Patent Publication Number: US-7711161-B2

Title: Validation scheme for composing magnetic resonance images (MRI)

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 60/604,105, filed Aug. 24, 2004, which is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     It is now common to generate multiple volumes of image data during a magnetic resonance imaging procedure on a patient. Other medical imaging procedures on a patient also generate multiple volumes. 
     When analyzing the volumes to generate an image of the patient, it becomes necessary to align adjacent volumes. This is a difficult task to perform. It generally requires analyzing a pair of volumes and determining the optimal way to align the volumes based on the analysis. 
     It is possible to visually tell whether volumes are well aligned by examining the resulting photo. For example, if a visual inspection of the image shows that structures, such as bones or blood vessels align, then the alignment would be deemed good. On the other hand, if the visual examination shows that the structures do not align, then the alignment would be deemed bad. 
     The visual inspection, however, has its limitations. For example, it is time consuming. It also requires one or more persons to examine a plurality of different alignments to judge which are acceptable. The visual inspection is also subject to the inspector&#39;s objectivity. Furthermore, it is difficult for a inspector to determine an optimal alignment in a three dimensional setting, such as MR volumes. 
     Thus, new and improved method and systems to judge the quality of the alignment of volume pairs is needed. 
     SUMMARY OF THE INVENTION 
     In accordance with one aspect of the present invention, a method for validating the alignment of a plurality of volumes of image data obtained during a medical imaging procedure is provided. The method includes selecting one or more points in a first volume and one or more points in a second volume and then determining the average Euclidean distance between the one or more points in the first volume and the one or more points in the second volume. Then a distortion category is determined based on the average Euclidean distance, and a quality value (a Q value) indicative of the quality of the alignment of the first volume and the second volume is assigned based on the distortion category. 
     In accordance with a further aspect of the present invention, the distortion category is based on the average Euclidean distance as follows: if the average Euclidean distance is between 12 and 16, the distortion category is Severe; if the average Euclidean distance is between 8 and 12, the distortion category is Medium; if the average Euclidean distance is between 4 and 8, the distortion category is Moderate; if the average Euclidean distance is between 0 and 4, the distortion category is Minimal. 
     In accordance with another aspect of the present invention, a noise level from a background region in the first volume is determined and a noise level from a background region in the second volume is determined. The average of the two noise levels is determined, and a Q value indicative of the quality of the alignment based on the distortion category and on the average noise level is determined. 
     In accordance with another aspect of the present invention, a system for validating the alignment of datasets obtained during a medical imaging procedure is also provided. The system includes means for determining the average Euclidean distance between one or more points in a first volume and one or more points in a second volume and means for determining a distortion category based on the average Euclidean distance. It also includes means for determining the noise level of an area of the first volume and the second volume and for determining the average noise level. The system determines the Q value based on the distortion category and the noise level. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a flow diagram of the method in accordance with one aspect of the present invention. 
         FIG. 2  illustrates a window screen showing selected landmarks and the calculation of noise levels and distortion levels in accordance with one aspect of the present invention. 
         FIG. 3  illustrates range values for noise level bins in accordance with one aspect of the present invention. 
         FIG. 4  illustrates range values for distortion amount bins in accordance with one aspect of the present invention. 
         FIG. 5  illustrates a table that can be used to determine Q values based on noise levels and distortion amounts in accordance with one aspect of the present invention. 
         FIGS. 6 and 7 . illustrate results obtained in accordance with the present invention. 
         FIG. 8  illustrates a system in accordance with one aspect of the present invention. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     This patent application describes a performance evaluation scheme of the MR composer application, which is used to align pairs of volumes obtained from a magnetic resonance imaging machine. Thus present invention provides a measure of a quality of the alignment of a pair of volumes by way of a Q value. The method and system of the present invention, however, is useful in a wide range of medical imaging applications. 
     A set of volumes obtained during acquisition is aligned to produce a single compact image. It is preferred to quantify how good the alignment is in the best possible manner. It is preferred to quantify the resulting alignment despite the fact that the true gold standard for the data sets is not available. The method of the present invention can be used in volume alignment processes or other processes designed to solve similar problems to find out whether the proposed changes improve the performance or not. 
     In accordance with one aspect of the present invention, a quality value (Q-value) is determined by the process of the present invention to characterize the quality of alignment parameters. It is very difficult to design a process that would automatically compute such a value that would be equal to the one assigned by a human observer who is judging the noise, distortion and alignment precision. 
     The main purpose of the present method is to answer two questions. The first question is how good is the alignment produced by the MR composer? The second question is does the Q-value reflect the quality of composing? 
       FIG. 1  illustrates the steps of a preferred embodiment of the present invention. In step  10 , one or more points in a first image are selected and one or more points in a second image are selected. These points can be selected manually or automatically by a processor. The points are preferably selected at a readily identifiable landmark on the images. In step  12 , a noise level of the background in each image is determined and an average of the noise level is obtained. In step  14 , the distortion between the two images is determined by determining the average Euclidean distance between the landmark points. In step  16 , a Q value is determined based on the average noise level and on the distortion level. The Q level indicates the quality of the alignment. 
     Referring to  FIG. 2 , the selection of landmarks is illustrated. The middle pane of  FIG. 2  shows a landmark  1  at position  20  in the top image and at position  21  in the lower image. It also shows a landmark  2  at position  22  in the top image and at position  23  in the lower image, a landmark  3  at position  24  in the top image and at position  25  in the lower image, a landmark  4  at position  26  in the top image and at position  27  in the lower image and a landmark  5  at position  28  in the top image and at position  29  in the lower image. 
     The landmarks can be selected manually or automatically via a software program in a processor. The landmarks are chosen as prominent feature points, such as vessel bifurcation points, points with maximum curvature, etc. The landmarks should be picked as precisely as possible and the Microsoft Windows Magnifier (Start→Programs→Accessories→Accessibility→Magnifier) with 8 times magnification can be used to position the landmark with a pixel accuracy. 
     The noise level is computed from a selected background region. The selected, background region may be user selected (drag and drop to outline a window) of N points, by estimating scale of an underlying Rice distribution. In this region, the deterministic signal is assumed to be zero and the noise level is determined as: 
                 σ   ^     2     =       1   KN     ⁢       ∑     i   =   1     N     ⁢     M   i   2               
Where K denotes twice the number of orthogonal Cartesian directions in which flow is encoded (equal to 2 for 2D slices—a single gradient selects slice and the values of kx, ky, in the k-space are filled; it is multiplied by the number of coils, M i  are signal magnitudes that follow generalized Rice distribution. Typically, around N=50,000 points are collected across the whole volume. In regions, where the signal is zero, the magnitude data is governed by a generalized Rayleigh distribution. For large signal magnitudes (SNR→∞) the Rice distribution approaches a shifted Gaussian distribution centered at r=sigma
 
     The noise level for two aligned volumes is determined in this fashion. Then, the average of noise levels of two volumes in each volume pair is used as the noise level of this volume pair. The noise level of each volume pair is grouped into four categories: LOW, MODERATE, MEDIUM, and HIGH, depending on the quantity of noise, as shown in  FIG. 3 . The threshold values for the bins illustrated in  FIG. 3  have been determined experimentally for one system, such that a LOW noise level falls within the range of 0.0 to 0.65, a MODERATE noise level falls within the range of 0.65 to 1.30, a MEDIUM noise level falls within the range of 1.30 to 1.95 and a HIGH noise level falls within the range of 1.95 to 2.6. 
     Distortion can be calculated before or after the noise calculation. Distortion is computed as the average distance between corresponding pairs of the previously described landmark points, as illustrated in  FIG. 2 . In a preferred embodiment of the present invention, five pairs of landmarks are used, two selected on one side of the image and three on the other. This allows the method to capture the amount of distortion across the whole image. In exceptional situations less landmark points can be used. 
     The image distortion of each volume pair is grouped into four categories: MINIMAL, MODERATE, MEDIUM, and SEVERE. Let (h i ,v i ,d i ) be the alignment parameters obtained from landmark point P i , where 1≦i≦n, n is the number of landmark pairs selected from the volume pair, n=5 in a preferred embodiment of the present invention. Then the assignment of categories is determined as follows: 
     
       
         
           
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     Note that only horizontal and vertical directions are used in accordance with this aspect of the invention. This is preferred due to the low resolution in the depth direction. This value is enough to classify the distortion into the four categories well. The category bins are illustrated in  FIG. 4 . The threshold values for the bins were derived from the experimental data. 
     In the next step, the noise level and the distortion level are used to determine the Q value. To do so, the noise level category determined in the previous step and the distortion level category determined in the previous step are used to reference a table illustrated in  FIG. 5 . Based on the input noise level and distortion level, a Q value is determined. 
     In general, Q values approaching 100 mean the composed result is very good. In these cases, it is almost not possible to tell where the seam is. On the other hand, values below 50 indicate a serious misalignment and are not acceptable. Further, manual alignment of two corresponding volumes should never have a value smaller than 48, because manual alignment provides the ideal result which should be accepted. Also, a value of 96 or above indicates perfect alignment of clean images (noise level=“MINIMAL”) with no distortion (distortion=“MINIMAL”) 
     It is also noted that noise does not affect the alignment quality as much as distortion. Image distortion and noise are the main reasons for a decrease of the Q-value. When the optimal alignment is provided by observers, Q-value (used as a ground truth) can be defined based on noise level and image distortion unambiguously as shown in  FIG. 5 . 
     The performance of the method has been analyzed. Let (h 1 ,v 1 ,d 1 ) and (h 2 ,v 2 ,d 2 ) be the lower and upper bound of the range of good alignments obtained from observers, and (h c ,v c ,d c ) be the alignment calculated by the composer, then the misalignment error E align  can be computed as 
     
       
         
           
             
               
                 
                   
                     
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     Note that the usage of the range of good alignments, not a single value, compensates the influence of image distortion in the alignment error calculation. 
     The estimated Q-value {circumflex over (Q)} for the automatically calculated alignment can be computed as
 
 {circumflex over (Q)}=Q   g −8* E E   align ,
 
where Q g  is the Q-value for good alignments of this volume pair based on noise and distortion (from Table 1). Let Q c  be the Q-value calculated by the composer, then the Q-value error is
 
 E   Q   =Q   c   −{circumflex over (Q)}.  
 
     This proposed evaluation scheme was used to study results of 12 MRA (MR angiography) patient series, a total of 31 aligned volume pairs. Average alignment errors are summarized in  FIG. 6 .  FIG. 6  shows the absolute distances between automatic and manual alignments in all directions were calculated using the evaluation scheme with ranges described in the previous section. Euclidean distance was computed from these directions. Total alignment error E align  was calculated by Equation 1. 
     The Q-value with respect to noise and distortion measurements is plotted in  FIG. 7 . It has the desired i.e. it reflects the quality of alignment with respect to the noise, amount of distortion and error. The evaluation scheme is particularly useful for comparing performance of different algorithms or two different versions of the same algorithm. In  FIG. 7 , Q value reflects the alignment error with respect to the amount of distortion and noise level. It is divided by 10 in this plot. 
       FIG. 8  illustrates a medical imaging system  80 . The system  80  includes an imaging device  82 , a processor  84  and a display  86 . The imaging device  80  is a magnetic resonance imaging machine, by way of example only. The processor  84  is a personal computer, workstation or the like, that can be connected to the imaging device  80  or a stand-alone machine. The processor  84  can also be a multi-processor system. The processor  84  performs the steps described herein. 
     To determine how well the Q level indicates the quality of alignment, it is desirable to obtain the gold standard for the alignment and Q-value. This is done manually with two observers and a panel (the two observers and a moderator) participating in the evaluation. Horizontal, vertical, and depth alignment and Q-value from the software alignment composer are recorded for evaluation. Using guidelines described below, noise level and distortion (including MR reconstruction distortion and patient movement) are determined by observers and used for calculating the Q-value that will serve as the ground truth for the Q-value validation. 
     The determination of the quality of alignment by the observers follows certain general rules. First, each observer is shown the volumes/pairs in a random order. Second, all available volumes from a series are composed at once. This way all volumes get equalized to the maximum resolution out of all in the given series. 
     Manual alignments are determined from MIP images at first, but the original volumes are checked each time. MIP images are displayed without overlapping (so that observers would not be biased by a displayed alignment). XY MIP images are used to determine the initial horizontal and vertical shift, YZ MIP images are used to determine the depth shift. Precise alignments are determined with the help of landmark points. Landmarks are chosen as prominent feature points, such as vessel bifurcation points, points with maximum curvature, etc. Landmarks are picked as precisely as possible and the Microsoft Windows Magnifier (Start→Programs→Accessories→Accessibility→Magnifier) with 8 times magnification is used to position the landmark with a pixel accuracy. The clarity of the MIP images is improved by adjusting the slice ranges across which MIP is computed. The observers constantly refer back to the original images, in order to verify the decisions made based on the MIP images. The observers validate alignment in MIPS by moving cutline in the overlap region. 
     In general, if the horizontal or depth alignment values differ by one or more, two observers need to re-evaluate the results. Also, if the vertical alignment values differ by two or more, two observers need to re-evaluate results. If an agreement cannot be made, the case needs to be discussed in a panel. The image quality (noise and distortion) indicators must be agreed on precisely. If the categories differ, two observers need to re-evaluate results. If an agreement cannot be made the case is discussed in the panel. The Q-value will have the same value for both observers determined by the image quality categories. 
     While the method has been described using distortion and noise level to determine the Q-value, it is possible to determine Q value using just the distortion level or just the noise level as well. Further, the method has been described using an average of the noise levels, but the method can be followed using a single noise level, without having to determine an average. 
     Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It will be apparent to those skilled in the art that various modifications and variations can be made to the method of the present invention without departing from the spirit and scope of the invention. Thus, it is intended that the present invention include modifications and variations that are within the scope of the appended claims and their equivalents.