Abstract:
The analysis of the focal changes in the morphology of a tissue such as cartilage is completed through statistical parametric mapping by first detecting the amount of thickness changes; followed by the point by point estimation of the variance in the thickness delta estimation. Once the change and the variance are estimated, the z-map is computed. The z-map is used to compute single change parameters. i.e: volume significant change, area of significant change, average thickness of the significant changes, and D values from the probability distributions. That can be used for treatment decisions.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention is directed to the evaluation of osteoarthritis progression and more particularly to such evaluation using statistical parametric mapping to evaluate cartilage degradation. 
       DESCRIPTION OF RELATED ART 
       [0002]    The detection of structural changes is an important task in the evaluation of osteoarthritis progression. The cartilage tissue is one of the primary affected structures in OA which is a debilitating disease. The early detection of OA treatment efficacy requires monitoring of small changes in cartilage morphology. 
         [0003]    It has been reported that 70% of women and 60% of men aged 65 years or older suffer from OA. Therefore, a great deal of effort has been placed in the accurate quantification of cartilage morphological parameters such as volume, average thickness, and surface area. Cartilage tissue in normal subjects has very similar quantitative properties to OA subjects, and the evolution of those changes is very small. The problem is related to the focal nature of OA changes, which affect a small percentage of the cartilage tissue. Current approaches rely on carefully monitoring global cartilage parameters. However, they are not very sensitive to the detection of focal morphological changes in cartilage structure. 
         [0004]    Several attempts have been proposed to address the focal nature of the disease, from direct quantification to localized analysis of sub regions of the cartilage. On the other hand, the focal nature of the OA progression has also been addressed by semi-quantitative radiological scoring, but these techniques rely heavily on human expertise. Even standard evaluation of cartilage volume depends on human intervention, which tends to introduce error in the analysis. To minimize the requirements of an expert observer for the evaluation of cartilage changes, researchers have proposed automation; even still, full cartilage analysis can not be achieved with the desired precision to detect focal changes. Therefore, new approaches are required. 
         [0005]    In an unrelated field of endeavor, statistical parametric mapping (SPM) is a useful technique for the evaluation and localization of significant signals in medical imaging. Although SPM has been commonly used for the evaluation of activation regions in functional MRI, little effort has been made towards the application of SPM in the detection of structural changes in human anatomy. 
       SUMMARY OF THE INVENTION 
       [0006]    It is therefore an object of the present invention to present the use of SPM techniques for the detection, quantification and localization of structural cartilage changes with minimum user interaction and with just two time points. 
         [0007]    It is another object to reduce the error in quantitative MRI measurements due to the nature of human-computer performance. 
         [0008]    To achieve the above and other objects, in the present invention, the highly correlated nature of thickness measurements and a model behavior of random errors across areas of lower contrast are coupled to get an estimation of the thickness measurement errors. The error and the actual changes create a map of the probable location of cartilage defects that can be quantified for size. Furthermore, the approach creates probabilistic distributions of the changes that can be further explored to gain statistical insight of the changes in the cartilage tissue. 
         [0009]    The analysis of the focal changes in cartilage morphology is completed by first detecting the amount of thickness changes, followed by the point by point estimation of the variance in the thickness delta estimation. Once the change and the variance are estimated, the z-map is computed. The change in thickness requires a baseline to follow-up registration of their 3D reconstruction of the cartilage thickness maps. The registration is achieved using the maximization of the bone-cartilage interface overlap. The change in thickness is computed for every cartilage point. The variance estimation is computed using a uniform structural kernel on the non-regular 3D surface grid. The local standard deviation of the difference (SDD) is estimated using the local variance and the global root mean square (RMS) of the variance. Finally, the z-map is computed by dividing the thickness change by the SDD. The analysis of the SDD map is quantified to provide areas of significant change, the volume of the change and the average change in thickness. Furthermore, the probabilistic distribution functions (PDF) of the thickness change are computed. The PDF is further analyzed to provide evidence of the differences in the global behavior among the different cartilage components. 
         [0010]    Quantification of structural changes using SPM is a very useful technique for the evaluation of focal changes in cartilage morphology. The technique was evaluated with synthetic data and reduced the total system variability and improved the system ability to detect focal changes. It also provided the location and size of newly formed regions. The application of this concept in nine healthy volunteer data sets demonstrated that there is minimal change in cartilage morphology over one year. Although no major changes were observed, the delta map approach improved the precision and the ability to detect smaller changes in cartilage morphology. The improved precision was archived by the ability to map the baseline segmentation into the follow-up segmentation and look for changes on all the points that shared a common supporting area. The use of a common area reduces the errors in the definition of the extent of the cartilage surface. Furthermore, precision gains are achieved by the ability to compensate for magnet distortions. The distortions are compensated by adjusting the global cartilage shape to mimic one time point to the other time point. The ability to correct and map all the cartilage points to each other allows for the measurement of the thickness error and the estimation of areas with significant changes. The precision of those significant changes were better that the precision of the raw changes; hence the ability to detect changes is improved. On the other hand, the additional extracted information from the SPM was used to test differences among the distribution of two cartilage plates. The test in change among two distributions can be used to test the difference among two different groups, which is important when evaluating drug efficacy in newly designed disease modifying osteoarthritis drugs (DMOAD). 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    A preferred embodiment of the present invention will be set forth in detail with reference to the drawings, in which: 
           [0012]      FIG. 1  is a flow chart showing the process according to the preferred embodiment; 
           [0013]      FIG. 2  is a plot showing a model of the relative errors in boundary placement as a function of signal contrast; 
           [0014]      FIGS. 3A-3F  show an identification of significant changes in cartilage; 
           [0015]      FIG. 4  shows a slice of an MRI data set with possible areas of significant loss marked; 
           [0016]      FIGS. 5A and 5B  show renderings of a femoral cartilage baseline and a statistical parametric map, respectively; 
           [0017]      FIG. 6  is a plot showing a change in scale between baseline and follow-up scans; 
           [0018]      FIGS. 7A-7C  are plots showing relative changes of volume and thickness and the precision of the volume measurements, respectively; 
           [0019]      FIGS. 8A-8C  are plots showing the distribution of the change in thickness and its corresponding probabilistic distribution functions; 
           [0020]      FIGS. 9A and 9B  show the computation of distribution difference functions using continuous distribution functions; and 
           [0021]      FIG. 10  is a block diagram showing a system on which the preferred embodiment can be implemented. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0022]    A preferred embodiment of the present invention will be set forth in detail with reference to the drawings, in which like reference numerals refer to like elements or steps throughout. 
         [0023]    The data set used for the evaluation of the method consisted of a pair of scans from nine healthy volunteers between 31 and 71 years old (mean age of 44), five female and four male, with no clinical evidence of OA. One knee of each of the nine volunteers was scanned using a GE Sigma 1.5 T scanner (GE, Milwaukee, Wis.) at two time points: baseline scan and the one year follow-up. All volunteers consented to the study protocol which was previously approved by the Institutional Research Subjects Review Board. Two sets of images were acquired for each knee: first a sagittal 3D SPGR fat saturated sequence with a TR of 39, TE of 7.0 ms, Nex 1, Flip angle of 20°, and a 256 by 256 matrix with a 14.0 cm field of view. Second, a sagittal 3D GRE image with a TR of 29, TE of 15.0 sec, Nex 1, Flip angle of 40°, and a 256 by 256 matrix with a 14.0 cm field of view were acquired. 
         [0024]    The extraction of the cartilage tissue followed the methods described in Tamez-Peña J, Barbu-McInnis M, Totterman S, “Knee Cartilage Extraction and Bone-Cartilage Interface Analysis from 3D MRI Data Sets”,  SPIE Medical Imaging,  5370, 1774-1784, 2004, and in Tamez-Peña J, Barbu-McInnis M, Totterman S “Unsupervised Definition of the Tibia-Femoral Joint Regions of the Human Knee and its Applications to Cartilage Analysis”,  SPIE Medical Imaging,  6144, 2006. The extraction of cartilage tissue was 3D rendered using an enhanced 3D reconstruction algorithm that was able to interpolate the surface boundary in areas with no-contrast. The 3D reconstruction also included the corresponding labels of the cartilage regions. Once the cartilage was 3D rendered, the thickness computation was done using a heuristic approach that combined the normal thickness computations described in the 2006 paper just cited and the thickness computation of the minimum point-to-point distance between the articular surface and the subchondrial bone plate. The combination of the two methods allowed the determination of thickness values at regions with cartilage defects and under the presence of bone osteophytes. 
         [0025]    Once the cartilage was 3D rendered at the two time points by the enhanced approach, the next step was the computation of the significant changes. The following steps describe the approach, as shown in the flow chart of  FIG. 1 : 
         [0026]    In step  102 , the images are taken. Then the subchondrial bone plate areas are registered in the following fashion: 
         [0027]    Step  104 : Gross alignment of the areas by computing the inverse rotation matrix using the scattering matrix that is defined by all the cartilage points that are in contact with the bone. 
         [0028]    Step  106 : Fine alignment of the subchondrial bone plates by maximizing the overlapping area. The overlapping maximization includes nine degrees of freedom: three for translation, three for rotation, and three for scale (only applied for the fitting of for the femoral cartilage). The maximization is completed using a greedy maximization approach in a multi-resolution scheme. 
         [0029]    Step  108 : Transform the follow-up rendering into the baseline space. 
         [0030]    Step  110 : Re-compute the follow-up cartilage thickness in the baseline space. 
         [0031]    Step  112 : For each point in the baseline surface: compute the point to point distance between the baseline surface and the follow-up surface. 
         [0032]    Step  114 : Compute the local variance of thickness difference. 
         [0033]    Step  116 : Compute the local signal contrast between the cartilage surface and the adjacent tissues. 
         [0034]    Step  118 : Adjust the local variances using the pooled estimation: 
         [0035]    Var=(Local Var+Voxle Res*M(c))*0.5 
         [0036]    where c is the contrast and M(c) is the correction factor associated with each contrast value. 
         [0037]    Step  120 : Compute the z-map: The measured change in thickness divided by the local pooled estimation of the standard deviation of the difference (SDD). SDD=(Var) 0.5 .  FIG. 2  shows a model of the SD behavior as a function of the signal contrast. The variability (random error) in locating the right location of the cartilage boundary depends on the signal contrast of the cartilage to the adjacent tissues. The lower the contrast, the higher are the chances in making errors in the localization of the cartilage boundary, as described in Tamez-Peña J, Barbu-McInnis M, Totterman S, “Knee Cartilage Extraction and Bone-Cartilage Interface Analysis from 3D MRI Data Sets”,  SPIE Medical Imaging,  5370, 1774-1784, 2004. 
         [0038]    Step  122 : Compute the area of significant change: Add all the surface area of all the surface faces whose change in thickness is greater than three SDD: 
         [0039]    Area Loss: Changes lower than three SDD; 
         [0040]    Area Gain: Changes greater than three SDD. 
         [0041]    Step  124 : Compute the volume of significant change: Add the volume of the associated change: face area times the average change observed in that face: 
         [0042]    Volume Loss: Changes lower than three SDD; 
         [0043]    Volume Gain: Changes greater than three SDD. 
         [0044]    In the above steps, three SDD is given as an illustrative rather than limiting value. Other values can be used instead, e.g., 2.5 or 3.5. 
         [0045]    Step  126 : Compute the average thickness of the significant change: 
         [0046]    Thickness Loss: Volume Loss/Area Loss; 
         [0047]    Thickness Gain: Volume Gain/Area Gain. 
         [0048]    Step  128 : Compute the change PDF (PDF of the thickness change) at each cartilage region: For each z value of the points of the cartilage, add the total area associated to that z value and divide it by the total area of the cartilage region. 
         [0049]    An example of an identification of the significant changes is shown in  FIGS. 3A-3F . The baseline thickness map and the follow-up thickness map, shown in  FIGS. 3A and 3B  respectively, are registered, and a difference map is created, as shown in  FIG. 3C . The difference map is used to compute the signal contrast and the local variability of the paired measurements. The contrast and the variability are used to compute the SDD map, shown in  FIG. 3D . The SDD map and the thickness changes are used to compute the z-map, shown in  FIG. 3E , which is then data-mined to compute the area and the volume of the significant changes, as shown in  FIG. 3F . 
         [0050]    The advantage of this approach is that significant changes can be mapped back to the MRI image and that the computer can highlight those changes. That can be seen from  FIG. 4 , which shows a slice of the MRI data set with possible areas of significant loss marked. 
         [0051]    The approach described here was first tested in a controlled environment.  FIGS. 5A and 5B  show the 3D renderings of a single time point analysis for a subject with a simulated lesion.  FIG. 5A  shows the 3D rendering of the femoral cartridge baseline and the cartilage with the simulated lesion.  FIG. 5B  shows the SPM of the detected changes. The location of the lesion is indicated. To simulate the lesion two independent segmentation of the knee cartilage were created. Once the segmentations were completed, the second one was doctored by an expert user that created a virtual lesion. The doctored segmentation and the original segmentation were compared to the independent segmentation using the SMP approach. 
         [0052]    Table 1 describes the results of the quantification. The analysis reanalysis resulted in some false positive identification; however, the size of those identifications was smaller than the expected size (0.5%). Once the SPM approach was complete on the doctored data set, the size of the significant change was greater than the 0.5%. 
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                 TABLE 1 
               
             
             
               
                   
               
               
                 Simulated Results 
               
             
          
           
               
                   
                   
                   
                   
                 Volume of 
                 Area of 
                   
               
               
                   
                   
                 SPM 
                 RMS 
                 Significant 
                 Significant 
                 Greater 
               
               
                   
                 Raw 
                 Detected 
                 SDD 
                 Change 
                 Change 
                 Than 
               
               
                   
                 Change 
                 Change 
                 (mm) 
                 (mm 3 ) 
                 (mm 2 ) 
                 Chance 
               
               
                   
                   
               
             
          
           
               
                 Analysis- 
                 −1.5% 
                 0.5% 
                 0.21 
                 −17.4 
                 6.4 
                 No 
               
               
                 Reanalysis 
               
               
                 Simulated 
                 −1.8% 
                 0.2% 
                 0.21 
                 −30.0 
                 19.2 
                 Yes 
               
               
                 Lesion 
               
               
                   
               
             
          
         
       
     
         [0053]    After the successful competition of the test case we preceded to the computation of the delta maps on the nine healthy volunteers. The registration of the nine subjects created delta maps that were scaled from the independent analysis of the follow-up.  FIG. 6  shows the scaled values applied to the 3D segmentations. As we can see, some significant changes in scale occurred between the baseline scan and the follow-up. The effect of the scale was most of the time greater than one. Therefore, the uncorrected data will produce artificial change in cartilage volumes. 
         [0054]    Table 2 summarizes the results of the global parameters. The volume measurements were transformed by the cubic root in order to remove error dependency due to size. The table shows that the precision of the volume measurements were improved by the delta map approach while the precision thickness measurements were not affected significantly. On the other hand, the use of the SPM affected the global measurements in a positive way as seen in  FIGS. 7A ,  7 C and  7 C, which show, respectively, the changes of volume and thickness and the relative precision of annual based volume measurements. 
         [0055]    Table 3 and Table 4 show the amount of significant changes. As expected the amount of significant changes was very small. None of the volunteers showed clinical OA, and the MRI findings did not show any evidence of OA findings or OA progression. The only cartilage region that showed some relevant changes was the patella, which showed an increase of cartilage volume and some changes in a significant portion of the cartilage area. 
         [0056]    We also explored the PDF information that was created by the SPM.  FIGS. 8A-8C  and  9 A- 9 B show the results.  FIGS. 8A-8C  show the distribution of the change in thickness and its corresponding probabilistic distributions functions (PDF). More specifically, they show, respectively, the femoral histograms of change from the nine subjects in the study, the conversion of the histograms to a PDF using the SPM data, and the population-wise regional PDF extraction from the SPM data. The PDFs were computed from the SPM maps. Because the PDFs are normalized they are less sensitive to subject size. 
         [0057]      FIGS. 9A and 9B  show that the PDF information can be used to compute distribution differences among populations using the associated continuous distributions functions (CDF). In  FIG. 9A , the different behavior of the cartilage plates and the comparison of the lateral change and the Normal distribution are plotted. In  FIG. 9B , the different behaviors of the medial and lateral tibia cartilages are plotted. These plots show indications of behavioral differences among different plates and deviations from the normal distributions; however, the study&#39;s small sample size can not be used to make any population generalizations. 
         [0058]      FIGS. 8A-8C  show how the histogram distribution of the thickness changes is improved by the PDF normalization. The spread and the range of the changes are more uniform in the transformed PDF space. This resulted in a tighter distribution of the PDF lines compared to the histogram lines. Once we compute the average histogram by cartilage plate we can see some differences in the distributions. The statistical evaluation of those differences in accomplished using the cumulative distribution functions (CDF).  FIG. 9A  shows those CDF of the major cartilage plates, while  FIG. 9B  compares the CDF of the medial and the lateral tibia cartilage. The computation of the D statistics (D=|p 1 (x&lt;z)−p 1 (x&lt;z)|) allows the evaluation the degree of statistical deviation from the two distributions. This parameter shows that there is some differences in progression between the Medial and Lateral side of the tibia cartilage; but this difference can be due to random occurrence of the small population. 
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                 TABLE 2 
               
             
             
               
                   
               
               
                 Summary of the baseline data and the measured changes in the femur, tibia 
               
               
                 and patella cartilage 
               
               
                 Summary Statistics of Annual Changes in Cartilage Morphology as Measured by MRI 
               
             
          
           
               
                   
                 Volume (1/3)  (mm) 
                   
               
             
          
           
               
                   
                 Raw 
                   
               
             
          
           
               
                   
                 Changes 
                   
                 Thickness (mm) 
               
             
          
           
               
                   
                 Baseline 
                   
                 Changes 
               
             
          
           
               
                   
                   
                 vs. 
                 Delta Map Changes 
                   
                 Change: 
               
             
          
           
               
                   
                 Baseline 
                 Follow- 
                   
                 Delta 
                 Significant 
                 Baseline 
                   
                 Delta 
               
               
                   
                 Data 
                 up 
                 Scaled 
                 Map 
                 Changes 
                 Thickness 
                 Raw 
                 Map 
               
               
                   
                   
               
             
          
           
               
                 Femur 
                 AVG 
                 21.93 
                 0.25 
                 0.00 
                 −0.11 
                 0.09 
                 2.16 
                 −0.04 
                 −0.03 
               
               
                   
                 STD 
                 2.60 
                 0.46 
                 0.46 
                 0.23 
                 0.06 
                 0.36 
                 0.08 
                 0.06 
               
               
                 Tibia 
                 AVG 
                 16.79 
                 −0.08 
                 −0.20 
                 −0.25 
                 0.04 
                 2.18 
                 −0.05 
                 −0.09 
               
               
                   
                 STD 
                 1.89 
                 0.44 
                 0.47 
                 0.16 
                 0.03 
                 0.38 
                 0.12 
                 0.07 
               
               
                 Patella 
                 AVG 
                 14.74 
                 0.16 
                 0.10 
                 −0.06 
                 0.15 
                 2.74 
                 0.02 
                 −0.04 
               
               
                   
                 STD 
                 1.59 
                 0.43 
                 0.42 
                 0.30 
                 0.11 
                 0.43 
                 0.19 
                 0.17 
               
               
                   
               
             
          
         
       
     
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                 TABLE 3 
               
             
             
               
                   
               
               
                 Amount of Significant Changes in the Cartilage Tissue 
               
             
          
           
               
                   
                 Significant Changes (Absolute) 
               
             
          
           
               
                   
                   
                   
                 Depth of 
               
               
                   
                 Volume (mm 3 ) 
                 Area (mm 2 ) 
                 Changes (mm) 
               
             
          
           
               
                   
                 Gain 
                 Loss 
                 Total 
                 Gain 
                 Loss 
                 Total 
                 Gain 
                 Loss 
               
               
                   
                   
               
             
          
           
               
                 Femur 
                 19.67 
                 25.27 
                 44.94 
                 29.67 
                 39.08 
                 68.75 
                 0.66 
                 0.60 
               
               
                 Tibia 
                 4.81 
                 8.93 
                 13.74 
                 8.09 
                 9.97 
                 18.06 
                 0.57 
                 0.76 
               
               
                 Patella 
                 49.18 
                 46.28 
                 95.46 
                 64.07 
                 59.05 
                 123.12 
                 0.76 
                 0.74 
               
               
                   
               
             
          
         
       
     
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                 TABLE 4 
               
             
             
               
                   
               
               
                 Relative annual changes in the cartilage parameters 
               
             
          
           
               
                   
                 Significant Changes (Relative) 
               
             
          
           
               
                   
                   
                   
                 Relative Depth of 
               
               
                   
                 Volume 
                 Area 
                 Changes 
               
             
          
           
               
                   
                 Gain 
                 Loss 
                 Total 
                 Gain 
                 Loss 
                 Total 
                 Gain 
                 Loss 
               
               
                   
                   
               
             
          
           
               
                 Femur 
                 0.4% 
                 0.5% 
                 0.9% 
                 1.4% 
                 1.8% 
                 3.2% 
                 30.9% 
                 28.2% 
               
               
                 Tibia 
                 0.2% 
                 0.3% 
                 0.5% 
                 0.7% 
                 0.8% 
                 1.5% 
                 24.8% 
                 33.0% 
               
               
                 Patella 
                 1.3% 
                 1.2% 
                 2.5% 
                 4.9% 
                 4.5% 
                 9.3% 
                 27.0% 
                 26.1% 
               
               
                   
               
             
          
         
       
     
         [0059]    The embodiment disclosed above and other embodiments can be implemented in a system such as that shown in the block diagram of  FIG. 10 . System  1000  includes an input device  1002  for input of the image data and the like. The information input through the input device  1002  is received in the workstation  1004 , which has a storage device  1006  such as a hard drive, a processing unit  1008  for performing the processing disclosed above to provide the data, and a graphics rendering engine  1010  for preparing the data for viewing, e.g., by surface rendering. An output device  1012  can include a monitor for viewing the images rendered by the rendering engine  1010 , a further storage device such as a video recorder for recording the images, or both. The software used to implement the invention can be provided on any suitable medium  1014 . Illustrative examples of the workstation  1004  and the graphics rendering engine  1010  are Intel x86 systems and Open GL. 
         [0060]    While a preferred embodiment of the present invention has been set forth above, those skilled in the art who have reviewed the present disclosure will readily appreciate that other embodiments can be realized within the scope of the invention. For example, numerical values are illustrative rather than limiting, as are specific imaging modalities. Also, the invention can be used with any joints for which it is suitable, for human and non-human animal patients. Moreover, the concept can be applied in neurological, cardiovascular, cancer and other applications as well. Therefore, the present invention should be construed as limited only by the appended claims.