FAST HIERARCHICAL BACKPROJECTION METHOD FOR IMAGING

Abstract: A process for backprojecting a sinogram (10) to a tomographic image (12) includes the steps of subdividing the sinogram (10) into a plurality of subsinograms (14, 16, 18, 20), preferably in a recursive manner, until each subsinogram (14, 16, 18, 20) represents an image as small as one pixel in size. Each of the final subsinograms (14, 16, 18, 20) is backprojected to produce a plurality of corresponding subimages (22, 24, 26, 28), and the subimages (22, 24, 26, 28) are aggregated to create the image. Two algorithms can be used in the decomposition process. An exact decomposition algorithm is accurate, but relatively slow. An approximation decomposition algorithm is less accurate, but fast. By switching between the two algorithms, accurate decomposition is realized in a short time, speeding up the backprojection process considerably.

Description: FAST HIERARCHICAL BACKPROJECTION METHOD FOR IMAGING This is a continuation-in-part of Serial No. 09/338,677, filed June 23, 1999. TECHNICAL FIELD This invention relates to imaging, and more particularly, to a method for reconstructing tomographic images from projections. BACKGROUND ART The reconstruction problem in 2D axial computed tomography (CT) is one of recovering an image from a set of its line-integral projections at different angles. The type of algorithm used to address the reconstruction problem depends almost exclusively on how much of the line-integral information is available. When line-integrals are available from all possible directions, and the measurement noise is negligible, the Filtered BackProjection (FBP) reconstruction technique (also known as the Convolution BackProjection technique, or CBP) is a popular method. Based on a discretization of an inverse formura*for the Radon transform, the FBP consists of filtering each projection by a prespecified filter, followed by a backprojection operation. The filtering operation requires 0(N 2 log N) operations when implemented by an FFT, or as little as 0(N 2 ) operations when implemented as a convolution with a fixed impulse response. In contrast, the backprojection operation, which computes for each pixel in the reconstructed image the sum of all line integrals that pass through that pixel, requires <9(N 3 ) operations, assuming a reconstruction of N 2 pixels, and projections at 0(N) angles. Backprojection, therefore, by far dominates the computational cost of FBP (or CBP) reconstruction. Traditionally, for medical applications, backprojection has been accomplished by special hardware that exploits parallelism of the backprojection process to try and achieve near real-time reconstruction of a single slice. See, e.g., U.S. Patent No. 4,042,811 and U.S. Patent No. 4,491,932. However, there still exists a lag in reconstruction time that is becoming increasingly important as technologies that are able to acquire data at ever faster rates are being introduced. With the increasing data rates, the FBP has become the bottleneck in the reconstruction and display process, and presents a barrier to real-time imaging. Existing fast algorithms for reconstruction are based on either the Fourier Slice Theorem, or on a multi-resolution resampling of the backprojection, such as Brandt's method of U.S. Patent No. 5,778,038, which is incorporated by reference in its entirety. Algorithms based on the Fourier Slice Theorem use interpolations to transform the Fourier projection data from a polar to a Cartesian grid, from which the reconstruction can be obtained by an inverse FFT. These fast algorithms, known as Fourier reconstruction algorithms (FRA), generally have 0(N 2 log N) complexity. J. Schomberg and J. Timmer, "The Gridding Method for Image Reconstruction by Fourier Transformation", IEEE Trans. Med. Imag., vol. 14, Sept. 1995. Unfortunately, the interpolation step generally requires a large number of computations to avoid the introduction of artifacts into the reconstruction. Experimental evidence indicates that for reasonable image sizes N < 10 3 , the realized performance gain of algorithms based on the Fourier Slice Theorem over the more straightforward FBP is significantly less than the potential N/log N speedup. This also generally comes at a loss in reconstruction quality as well. Brandt's method uses different nonuniformly sampled grids to efficiently represent the projections at different stages of the backprojection. The resulting algorithm has O (N 2 log N) complexity. The algorithm generates blurred reconstructions, so a postprocessing step involving a deconvolution with a Gaussian approximation to the point spread function is necessary to counteract the blurring. This deconvolution stop is not fully effective, and can lead to further artifacts. This algorithm, although potentially fast, does not achieve the accuracy of conventional FBP. Thus, there is a need for faster and accurate algorithms for reconstruction of tomographic images. Accordingly, one object of this invention is to provide new and improved reconstruction methods for creating images from projections. Another object is to provide new and improved methods for reconstructing tomographic images which are faster than existing methods. Yet another object is to provide new and improved methods for tomographic backprojection for use in all applications where existing methods are used, but which are faster than existing methods. DISCLOSURE OF THE INVENTION In keeping with one aspect of this invention, a method for backprojecting sinograms to images includes the steps of dividing a filtered sinogram into a plurality of subdivisions, each a smaller sinogram. The smaller sinograms are successively subdivided until the divisions represent a subimage as small as one pixel. The subsinograms are backprojected to create subimages, and the subimages are successively aggregated to create the image for display. The method uses two algorithms to subdivide the sinogram. For one algorithm subdivision is exact, and for the other algorithm the subdivision is an approximation. The first algorithm is accurate, but relatively slow, and the second algorithm is faster, but less accurate. By performing some subdivisions with the exact decomposition algorithm and some subdivisions with the approximation decomposition algorithm, switching between the two algorithms in one of a number of suitable ways, an accurate result can be obtained quickly. BRIEF DESCRIPTION OF THE DRAWINGS The above mentioned and other features of this invention and the manner of obtaining them will become more apparent, and the invention itself will be best understood by reference to the following description of an embodiment of the invention taken in conjunction with the accompanying drawings, in which: FIG. 1 is a block diagram of imaging apparatus made in accordance with the principles of the present invention; FIG. 2 is a diagram of the steps in a decomposition algorithm used in the present invention; FIG. 3 is a diagram of an additional decomposition algorithm used in the present invention; FIG. 4 is a graph showing the results obtained by the present invention, compared with known processes; FIG. 5 is a comparison of images obtained with the present invention and a known method; FIGS. 6(a) and 6(b) are slice plots showing the results obtained with the present invention, compared with the results of methods of the prior art; and FIG. 7 is a diagram of a conventional filtered backprojection method. DETAILED DESCRIPTION The present invention has application in a variety of imaging apparatus, including CT scanners. Typical imaging apparatus 1 (FIG. 1) includes a scanner 2 which acquires data from an object such as a head, and sends raw data to a receiver 3. The data is processed in a post-processor 4, which can include re- binning, filtering, or other processes. The post-processor 4 generates a sinogram which is backprojected in a Hierarchical BackProjection (HBP) apparatus 5. The HBP 5 produces an image which is shown on a display 6 or other suitable output device. A sinogram includes any two-dimensional array of numerical values in which one index is radial, and the other index is angular. Examples of a sinogram include a collection of projections, a collection of radially filtered projections, and a collection of synthetic-aperture -radar (SAR) data that has been radially-inverse-Fourier- transformed, and the like. To better understand the present invention, a general understanding of direct filtered backprojection (FBP) reconstruction will be given. As seen in FIG. 7, a sinogram 10, which in this case is data generated from a tomographic image, is directly backprojected to create an image 12, using known algorithms . While direct backproj ection has an acceptable degree of accuracy, it has a high computational cost and is time consuming. The present invention, called Hierarchical BackProjection, addresses some of the shortcomings of direct reconstruction and other algorithms. HBP methods are based on a hierarchical decomposition of the Radon transform, and require 0(N 2 log N) time for reconstruction. The algorithms used in HBP do not suffer from the artifacts from which FRA's suffer (as there are no frequency domain interpolations), and offer significant improvement in performance over the FRA's and Brandt's method, with little or no loss in reconstruction quality compared to the direct FBP reconstruction. The potential speedup for images of size 10 3 pixels square approaches and even exceeds two orders of magnitude. Thus, FBP-based reconstructions can be obtained in a small fraction of the time currently required. The proposed algorithms are also highly parallelizable, and can reuse existing hardware accelerations. Furthermore, the HBP algorithm produces reconstructions visually similar to the FBP. The basic principle behind the hierarchical decomposition is as follows: the backprojection of an Nx N image can be written as the sum of shifted backproj ections of 4 smaller images, each of size N/2 x N/2. Because these images are half the (linear) size of the original image, they can be computed from sinograms with half the number of angular samples. Thus, if N projections were available and needed for the reconstruction of the original image, then N/2 projections are needed to reconstruct the smaller images. Each of the 4 backproj ections of the subimages will cost (N/2) 2 N/2 = N 8 operations, and 4 of these backproj ections must be performed for a total cost of NV2 to reconstruct the object, compared with N 3 operations for the original backprojection. If this decomposition is applied recursively, then the cost is N 2 log N, reducing the image size and projection count by a factor of 2 at each stage. In the present invention, two algorithms are used to sub-divide the sinogram. The first algorithm is an exact decomposition algorithm which will be described in connection with FIG. 2, and the second algorithm is an approximation decomposition algorithm which will be described in connection with FIG. 3. The exact decomposition algorithm is accurate, but relatively slow, and the approximation decomposition algorithm is faster, but less accurate. By performing some subdivisions with each algorithm, switching between the two algorithms in one of a number of suitable ways, accurate reconstructions can be obtained quickly. When the exact decomposition algorithm is used (FIG. 2), the sinogram 10 is backproj ected to create the image 12 by dividing the sinogram into four subsinograms, 14, 16, 18 and 20, in processors (or steps) 13, 15, 17 and 19, respectively. Each subsinogram 14, 16, 18 and 20 is further subdivided, into a total of sixteen subsinograms (not shown), and each of those subdivided subsinograms are further subdivided, until the subsinograms represent an image as small as one pixel. The last subsinograms are then backproj ected to create the smallest subimages, shown diagramatically in FIG. 2 as subimages 22, 24, 26 and 28. The subimages are then successively aggregated to create the image 12. The algorithm used with the method shown in FIG. 2 is derived as follows. Consider an Nx N pixel object, with pixel size normalized to 1. Assume P view angles, uniformly spaced on [0, 2π], although the algorithm can be easily generalized to other view angle distributions. In order to simplify the expressions in this section as much as possible, an operator formulation will be used. The Hubert spaces that arise in the formulation are as follows: L 2 (R 2 ), the space of square-integrable functions supported on E 2 ; l (E), the space of -tuples of square-integrable functions; (Z 2 ), the space of square summable 2D sequences; and n p Z), the space of -tuples of square summable sequences. All spaces are equipped with the standard, unweighted inner products . A version of discrete backproj ection will be used that incorporates a modification that is required to construct the hierarchical decomposition. The discrete backprojection operator is a map B : ζ(Z) "t 2 (Z 2 ) that produces a discrete image (element of # 2 (Z 2 )) with N x N support from P discrete projections (each an element of £ 2 (Z)). B P N is affected by a further parameter τ e [-.5, .5] p , a vector of radial shifts, whose purpose will be explained shortly. The dependence of B P N on τ is suppressed for notational convenience. This discrete backproj ection operator is decomposed into a product of four simpler operators. Specifically, where is the radial interpolation operator with shifting by τ I≠TJ>) =∑ g(k,p) φ(r-(k+τ )T), where T is the radial sampling period, and φ is the convolutional interpolation kernel used. This operator interpolates each of the projections, shifting the ?-th projection by τ p . For the case of simple linear interpolation, φ(x) = A(x/T), where — | J j \x < 1 ΛW= i else The operator L (R) 2 (R 2 ) is the backprojection operator for unsampled sinograms, defined by The next operator S : L 2 (I 2 ) → d 2 (Z 2 ) is the mollification, or sampling operator that represents the backproj ected function on a discrete grid. Assume that this operation is shift invariant, and that the coordinates have been scaled so that S can be written as ! f (x,y)b(x-i, y-j)dx dy. The function b(x,y) may be an indicator function for a pixel, or some smoother function. Assume that b(x,y) is chosen to be smooth and have small support. Suggestions for b(x,y) include tensor splines, truncated Gaussians, and smooth, rotationally symmetric functions. The final two operators which will be used are truncation operators. The first truncation operator K N . Q 2 (Z 2 ) >→ H 2 (Z 2 ) windows the argument 2D sequence to a square of size N x N, approximately centered at the origin (depending on whether N is odd or even), Λ The second truncation operator jr -. H (Z) « | (Z) windows the argument radially to a width of M + λ(φ), where λ(φ) is the length of the support of the interpolation kernel in I τ . If φ is a sine kernel, then clearly K N has no effect on the input sequence. To derive the hierarchical decomposition, the interaction of B P N with two operations should be examined: 2D shifts (translations) of the output, and truncations of the output. To examine this interaction in detail, a series of simple properties will be presented, each of which explicitly describe the interaction of various shift and truncation operations with each of the operators defined above. Proofs of these properties are omitted, as they follow directly from the appropriate definitions. First, four shift operators will be defined. The first is a discrete shifter Z δ : i 2 (Z 2 ) ~ C 2 (Z 2 ), with δ Z 2 , defined by Z h f (i ) = f (i - ύ - ό,). The second operator is a continuous shifter M 6 : Z 2 (R 2 ) >→ 2 (R 2 ), with δ e 2 , defined by M δ f c (x,y) = f c (x - δ j , y - δ 2 ). Consider two sinogram domain shift operators, one discrete and one continuous. The first is Z δ : Up (Z) » d (Z) for δ e Z p , defined by Z δ g (k,p) = g (k- δ p ,p) . Λ p p The second is a continuous shifter M : L (R) >→ L (R), defined by M b g c (r,p) = g c (r~δ p , p), where δ 6 R p . Property 1 (Shift property of Bp). M b B p g c (x,y) = B p M w )b g c (x,y), where δ e R 2 , and W : [0, 2π] p >→ R Px2 is defined by Property 2 (Shift property of S). As would be expected from the shift invariance of S, Z b Sf c (i,j) = SM δ f c (i,j) for δ e Z 2 . Property 3 (Shift property of I τ ). M. b l (r,p = I <τ+blT> Z_ [τ+blT] g(r,p) where [ ] ; is rounded to the nearest integer, and < x = x - [x] t . Property 4 (Truncation of PN input). The hierarchical decomposition of the backprojection operator is shown in the following theorem. Theorem 1. Fof . N = 2 K for K E Z, K≥l, B P;N £(-,/) = ∑Z δι B pN/2 K N/y/2 Z_ [v] g(i ) , where for N>2, δ. = [N/4,N/4] T , δ 2 = [-N/4,JV/4] r , δ 3 = [JV74, -N/4] τ , δ 4 = [-NIA, -N/4] τ and for N = 2, δ_ = [W δ 2 = [0,0] 3 δ 3 - [i.ir δ 4 = [0,1] 3 In all cases, , = Wφ) δ, To summarize, the decomposition of Theorem 1 consists of terms of the form B P,N/2 Z_ [V/] g( j) , which is essentially the backprojection of an N/2 x N/2 quadrant of the original image, using the same number of projections P. The decomposition consists of the following steps. 1. The projections are shifted so that they correspond to the zth quadrant of the image being centered at the origin. 2. The projections are then radially truncated to the width used by the backproj ection B p N/2 . Steps 1 and 2 above describe the processing steps 13, 15, 17 and 19 of FIG. 2. FIG. 3 is a diagram of the method of the present invention when the approximation decomposition algorithm is used. As in FIG. 2, the sinogram 10 is subdivided into successively smaller subsinograms until the smallest sinograms correspond to subimages as small as one pixel in size. Thus, for example, if the sinogram 10 corresponded to a four pixel size image, the sinogram would be divided into four subsinograms 30, 32, 34 and 36, which are processed using an approximation described below, in steps shown at 38, 40, 42 and 44. The subsinograms 30, 32, 34 and 36 are then backprojected to form subimages 46, 48, 50 and 52. The subimages 46, 48, 50 and 52 are then aggregated in processing steps 54, 56, 58 and 60, to form the image 12. Generally, this algorithm corresponds to the following steps: 1. The sinograms are radially shifted so that they correspond to the z ' th quadrant of the reconstruction being centered at the origin. 2. The sinograms are angularly resampled to reduce the number of angles by a factor of two. 3. The sinograms are radially truncated to the width used by the backprojection. Steps 1 through 3 above describe the processing in steps 38, 40, 42 and 44 in FIG. 3, but not steps 13, 15, 17 and 19 in FIG. 2. The approximate decomposition algorithm used in the processing steps of FIG. 3 will now be described in greater detail. Instead of backproj ecting the smaller N/2 x N/2 subimages using all P projections, as prescribed in Theorem 1, the sinograms are angularly re-sampled from P to P/2 projections before backproj ecting, so that each term in the sum involves a backprojection to an Ν/2 x N/2 size object using P/2 projections. This basic concept will be developed into the approximation decomposition algorithm used in FIG. 3. Next, define the angular resampling operator ° Pl ~ P 2 ■ L P 2 l (R) - 2 (R) that angularly resamples g c by the ratio P 2 /P v The operator can be written as O p where ψ is the angular interpolation kernel (a Dirichlet kernel for the case of uniformly spaced angles and angularly bandlimited g c ). After sub-dividing the sinogram for a Nx N image into subsinograms for N/2 x N/2 subimages, only P/2 projections are needed to accurately reconstruct the subimages. Hence, replace the term B P,Ν/2 Sc in Theorem 1 by the term °P/2,N/2 ^P ~P12 c where g c is radially bandlimited, with bandlimit B. The decomposition of Theorem 1 can be approximated (if I τ is an ideal bandlimited radial inteφolator with bandlimit E) by 4 B P,N g( m >") a Ε . =1 Z δ t K Ni2 S pnjrn °P»PII 7 <V ( > Z -[vj g( m > n • The second step involves the interchange of p „ p/2 and I . For simplicity, define D 2 = O P ^ P/2 , and define a decimator • 1 2 : R p >→ R p/2 as (x 1 2) = x 2p . Suppose I τ is a perfectly bandlimited inteφolator, with φ bandlimited to radial bandwidth B< 1/(27). Then for any sequence g e (Z) 7 τ g can be exactly inteφolated from radial samples at rate T< 1/25 using bandlimited inteφolation. It follows that for each / ' = 1 , . . . 4, where P/2 P/2 G τ : L (R) - f 2 (Z) is a generalized resampling operator, defined by G τ g c (k,p) = g c ((k+X p ) T,p) . This decomposition is exact when -^ <Vj> is ideal bandlimited inteφolation. In practice, good approximations can be obtained with faster decaying inteφolation schemes if \IT» B. The operation A p> . : ζ(Z)"ζ n (Z) where is a discrete to discrete mapping that incoφorates the radial inteφolation, angular smoothing and decimation, and radial sampling all in one step. Using this definition, the final form for the approximate decomposition of the backprojection operator is 4 4 B pjf g (i, j) « ∑ Z b K m SB m < 12 -4 / Z g (i,j) = ∑ Z δ B pn A p ι Z g (i, j) 1=1 /=1 Reincoφorating the radial truncation K, the final decomposition of B P N is 4 Σ Λ Λ Z δ, B f>/2 /2 K NlXΪ A PJ Z -[VJ f J) . =1 The decomposition of this formula is illustrated in FIG. 3 for comparison with FIG. 2. The algorithms used in the process of FIG. 2 produce an accurate result, but by themselves do not reduce the computational cost of processing. The algorithms used in the process of FIG. 3 are fast, but less accurate because of the approximations used. However, by combining the processes of FIGS. 2 and 3, speed can be greatly increased without significantly compromising accuracy, by artificially increasing the sampling rate in the process. The two algorithms can be used in a variety of combinations, such as the combination used in the following psuedocode: algorithm initialized with 0. — Q function/= HBP (τ, θ, g, d) N= size (f) if (N >1 pixel) /= 0 for ( = l; z ' < 4; / + +) if (fi < 0) (τ J 2, Θ I 2,K Ν/ ^A p i Z_ [v ^, fi) /o = HBP else end if end for else /= BP(τ, θ, g) end if end function where BP(τ,θ) implements f = B PN g. The exact decomposition algorithm is used Q consecutive times, and the approximate decomposition algorithm is used for the remaining recursions. The parameter Q is chosen to obtain the desired accuracy and speed. Angular oversampling and radial oversampling can be used before decompositions to increase accuracy, which allows Q to be reduced. This increases the decomposition speed because the approximate decomposition algorithm can be used more often without losing accuracy. Under the assumption that the processing in blocks 38, 40, 42 and 44 of FIG. 3 can be completed in 0(PN) time, where the subsinograms in blocks 30, 32, 34 and 36 are size P x N, the operation count for the psuedocode above is CY2 β iW(log 2 N - Q + l)), which reduces to PN\og 2 N for > = O, and to PN 2 for Q = log 2 N. The method of the present invention was tested on a Shepp-Logan head phantom. Similar results were obtained using other types of phantoms. Analytical projections of the Shepp-Logan phantom were computed, and then reconstructions performed on a 256 x 256 pixel grid. The distortion was measured on the interior portion of the skull, which was assumed to be the region of interest. HBP was used with ramp-filtered projections to form the reconstructions. FIG. 4 depicts the performance of different algorithms in terms of speed-up and accuracy. Here, points (represented by runs of the algorithm with different parameter choices) further to the right represent greater speedup, while points closer to the -axis represent more accurate reconstructions. Compare the reconstruction by gridding and Brandt's method to the reconstructions of the present invention. The curve for the FRA is obtained by performing reconstructions for a variety of choices of K. The curve for Brandt's method was obtained by varying the minimum number of samples to take in the slow direction of any grid. The curves for the present invention were obtained by varying Q and the amount of radial oversampling. Q decreases as the speed increases, indicated by movement to the right along the x-axis. FIG. 4 shows that for this phantom, and these implementations, the FRA and Brandt's method are uniformly and significantly outperformed by the proposed algorithm. Attempts to improve the performance of the FRA by changing the parameters recommended in J. Schomberg and J. Timmer, "The Gridding Method for Image Reconstruction by Fourier Transformation", IEEE Trans. Med. Imag., vol. 14, Sept. 1995 were unsuccessful. Attempts to improve the performance of Brandt's method by deviating from the guidelines of U.S. Patent No. 5,778,038 were also unsuccessful. FIG. 4 also shows the results obtained using the direct FBP method of FIG. 7, to which FIG. 4 is normalized. The improvement over FBP realized by the present invention is also apparent from a review of FIG. 4. As a final subjective comparison, FIG. 5 depicts full 1024 x 1024 reconstructions from 1024 uniformly spaced projections on [0, π) using the HBP (left) and FBP (right) algorithms. Here, a truncation was applied to all densities above 0.05 to show the detail of the skull and any artifacts exterior to the skull. The full reconstructions are visually indistinguishable, but the reconstruction on the left was over 90 times faster than the reconstruction on the right. Greater performance gains should be obtainable through the obvious parallel implementation of the algorithm and optimization of the code. Slice plots through the rows and columns of these two reconstructions, shown in FIGS. 6(a) and 6(b), reinforce the visual assessment. On the basis of this phantom and repeated experiments for different sized pixel grids (up to 1024 x 1024), an empirical speedup of ~ N/log N for reconstructions of visual quality similar to the FBP via the HBP algorithm is suggested. Extrapolating this speedup suggests that for high resolution medical imaging, with 4096 x 4096 pixel images, speedups of greater than two orders of magnitude should be achievable with reconstructions of visual quality comparable to the FBP. A class of fast algorithms for fast backprojection from 2D tomographic data have been developed. Methods using these algorithms provide orders of magnitude speedup in reconstruction time with little or no added distortion. The proposed algorithms are parallelizable, simple, and outperform Fourier Reconstruction Algorithms as well as Brandt's method in terms of reconstruction distortion and CPU time. While the principles of the invention have been described above in connection with specific apparatus and applications, it is to be understood that this description is made only by way of example and not as a limitation on the scope of the invention.

Claims: CLAIMS: 1. A process for generating an electronic image (12) from a sinogram (10) characterized by the steps of: subdividing the sinogram (10) into a plurality of subsinograms (14, 16, 18, 20); backproj ecting each of said subsinograms (14, 16, 18, 20) to produce a plurality of corresponding subimages (22, 24, 26, 28), and aggregating said subimages (22, 24, 26, 28) to create the electronic image (12). 2. The method according to claim 1 further characterized in that said subdividing step includes performing a number of approximations. 3. The method according to claim 1 further characterized in that said subdividing step includes performing a number of exact subdivisions. 4. The method according to claim 1 further characterized in that said sinogram (10) is subdivided into a plurality of subsinograms (14, 16, 18, 20) in a recursive manner, wherein said subdividing steps include a number of exact subdivisions and a number of approximate subdivisions. 5. The method according to claim 1 further characterized in that said aggregation step of subimages (22, 24, 26, 28) is performed in a recursive manner. 6. The method according to claim 1 further characterized in that said electronic image is a tomographic image. 7. The method according to claim 1 further further characterized in that preprocessing in which angular and radial oversampling are used to improve the accuracy of the electronic image. 8. The method according to claim 1 further characterized in that said subsinograms (14, 16, 18, 20) are subdivided in a recursive manner, until each subsinogram (14, 16, 18, 20) represents an image of a desired size. 9. The method according to claim 8 further characterized in that said subsinogram images (14, 16, 18, 20) are one pixel in size. 10. The method according to claim 1 further characterized in that the sinogram (10) includes filtered proj ections . 11. Apparatus ( 1 ) for generating an electronic image of an obj ect characterized by ; means (2) for scanning the object to generate data representing an image of the object; means (4) for processing said data to generate a sinogram which includes a plurality of filtered projections; means (5) for subdividing said sinogram into a plurality of subsinograms; means (5) for backproj ecting each of said subsinograms to produce a plurality of corresponding subimages; means (5) for aggregating said subimages to create the electronic image; and means (6) for displaying the electronic image. 12. The apparatus according to claim 11 further characterized in that said means for backproj ecting performs a number of approximate subdivisions. 13. The apparatus according to claim 11 further characterized in that said means for backproj ecting performs a number of exact subdivisions. 14. The apparatus according to claim 11 further characterized in that said sinograms are subdivided into a plurality of subsinograms in a recursive manner, wherein said means (5) for subdividing performs a number of exact subdivisions and a number of approximate subdivisions. 15. The apparatus according to claim 11 further characterized in that said means for aggregating operates in a recursive manner. 16. The apparatus according to claim 11 further characterized in that in said electronic image is a tomographic image. 17. The apparatus according to claim 11 further characterized in that said means (10) for processing performs angular and radial oversampling to improve the accuracy of the electronic image. 18. The apparatus according to claim 11 further characterized in that said means (5) for subdividing operates in a recursive manner, until each subsinogram represents an image of a desired size. 19. The apparatus according to claim 18 further characterized in that said subsinogram images are one pixel in size.

Inventors: BASU SAMIT, BRESLER YORAM, BASU, SAMIT, BRESLER, YORAM
Applicants: UNIV ILLINOIS, THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS
Application year: 2000
Main classification: A61B6
IPC codes: A61B6, G06T11