI modalities including Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Electron Beam CT (EBCT), Single Photon Emission CT (SPECT), Ultrasound, optical coherence tomography, etc. 
       FIG. 1  illustrates an imaging modality acquisition system  10  with an associated display  20 . Imaging system  10  can be of any modality, but in one embodiment, system  10  is a CT system. In another embodiment, system  10  is a dual modality imaging system such as a combined CT/PET system and the below described clustering and statistical methods can be done in one modality (e.g., CT) and the processed data can be transferred to the other modality (e.g., PET). Display  20  can be separate from system  10  or integrated with system  10 . System  10  includes an acquisition device such as an x-ray radiation detector, a Gamma Camera, and/or an ultrasound probe or RF Coil. Note that in CT, EBCT, and ultrasound the acquisition device receives energy transmitted through the patient, but in PET and SPECT, the acquisition device receives energy emitted from the patient. In MRI, energy is transmitted and a passive signal from this is received. Common to all modalities is that an acquisition device receives energy regarding the patient or other scanned object. 
       FIG. 2  illustrates the different peak uptake of agent/blood in greatly injured myocardium, slightly injured myocardium, and normal myocardium.  FIG. 3  illustrates the overlap in CT number (in Hounsfield Units HU) from the different tissues as set forth with respect to  FIG. 2 .  FIG. 4  illustrates in part (a) a cluster image of a right coronary artery (RCA) occlusion in accordance with the herein described methods and apparatus, and in part (b) a standard axial image. 
     This herein described methods and apparatus are based on the premise that one must separate out minor differences in signal intensity between regions of the myocardium or other tissues. Damaged myocardium display as either hypo-enhanced or hyper-enhanced compared with normal myocardium. One can see in  FIG. 3  that some of the pixels overlap regions. This is where it becomes difficult to differentiate the region of tissue that a particular pixel belongs to. There are multiple methods to use for differentiating pixel intensities. Herein disclosed are protocols and methods of post-processing the images which will help determine the relative regional blood flow. 
     An image can be displayed on a color scale and a mean pixel intensity and standard deviation can be calculated and either a z-score or a T-test can be used to determine the probability that there is a difference in the values between the myocardium and the tissue in question. One can assign a pixel value by where it falls in a Gaussian or Normal distribution. 
     
       
         
           
             
               
                 
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                   Equation 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   1 
                 
               
             
           
         
       
     
     Additionally, the z-score/T-test can be run with or without the clustering algorithm to determine the probability that the injured myocardium is significantly different than the normal myocardium. The probabilities or the Z-score themselves can be mapped on an image. Mapping probabilities within the range of the myocardium or other types of tissues allows for improved visualization of the area of interest. 
     Cluster analysis divides data into groups (clusters) such that similar data objects (those of similar signal intensity) belong to the same cluster and dissimilar data objects to different clusters. The resulting data partition improves data understanding and reveals its internal structure. Partitional clustering algorithms divide up a dataset into clusters or classes, where similar data objects are assigned to the same cluster whereas dissimilar data objects should belong to different clusters. 
     The problem with using a simplistic method like K-means analysis is that choosing the initial centroids of the cluster will determine the outcome. 
     In medical applications there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for image data. Membership degrees between zero and one are used in fuzzy clustering instead of crisp assignments of the data to clusters. Fuzzy clustering allows one to calculate a membership function for which each pixel can belong. Each pixel is assigned a value to each cluster somewhere between 0 and 1. The object of the this type of clustering is to minimize the distance between each point and the centroid of the cluster. This is done through an iterative method as described in the equations below. 
     
       
         
           
             
               
                 
                   
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     Once a minimum distance is reached the maximum coefficients of each pixel can be displayed as an image.
 
max{| u   ij   k   −u   ij   k |}&lt;ε0&lt;ε1 where  k =iteration steps  Equation 4
 
     This is just one type of clustering analysis, which can be used to map out the Hounsfield units. Other methods of clustering algorithms can also be employed to this end. 
     The clustered and scored images can be displayed as either 2D or 3D datasets, which can be reformatted. The 2D sets can be read out in parallel with the other standard gray-scale datasets for comparison. Likewise, the 3D datasets could be placed as colored clusters on top of a transparent 3D dataset. This gives the relative placement of the infarct, which can be correlated with the vascular disease. In addition to, the number of pixels should be able to be counted so that size, transmurality and volume of infarct can be determined. This allows one to calculate the ischemic-burden with the myocardium and develop a scale, which will allow the physician to better determine the severity of the disease. 
     Furthermore, histogram analysis and/or a mean shift algorithm can be added to the post-processing to increase the speed of it and add additional information. Completing a quick determination of centroid location can improve the speed of the fuzzy clustering algorithm by starting the centroids close to their final location and therefore, reducing the amount of iterations needed to determine the optimal point where they converge to a minimum value. 
     Additionally, a method of visualization using the 3D dataset would allow for better visualization then using the 2D datasets. The data from the clustering and z-scoring method can be displayed as both a 3D rendered image as well as the reformatted 2D displayed images. This can include the clustered images acting as masks for the standard image data and displayed with a color scale in conjunction with the underlying grayscale image. This allows for more accurate assessment of the transmurality of the defects. The 3D datasets can then be mapped onto a “bulls-eye” to allow those physicians, who are familiar with nuclear medicine, to easily understand the location and extent of disease. 
     For the clustering, one embodiment uses fuzzy C-Means clustering and the following equation can be used: 
     
       
         
           
             
               
                 
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                   5 
                 
               
             
           
         
       
     
     where c=number of clusters, u=distance of a pixel from cluster centroid. 
     The membership of a pixel in a cluster is decided as: 
     
       
         
           
             
               
                 
                   
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                   6 
                 
               
             
           
         
       
     
     As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural said elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “one embodiment” of the present invention are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. 
     Also as used herein, the phrase “reconstructing an image” is not intended to exclude embodiments of the present invention in which data representing an image is generated but a viewable image is not. Therefore, as used herein the term, “image,” broadly refers to both viewable images and data representing a viewable image. However, many embodiments generate (or are configured to generate) at least one viewable image. 
     In one embodiment, system  10  includes a device for data storage, for example, a floppy disk drive, CD-ROM drive, DVD drive, magnetic optical disk (MOD) device, or any other digital device including a network connecting device such as an Ethernet device for reading instructions and/or data from a computer-readable medium, such as a floppy disk, a CD-ROM, a DVD or an other digital source such as a network or the Internet, as well as yet to be developed digital means. In another embodiment, the computer executes instructions stored in firmware (not shown). Generally, a processor is programmed to execute the processes described herein. Of course, the methods are not limited to practice in CT and system  10  can be utilized in connection with many other types and variations of imaging systems. In one embodiment, the computer is programmed to perform functions described herein, accordingly, as used herein, the term computer is not limited to just those integrated circuits referred to in the art as computers, but broadly refers to computers, processors, microcontrollers, microcomputers, programmable logic controllers, application specific integrated circuits, and other programmable circuits. Additionally, the computer is operationally coupled to the acquisition device. Although the herein described methods are described in a human patient setting, it is contemplated that the benefits of the invention accrue to non-human imaging systems such as those systems typically employed in small animal research. 
     Technical effects include obtaining relative perfusion information without the extra dose from a scan. The ability to retrieve flow information with additional dose from extra scans. The ability to provide vascular information at the same time it is providing functional information. And, the visualization and review capabilities in 2 &amp; 3 dimensions for more accurate assessment of the myocardial state. 
     Exemplary embodiments are described above in detail. The assemblies and methods are not limited to the specific embodiments described herein, but rather, components of each assembly and/or method may be utilized independently and separately from other components described herein. 
     While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims.