Patent Publication Number: US-5293195-A

Title: Reduction of image artifacts caused by Compton scatter

Description:
FIELD OF THE INVENTION 
     This invention is concerned with gamma camera imaging and more particularly, with methods and systems for obtaining images with a greatly reduced content of Compton scattered events: i.e., practically Compton free images. 
     BACKGROUND OF THE INVENTION 
     In passing through the human body, gamma photons have a certain probability of scattering due to the Compton effect. Such scattering changes the direction of motion of the photons and the energy of the photons. When a photon that has been scattered is recorded in the gamma camera image, false position information is derived from the scattered photons. 
     In principle, the scattered photons should be discarded. However, it is not easy to arrive at criteria that are efficient and effective for discarding scattered photons. For example, an energy level criterion is not effective because even though the photon loses part of its energy in the scattering process, the energy resolution of a typical gamma camera is such that there is a large amount of overlap in acceptable recorded energy between scattered and unscattered photons. 
     An object of the present invention is to both qualitatively and quantitatively improve the recorded images by significantly eliminating the contribution of the scattered photons to the final image and obtaining the practically Compton free images within seconds after acquisition. There are many prior art methods that also reduce, or attempt to reduce the contribution that the scattered photons make to the final image. However, of all the prior art methods and systems known, no high quality, practical method is known wherein the contribution of the scattered photons to the final image is significantly reduced and the final image is shown to be qualitatively and quantitatively practically free of Compton scatter contamination. 
     Some of the prior art methods are: 
     (a) Dual Window Acquisition 
     In addition to acquiring data using the standard, full energy window around the peak energy, a second image is acquired at &#34;low&#34; energy. The second image is then multiplied by an empirical constant and subtracted from the first image to reduce the effect of the scattered events on the final image. 
     (b) The Method of Axelsson et al (see the article &#34;Subtraction of Compton-Scattered Photon Emission Computerized Tomography&#34;, J. Nuc. Med. 25, pp. 490-494, 1984) 
     This is a spatial deconvolution method which is designed to extract the exponential tail caused by Compton scattering from the point spread function. The magnitude of the exponential tail is considered a constant throughout the image. 
     (c) Weighted Acquisition Module 
     This method has been described in U.S. Pat. No. 4,780,823. Basically, what is described in the Patent is a constant spatial filter applied on the fly over the whole area of the image. The method does not remove the Compton scattered events at all, but tends to sharpen edges blurred by the scattering. 
     (d) The Method of Koral et al 
     This method is taught in U.S. Pat. No. 4,839,808. Therein the energy spectrum is fitted at each given location with a photopeak shaped curve combined with a third order polynomial that is assumed to represent the Compton scatter spectrum. The result of the fit is assumedly a separation of events at each point into a scattered and an unscattered portion. In order to obtain reasonable statistics and also save computation time, the energy spectra are in practice analyzed on a coarse spatial grid. The values of the Compton scatter fraction are then interpolated to obtain values for a finer imaging grid. 
     The basic shortcoming which all of the above methods have in common is that they do not utilize the innate physical properties of the energy spectrum to account for the Compton scattered events. As a result, the above Compton scatter correction methods all inherently cannot possibly correctly calculate and subtract Compton scattered events. 
     The methods (a) and (b) described above, are both totally empirical and non-local. As a result, neither method has been successful in overcoming artifacts due to Compton scattering and as a result, in practice, they are not used. 
     The weighted acquisition module method does not even attempt to separate the Compton scattered events from the unscattered events; rather, the method sharpens somewhat the edges blurred by the scattering. It does have limited use in qualitative images, but is practically totally useless in quantative SPECT imaging, which is expected to be a main application of scatter corrected imaging. 
     The method of Koral et al suffers from several drawbacks. The main drawback is the fact that the third order polynomial used for fitting the Compton scatter distribution does not truly define the physical distribution of the Compton scatter events. The third order polynomial is unrestricted especially at the high energy Compton scatter thresholds of the spectrum which overlap the photopeak. The high energy portion of the overlap is the most relevant area in which it is difficult to eliminate Compton scatter by conventional methods of energy windows. 
     With the fit being performed thousands of times per image, it is practically impossible to prevent using many incorrect composite curves which result in erroneous image quantitation and artifacts. 
     BRIEF DESCRIPTION OF THE PRESENT INVENTION 
     The present invention is somewhat similar to the system of Koral et al as described in the Patent. The present invention locally analyzes the energy spectrum and attempts to fit it with a &#34;trial&#34; function composed of a photopeak component of known energy shape and unknown magnitude and a Compton scatter component unknown both in shape and in magnitude. However, instead of using an arbitrary polynomial to fit the Compton component energy spectrum as used by Koral et al, the true physical characteristics of the Compton process are used to derive Compton multi-scatter functions which are subsequently used to construct the Compton component energy spectra. The fit, therefore, provides quantitative information on both photopeak and Compton components. 
     In greater detail, the following &#34;inputs&#34; are used to determine the unknowns; i.e., the magnitude of the photopeak component, the shape and magnitude of the Compton multi-scatter components: 
     1) The measured energy spectrum per pixel. This includes counts due to scattered and unscattered photons, 
     2) The system energy spread function. For the isotope center line, this is the photopeak energy shape. 
     The shape of the Compton component of the trial function is derived using the following steps: 
     1) The Nishina-Klein equation, describing the physical relativistic scattering of photons and electrons is converted into a probability distribution for a photon to scatter from a given energy to a lower energy in a single interaction. 
     2) By repeated convolutions, probability distributions for higher order interactions are constructed. 
     3) A superposition of the various orders with local coefficients of as yet unknown values, is constructed. 
     4) This superposition is convolved with the system energy spread function to yield the physical Compton multi-scatter component. 
     By locally fitting this trial function to the measured energy spectrum of acquired data, the values of the multi-scatter Compton coefficients and the photopeak magnitude are obtained. This enables the removal of Compton contamination from the acquired data. 
     The execution of the local fitting is preferably expedited by orthonormalizing the set of the multi-scatter probabilities. The orthonormalization can be achieved, e.g. by a Graham-Schmitt procedure. In practice, instead of solving for the coefficients of Compton multi-scatter probabilities, the coefficients of the orthonormalized functions are the unknowns which are determined. 
     An alternative system of the present invention also treats the problem of improving the statistics for the calculations to compensate in a manner more accurate than the manner used by Koral et al. In the alternative system of the present invention. The smoothness of the Compton distribution is utilized in summing the Compton scatter data over a &#34;quasi-local&#34; area; i.e., in a (2 n+1) ×(2 m+1) rectangle of pixels for the fit, where m and n=0.1.2 . . . 
     The value obtained from the rectangle of pixels is divided by the number of pixels in the rectangle and is assigned to the central pixel of the rectangle. This calculation is repeated for each pixel. This &#34;quasi-local&#34; method is more accurate than the interpolation method used by Koral et al. Moreover, computation time is approximately the same in the present method as is computation time required by the Koral et al interpolation method. The actual algorithm that is preferred is one of continuously updated rectangles such that the time of processing is practically independent of the number of pixels in the rectangle. 
     The actual fit of the &#34;trial&#34; function to the local or quasi-local measured energy spectra can be done using many methods. In a preferred embodiment either a least square fit or a maximum likelihood method is used. The maximum likelihood method is more time consuming than the least square fit method, however it takes explicitly into account the Poisson statistics in gamma ray processes. 
     In accordance with a preferred aspect of the present invention, a method of eliminating the contribution of Compton scattered photons on an image produced by a gamma ray imaging system is provided, said method comprises the steps of: 
     detecting as counts photons impinging on a gamma ray detector responsive to gamma radiation from a source. 
     determining an X,Y coordinate location for each detected count according to the location of the impingement of the photons on the detector. 
     measuring the energy of the impinging photons, 
     using a plurality of energy windows that together span an energy range with each window extending over a portion of the energy range, 
     placing each detected count in a location in a memory matrix assigned to each of said energy windows according to the measured energy of the impinging photon, said memory matrices each being digital images corresponding to a single energy window comprising X,Y co-ordinates, 
     determining a measured energy spectrum per X,Y location by using the counts accumulated in each of the memory matrices for each X,Y coordinate location, said measured energy spectrum including counts produced by both scattered and unscattered photons, 
     calculating the energy distribution of Compton multi-scattered photons using Compton multi-scattered components, where &#34;multi-scattered&#34; includes at least first order scatterings, 
     convolving the calculated energy distributions of the multi-scattered photons with a spread function typical of a gamma ray imaging system to obtain system dependent energy distribution of the Compton multi-scattered photons, 
     constructing a trial function by: 
     summing the system dependent energy distributions of the Compton multi-scattered photons with unknown coefficients, one for each order of scattering, and 
     adding the contribution of unscattered photons as a coefficient times the photopeak energy shape, 
     fitting the constructed trial function to the measured energy spectrum per X,Y coordinate location to solve the unknown X,Y coefficients and thus determine the counts per X,Y location of the unscattered photons, and 
     using the counts of unscattered photons per pixel to provide a Compton free image. 
     According to a feature of the present invention, calculating the energy distribution of scattered photons includes converting the Nishina-Klein equation to a first order probability distribution to determine the energy distribution of first order scattered photons, and convolving the first order probability distribution to obtain higher order scattered photon distributions where such higher order scattered photon distributions are being considered. 
     In accordance with an added feature of the present invention, a set of discrete functions is obtained from the energy distribution by averaging or integrating the energy distributions for each of the energy windows. 
     Yet another added feature of the present invention is the conversion of the discrete functions into an orthonormal set of functions to decrease the number of calculations. 
     In accordance with yet another added feature of the present invention, obtaining the discrete functions for the energy spectrum of the scattered photons is accomplished quasi-locally using &#34;large&#34; pixels. This use of &#34;large&#34; pixels for the comparatively smooth Compton distributions compensates for the low statistical counts in each of the regular pixels. 
     In accordance with yet another feature of the present invention. The fitting is accomplished using least square fitting computations. Alternatively, maximum likelihood solutions are used. 
     Another feature of the present invention is that the derivations of all energy shapes used in the trial function are data independent. Therefore, such shapes can either be stored or calculated prior to the data processing that generates Compton free images. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The above mentioned objects and features of the present invention along with additional objects and features will be best understood when considered in the light of the following description made in conjunction with the accompanying drawings; wherein: 
     FIG. 1 is a block diagram showing of a gamma radiation imaging system for providing Compton free images, and 
     FIG. 2 represents details of preparations, computations and operations used in the system shown in FIG. 1. 
    
    
     GENERAL DESCRIPTION 
     FIG. 1 at 11 shows in block diagram form the inventive gama camera system for producing Compton free images. FIG. 1 comprises a measured energy spectrum stage 12, a trial function preparation stage 14 and a curve fitting or computation stage 15 which provides the Compton free image 16. 
     The measured energy spectrum stage 12 comprises a gamma radiation detector 17. The gamma radiation detector 17 provides electrical signals responsive to photons impinging on the face thereof such as is indicated at 18. When a photon impinges on the face of the detector, electrical signals are provided on conductors 19, 21 and 22. These conductors 19, 21 and 22 are directed immediately to a coordinate computer 23 which determines the X and Y location of the impingement of the photon 18 on the detector 17. 
     The conductors 22 and 24 carries an electrical representation of the energy of the photon. The electrical representation of the energy is provided to an energy (Z) correction circuit 25. An energy processing circuit 26 divides the range of the energy detected into a number of energy windows predetermined by the system operator. 
     When the energy is within certain limits the energy correction circuit sends an enable signal over conductor 27 which enables the coordinate computer to determine the X and Y locations. This information is directed to an image corrector and digitizer circuit 31. The image corrector and digitizer circuit 31 corrects and digitizes the X,Y coordinates of each event. The information on the number of events is placed into a plurality of matrices 32 depending on the photon&#39;s energy. Each of the matrices is a memory that retains the count of events per X,Y location for a particular energy window such, as for example, a window that extends from 22 kev to 25 kev, for window #1 and 25 kev to 28 kev for window #2, etc. The windows are shown as W1, W2, W3 extending to Wn where n is the predetermined number of energy windows. 
     The matrices are thus divided into X,Y locations that correspond to the location of the event on the detector. The X,Y locations also correspond to pixels in the final image. An imaging preprocessor 33 receives the data pixel by pixel from each of the windows and computes measured or acquired energy spectrum N- per pixel as shown at block 34. This acquired energy spectrum includes both the counts of unscattered photons and scattered photons. 
     The trial function stage 14 of FIG. 1 prepares a theoretical or trial energy distribution n(X,Y,E) including scattered and unscattered events. Herein: ##EQU1## where: ε=E/m-C 2 , the photon energy in units of electron rest energy, m-C 2 , 
     P(ε&#39;,ε)=a typical energy spread function at energy ε&#39; for the system. Its shape has been measured and is known, 
     π m  (εo,ε)=the energy distribution of events caused by photons scattered m-times from original energy εo to energy ε&#39; (i.e. the shape of the energy probability distribution of the m-times scattered photons), 
     N P  (x,y)=the spatial distribution (counts/pixel) of the events caused by unscattered photons, 
     Q m  (x,y)=the spatial distribution (count/pixel) of the events caused by photons scattered m-times, 
     εo=the original energy of the photon, 
     ε=the measured energy of the photon, and 
     ε&#39;=an intermediate energy of a photon. 
     An important purpose of the invention is to determine N P  (x,y), the spatial distribution of unscattered events. 
     To determine the count of events per pixel, block 15 fits the computed values of the trial distribution n(x,y,ε) to the measured values NE(x,y). The output of the fit provides the unscattered spatial distribution N P  (x,y). With the knowledge of the unscattered spatial distribution the Compton free image is produced as indicated at 16. 
     Details of the computations that occur in the trial function preparation section 14 of FIG. 1 are indicated in FIG. 2. More particularly, as shown in FIG. 2 values based on the system energy spread function shown in block 41 of FIG. 1 and FIG. 2 are entered into block 36 of FIGS. 1 and 2. The energy spread function of the system is assumed to be known. It is measured once for the system or a typical system spread function is used and is kept in the memory of the system. The measurement is easily accomplished by providing sources of gamma radiation of known energy and detecting the radiation with the equipment 11 of FIG. 1, for example. The detection is made without any Compton scatter media between the energy source and the detector. This provides the energy spread function for a monoenergetic source due to the detector energy resolution such as shown at block 41. 
     The preparation block 36 computes π m  ; that is, the energy distribution of the scattered photons for each scattering order. 
     This is done by using the Nashina-Klein equation to derive the different orders of scattered unpolarized photons: i.e.: ##EQU2## The higher orders of scatters are derived by repeated convolutions using the equation: ##EQU3## Note that the equations are solved recursively in that each higher order equation requires knowledge of the lower orders. 
     The energy distribution of the Compton scatter photons provides a curve independent of the system for each order of scatter. However, this system independent curve is acted upon by the system energy spread function to provide the system dependent Compton multi-scattered energy distributions denoted by C m  (εo,ε). The shapes of the C m  (εo,ε) distributions are obtained by convolving π m  with the system energy spread function P(ε&#39;,ε):; i.e.: ##EQU4## This set of equations provides the shape of the Compton energy distributions for each order of scatter after being operated on by the system energy spread function. FIG. 2 indicates the computations resulting in the π m  values using the Nishina-Klein equation in box 42, 43 and 44 for π 1  and consequently π 2 , . . . π m . 
     The shapes of π 1 , π 2  and π m  in blocks 42, 43 and 44 are shown as being convolved with the system energy spread function of block 41 in blocks 46, 47 and 48 respectively, thereby providing the shapes C1, C2, etc. The computations to determine π 1 , π 2 , etc., and C1, C2, etc. are indicated as being recursive by the dotted line arrows going from π 1  to π 2 , etc., and from C1 to C2, etc. 
     A method for drastically reducing the number of computations is provided by this invention. This reduction in computation is accomplished by orthonormalization of the set Cm (εo,ε). The orthonormalization is provided by constructing an orthonormal function (vector) set θ m  using the Graham-Shmidt procedure: ##EQU5## 
     Where sums (integrals) over energy are defined by: ##EQU6## 
     Note that the array set {θ m  } obeys: ##EQU7## 
     The orthonormalization is accomplished in computer 49 and the results: i.e., θ 1 ,θ 2  . . . θ m  are shown in blocks 51, 52, 53, for example. 
     The Compton sum (EQ(4)) can be rewritten using the θ k  &#39;s: ##EQU8## 
     The trial distribution now reads: 
     
         n(X,Y;E)=Np(X,Y) P(E.sub.o,E)+C(X,Y;E.sub.o,E)             (8) 
    
     where: ##EQU9## Hereafter the known energy spread function, P is normalized such that &lt;P&gt;=1. 
     In a preferred implementation, a least square fit is used. More particularly, with the trial function n(X,Y;E) of equation (1) and the multi-window acquisition results N E  (X,Y) from block 34, a solution is sought for the number of counts caused by unscattered photons N P  (X,Y) that will minimize the sum of the squares of differences for each pixel δ(X,Y): 
     
         δ(X,Y)=&lt;[n(X,Y;E)-N.sub.E (X,Y].sup.2 &gt;              (10). 
    
     More particularly, in the block 15 the following &#34;fit&#34; operation is performed, i.e., ##EQU10## 
     It can be shown that the solution of these equations is: 
     
         N.sub.P (X,Y)=&lt;N.sub.E (X,Y)·J(E)&gt;                (13) 
    
     
         q.sub.k (X,Y)=&lt;N.sub.E (X,Y)·G.sub.k (E)&gt;         (14) 
    
     where: ##EQU11## 
     Note that since J(E) and G k  (E) are data independent, they can be apriori derived as indicated in FIG. 2 by block 54 which shows computing J(E) using θ 1 , θ 2 . . . θ k  and P. The &#34;per-pixel&#34; operations entail only the evaluation of the scalar product 
     
         Np=&lt;N.sub.E ·J(E)&gt;.                               (17) 
    
     The fit, therefore, determines the per pixel Compton scatter free count N P . 
     To compensate for low statistics per pixel per energy window, the relative constancy of scatter distributions over large spatial domains is put to advantage by use of a &#34;quasi-local&#34; solution. More particularly, an expanded or &#34;large&#34; pixel is used. Thus, if the top pixel is (X o ,Y o ) the spatial window W is defined as: 
     
         X.sub.o -W≦X≦X.sub.o +W,Y.sub.o -W≦Y≦Y.sub.o +W(18) 
    
     of area s=(2W+1) 2 . 
     When the Compton component of the entire window s, C s  is computed, the &#34;per-pixel&#34; Compton activity, C 1  can be approximated by its average: 
     
         C.sup.1 =C.sup.s /S.                                       (19). 
    
     The measured activity in the spatial window s (symmetric around the coordinates (X,Y)) is denoted as N E   s  (X,Y), and the (X,Y)-pixel activity is denoted as N E   1  (X,Y). A single parameter fit is done to find the local photopeak count. It can be shown that this is given by: ##EQU12## 
     Solving for N 1   P  (X,Y) gives the count/pixel of the Compton free image. 
     An alternative fitting method is the Maximum Likelihood Method. Given the measured activities {N E  } the joint Poisson probability with respect to the parameters of the trial function n(E) are maximized; i.e., find n(E) such that ##EQU13## 
     It can be shown that for the maximum likelihood solution: 
     
         &lt;n(E)&gt;=&lt;N.sub.E &gt; 
    
     which enables eliminating N P  from the n(E) function. (Hereafter (X,Y) are implicit, e.g., n(X,Y;E)=n(E). Thus from equations (8) and (9) it follows that: ##EQU14## 
     Calculating the derivatives of lnP with respect to q k  and setting the resulting equation to 0 as required by the maximum condition, the following equations are obtained: ##EQU15## 
     This is a set of non-linear coupled equations and cannot be solved in closed form. Using the multi-gradient method, an iterative solution for the q k  s can be obtained. Denoting δq k  as the difference between q k  before and q&#39; k  after the iteration 
     
         δq.sub.k =q&#39;.sub.k -q.sub.k.                         (27) 
    
     The coupled set of equations is linearized and soluble ##EQU16## 
     After proper convergence of the solution for the array q k  has been attained, the Compton free activity N P  can be obtained from: ##EQU17## 
     Yet another alternative fit is the partial Maximum Likelihood Solution. Suppose that the least square solution provides the approximate functional structure of the Compton component. However, it is desired to introduce the Poisson statistics by changing the ratio of the photopeak to Compton events fraction in order to optimize the joint distribution. The trial function, n(E), then is ##EQU18## and C is the least square Compton solution, normalized to 1; i.e.: 
     
         C=C/&lt;C&gt;                                                    (34). 
    
     Now, the Maximum Likelihood Equation is maximized with respect to a single parameter, the photopeak fraction, f P . Once f P  is calculated, the scatter-free event distribution N P  can be found using the equation: 
     
         N.sub.P =f.sub.P ·&lt;N.sub.E &gt;                      (35). 
    
     The optimization equation resulting from the differentiation with respect to f P  reads: ##EQU19## 
     It is soluble by an iterative Newton-Raphson method: ##EQU20## 
     Yet another related method of obtaining the value of N P  involves the semi-local Maximum Likelihood Fit Solution. As for the quasi-local solution, this is implemented here as follows: First a solution is obtained for the square called S surrounding the pixel X o ,Y o , i.e.: 
     
         S=X.sub.o -W≦X≦X.sub.o +W,Y.sub.o -W≦Y≦Y.sub.o +W=(2W+1).sup.2                                           (39). 
    
     Once the Compton free component of the entire window has been obtained, either by the full or by the partial Maximum Likelihood method: i.e., N P   s  (X,Y) is known, the slow Compton spatial variance is used by assuming: 
     
         C.sup.1 =C.sup.s /s                                        (40) 
    
     to obtain the (X,Y)-pixel Compton free activity: ##EQU21## 
     In operation, the inventive system locally analyzes the energy spectrum and fits it with a trial function comprising a combination of the unscattered photopeak function and a function representing the Compton scattered spectrum. The function representing the Compton scattered spectrum is derived using the Nishina-Klein formula. The Compton scatter spectrum shape, therefore, inherently reflects the true relativistic distributions of the Compton scatter, unlike the previously used arbitrary polynomials. The Nishina-Klein formula is recursively used to generate the multi-scattered Compton distribution π m . Then each π m  is convolved with the system energy spread function to obtain C m , the system dependent Compton scatter distributions. The convolved functions are averaged or integrated for discrete windows to obtain discrete arrays required for the calculations. The set of discrete functions C 1  is then preferably orthonormalized to reduce the number of computations necessary and assure that the inventive system can provide practically Compton free images within seconds after acquisition. The coefficients of the orthonormalized functions are parameters that provide the scattered counts per pixel. The parameters are determined by fitting the final function comprised of the photopeak component and the scatter component to the measured energy distribution which includes both the scattered and unscattered photons. The local (and quasi-local) fitting optimizes the coefficients of the fit functions. 
     A unique approach of the invention is that the parameters to be determined are coefficients of the physical Compton scatter functions. The said functions have the correct high energy threshold behavior ensuring correct fit at every point. 
     The inventive method also preferably improves the statistics for the calculations for the Compton fit by a method that takes advantage of the smoothness of the Compton distribution (quasi-local method). That is, the data in a preferred embodiment is summed over a (2 n+1) square pixels for the fit where n is an integer. The values are then attributed only to the central pixel of the square. Similar calculations are done for each pixel. 
     Preferably a least square fit is used to solve the unknown coefficients, i.e., the amount per pixel of the unscattered events (and if desired for the amount per pixel of the scattered events). However, several variations employing the maximum likelihood fit are also described. 
     While the invention has been described with regard to specific embodiments, it should be understood that the description is made by way of example only and not as a limitation of the scope of the invention which is defined by the accompanying claims.