Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
  • A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
  • A61B6/03—Computerised tomographs
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T11/00—2D [Two Dimensional] image generation
  • G06T11/003—Reconstruction from projections, e.g. tomography
  • G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
  • A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
  • A61B6/027—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2211/00—Image generation
  • G06T2211/40—Computed tomography
  • G06T2211/416—Exact reconstruction
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2211/00—Image generation
  • G06T2211/40—Computed tomography
  • G06T2211/421—Filtered back projection [FBP]
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
  • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10S378/00—X-ray or gamma ray systems or devices
  • Y10S378/901—Computer tomography program or processor

Definitions

• This invention relates to computer tomography, and in particular to processes and systems for reconstructing three dimensional images from the data obtained by a spiral scan, and this invention claims the benefit of priority to U.S. Provisional Application 60/312,827 filed August 16, 2001.
• CT computer tomography
• exact algorithms can provide a replication of an exact image.
• exact algorithms can be known to take many hours to provide an image reconstruction, and can take up great amounts of computer power when being used. These algorithms can require keeping considerable amounts of cone beam projections in memory. Additionally, some exact algorithms can require large detector arrays to be operable and can have limits on the size of the patient being scanned.
• Approximate algorithms possess a filtered back projection (FBP) structure, so they can produce an image very efficiently and using less computing power than Exact algorithms. However, even under the ideal circumstances they produce an approximate image that may be similar to but still different from the exact image. In particular, approximate algorithms can create artifacts, which are false features in an image. Under certain circumstances these artifacts could be quite severe.
• FBP filtered back projection
• a primary objective of the invention is to provide an improved process and system for reconstructing images of objects that have been scanned in a spiral fashion with two-dimensional detectors.
• a secondary objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that is known to theoretically be able to reconstruct an exact image and not an approximate image.
• a third objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that creates an exact image in an efficient manner using a filtered back projection (FBP) structure.
• a fourth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that creates an exact image with minimal computer power.
• a fifth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that creates an exact image with an FBP structure.
• a sixth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects with larger pitch, leading to faster scans than previous techniques.
• a seventh objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects which take less time than current techniques, thereby allowing use in everyday clinical applications.
• An eighth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that is CB projection driven allowing for the algorithm to work simultaneously with the CB data acquisition.
• a ninth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that does not requiring storage for numerous CB projections in computer memory.
• a tenth objective of the invention is to provide an improved process and system for reconstructing images of spirally scanned objects that allows for almost real time imaging to occur where images are displayed as soon as a slice measurement is completed.
• a first preferred embodiment of the invention uses a six overall step process for reconstructing the image of an object under a spiral scan.
• a current CB projection is measured.
• a family of lines is identified on a detector according to a novel algorithm.
• a computation of derivatives between neighboring projections occurs and is followed by a convolution of the derivatives with a filter along lines from the selected family of line.
• the image is updated by performing back projection.
• the preceding steps are repeated for each CB projection until an entire object has been scanned.
• This embodiment works with keeping several (approximately 2-4) CB projections in memory at a time and uses one family of lines.
• the novel algorithm allows for one CB projection to be kept in memory at a time and one family of lines is used.
• FIG. 1 shows a typical arrangement of a patient on a table that moves within a rotating gantry having an x-ray tube source and a detector array, where cone beam projection data sets are received by the x-ray detector, and an image reconstruction process takes place in a computer with a display for the reconstructed image.
• Fig. 2 shows an overview of the basic process steps of the invention.
• Fig. 3 shows mathematical notations of the spiral scan about the object being scanned.
• Fig. 4 illustrates a PI segment of an individual image reconstruction point.
• Fig. 5 illustrates a stereographic projection from the current source position on to the detector plane used in the algorithm for the invention.
• Fig. 6 illustrates various lines and curves, such as boundaries, on the detector plane.
• Fig. 7 illustrates a family of lines used in the algorithm of the invention.
• Fig. 8 is a four substep flow chart for identifying the set of lines, which corresponds to step 20 of Fig. 2.
• Fig. 9 is a seven substep flow chart for preparation for filtering, which corresponds to step 30 of Fig. 2.
• Fig. 10 is a seven substep flow chart for filtering, which corresponds to step 40 of Fig.
• Fig. 11 is an eight substep flow chart for backprojection, which corresponds to step 50 of Fig. 2.
• Fig. 12 illustrates the first family of lines used in Embodiment Three of the invention.
• Fig. 13 illustrates the second family of lines used in Embodiment Three of the invention.
• FIRST EMBODIMENT Fig. 1 shows a typical arrangement of a patient on a table that moves within a rotating gantry having an x-ray tube source and a detector array, where CB projections are received by the x-ray detector, and an image reconstruction process takes place in a computer 4 with a display 6 for displaying the reconstructed image.
• the detector array is a two-dimensional detector array.
• the array can include two, three or more rows of plural detectors in each row. If three rows are used with each row having ten detectors, then one CB projection set would be thirty individual x-ray detections.
• Fig. 2 shows an overview of the basic process steps of the invention that occur during the image reconstruction process occurring in the computer 4 using a first embodiment.
• the first embodiment works with keeping several (approximately 2-4) CB projections in computer memory at a time and uses one family of lines.
• the next step 20 identifies a set of lines on a virtual x-ray detector array according to the novel algorithm, which will be explained later in greater detail. In the given description of the algorithm it is assumed that the detector array is flat, so the selected line can be a straight tilted line across the array.
• the next step 30 is the preparation for the filtering step, which includes computations of the necessary derivative of the CB projection data for the selected lines.
• the next step 40 is the convolution of the computed derivative (the processed
• s is a real parameter
• h is pitch of the spiral
• R is distance from the x-ray source to the isocenter.
• the object being scanned is located inside an imaginary cylinder U of radius r , r ⁇ R (see Fig.3).
• y(s ⁇ >) > y( s ⁇ > y( s 2 are tnree P°' nts on me s P' ra l related according to (4), (5);
• u(s 0 ,s 2 ) is a unit vector perpendicular to the plane containing the points ( o) > ; ( .)> ⁇ ( ⁇ );
• y(s) : dylds ;
• a new detector arrangement and a new completeness condition in "Proc. 1997 Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine (Pittsburgh)", eds. D. W. Townsend and P. E. Kinahan, yr. 1997, pp. 141-144, and M. Defrise, F. Noo, and H. Kudo "A solution to the long-object problem in helical cone-beam tomography", Physics in Medicine and Biology, volume 45, yr. 2000, pp. 623 — 643).
• a PI segment is a segment of line endpoints of which are located on the spiral and separated by less than one pitch in the axial direction (see Fig. 4).
• / is the function representing the distribution of the x-ray attenuation coefficient inside the object being scanned
• e(s,x) ⁇ (s,x) x u(s,x)
• D f is the cone beam transform of / :
• ⁇ (s, ⁇ ) ⁇ " —D f (y(q), C osy ⁇ + sin ⁇ e(s, ⁇ )) -d ⁇ , sinr
• the top and bottom curves are denoted r, and Y hl)l , respectively (see Fig. 6 which illustrates various lines and curves, such as boundaries, on the detector plane).
• the common asymptote of T ' and T bol is denoted L 0 .
• Let x denote the projection of x . Since s e I PI (x) , x is projected into the area between r, and Y bol (see Fig. 6).
• Equation (16) is of convolution type and one application of Fast Fourier Transform (FFT) gives values of ⁇ (s, ⁇ ) for all ⁇ e Yl(s 2 ) at once. Equations (13) and (16) would represent that the resulting algorithm is of the FBP type.
• FFT Fast Fourier Transform
• each CB projection is stored in memory as soon as it has been acquired for a short period of time for computing this derivative at a few nearby points and is never used later.
• Step 10 Load the current CB(cone beam) projection into computer memory.
• the mid point of the CB projections currently stored in memory is y(s 0 ) •
• the detector plane corresponding to the x-ray source located at y(s Q ) is denoted DP(s 0 ) .
• Fig. 8 is a four substep flow chart for identifying the set of lines, which corresponds to step 20 of Fig. 2. Referring to Fig. 8, the set of lines can be selected by the following substeps 21, 22, 23 and 24.
• Step 21 Choose a discrete set of values of the parameter s 2 inside the interval
• Step 22 For each selected s 2 compute the vector u(s 0 ,s 2 ) according to equations (7), (8). Step 23. For each u(s 0 ,s 2 ) computed in Step 22 find a line which is obtained by intersecting the plane through y(s 0 ) and perpendicular to the said vector u(s 0 ,s 2 ) with the detector plane DP(s 0 ) . Step 24. The collection of lines constructed in Step 23 is the required set of lines (see Fig. 7 which illustrates a family of lines used in the algorithm of the invention).
• Fig. 9 is a seven substep flow chart for preparation for filtering, which corresponds to step 30 of Fig. 2, which will now be described.
• Step 31 Fix a line L(s 2 ) from the said set of lines obtained in Step 20.
• Step 32 Parameterize points on the said line by polar angle ⁇ in the plane through y(s 0 ) and L(s 2 ) .
• Step 33 Choose a discrete set of equidistant values y y that will be used later for discrete filtering in Step 40.
• Step 34 For each ⁇ find the unit vector /? y which points from y(s 0 ) towards the point on L(s 2 ) that corresponds to ⁇ .
• Step 35 Using the CB projection data D f (y(q), ⁇ ) for a few values of q close to s 0 find numerically the derivative (dldq)D j (y(q), ⁇ ) ⁇ for all
• Step 36 Store the computed values of the derivative in computer memory.
• Step 37 Repeat Steps 31-36 for all lines L(s 2 ) identified in Step 20. This way we will create the processed CB data ⁇ (s 0 , ⁇ j ) corresponding to the x-ray source located at y(s 0 ) .
• Fig. 10 is a seven substep flow chart for filtering, which corresponds to step 40 of Fig. 2, which will now be described.
• Step 41 Fix a line from the said family of lines identified in Step 20.
• Step 42 Compute FFT of the values of the said processed CB data computed in Step 30 along the said line.
• Step 43 Compute FFT of the filter 1 /sin/ Step 44. Multiply FFT of the filter 1 /siny (the result of Steps 43) and FFT of the values of the said processed CB data (the result of Steps 42).
• Step 45 Take the inverse FFT of the result of Step 44.
• Step 46 Store the result of Step 45 in computer memory.
• Step 47 Repeat Steps 41-46 for all lines in the said family of lines. This will give the filtered CB data ⁇ (s 0 , ⁇ j ) .
• Fig. 11 is an eight substep flow chart for backprojection, which corresponds to step 50 of Fig. 2, which will now be described.
• Step 51 Fix a reconstruction point x , which represents a point inside the patient where it is required to reconstruct the image.
• Step 52. If s 0 belongs to I PI (x) , then the said filtered CB data affects the image at x and one performs Steps 53-58. If .s 0 is not inside the interval I P1 (x) , then the said filtered CB data is not used for image reconstruction at x . In this case go back to Step 51 and choose another reconstruction point.
• Step 53 Find the projection x of x onto the detector plane DP(s 0 ) and the unit vector ⁇ (s Q ,x) , which points from y(s 0 ) towards .
• Step 54 Using equation (9) identify the lines from the said family of lines and points on the said lines that are close to the said projection x . This will give a few values of ⁇ (s 0 , ⁇ j ) for ⁇ ⁇ close to ⁇ (s 0 ,x) .
• Step 55 With interpolation estimate the value of ⁇ (s 0 , ⁇ (s 0 , x)) from the said values of ⁇ (s 0 , ⁇ ] ) for ⁇ ⁇ close to ⁇ (s Q ,x) .
• Step 56 Compute the contribution from the said filtered CB data to the image being reconstructed at the point x by dividing ⁇ (s 0 , ⁇ (s 0 ,x)) by
• Step 57 Add the said contribution to the image being reconstructed at the point x according to a pre-selected scheme (for example, the Trapezoidal scheme) for approximate evaluation of the integral in equation (15).
• Step 58 Go to Step 51 and choose a different reconstruction point .
• Step 60 Go to Step 10 (Fig. 2) and load the next CB projection into computer memory.
• the image can be displayed at all reconstruction points x for which the image reconstruction process has been completed (that is, all the subsequent CB projections are not needed for reconstructing the image at those points). Discard from the computer memory all the CB projections that are not needed for image reconstruction at points where the image reconstruction process has not completed.
• the algorithm concludes when the scan is finished or the image reconstruction process has completed at all the required points.
• one CB (cone beam) projection can be kept in memory at a time and, as before, only one family of lines on the detector is used.
• Integrating by parts with respect to s in equation (10) we obtain an inversion formula in which all the derivatives are performed with respect to the angular variables.
• equation (17) admits absolutely analogous filtered back-projection implementation. Moreover, since no derivative with respect to the parameter along the spiral is present, there is never a need to keep more than one CB projection in computer memory at a time. Now we describe the algorithm in detail.
• Step 10 Load the current CB projection into computer memory and discard the CB projection that was in computer memory before.
• the CB projection just loaded into computer memory corresponds to the x-ray source located at y(s) .
• Step 20 is the same as Step 20 in Embodiment One with s 0 replaced by s .
• Step 30 Preparation for filtering
• Steps 31-34 are the same as in Embodiment One with s 0 replaced by s .
• Step 35 Using the CB projection data D / (y(s), ⁇ ) find D J (y(s), ⁇ ] ) and the
• Step 36 Store the values computed in Step 35 in computer memory.
• Step 37 Repeat Steps 31-36 for all lines L(s 2 ) identified in Step 20. This way we will create the processed CB data D j (y(s), ⁇ ⁇ ) , (V u( ⁇ x) D f )(y(s), ⁇ J ) , and
• Step 40 Filtering Step 41. Fix a line from the said family of lines identified in Step 20.
• Step 42 Using FFT convolve the said processed CB data computed in Step 30 with filters 1 /sin r and coty along the said line according to equation (17). This will give the following three kinds of the filtered CB data (see also equations (12), (15), and (16)):
• ⁇ 2 (s, ⁇ ) ⁇ ⁇ (V X) D f )(y(s),cos ⁇ + sm ⁇ e(s, ⁇ ))c t( ⁇ )d ⁇ ,
• ⁇ 3 (s, ⁇ ) " ( ⁇ -D f (y( S ),cos ⁇ + sm ⁇ e(s, ⁇ ))) ⁇ -
• Step 43 Using the processed CB data (V u ⁇ i x) D f )(y(s), ⁇ j ) evaluate numerically the integral
• V s > ) + sm ⁇ e( S , ⁇ ))d ⁇ .
• Step 44 Store the results of Step 42 and 43 in computer memory.
• Step 47 Repeat Steps 41-44 for all lines in the said family of lines.
• Step 50 Back-projection Steps 51-53 are the same as in Embodiment One with s 0 replaced by s .
• Step 57 Add the said quantity A(s,x) to the image being reconstructed at the point x according to a pre-selected scheme (for example, the Trapezoidal scheme) for approximate evaluation of the integral with respect to s in equation (17).
• Step 58 If the parameter value s corresponding to the CB projection, which is currently in computer memory, is close to a boundary point of the parametric interval I PI (x) - either s h (x) or s, (x) , then using interpolation find
• s' is either s b (x) or s,(x) .
• Step 59 Go to Step 51 and choose a different reconstruction point x .
• Step 60 Go to Step 51 and choose a different reconstruction point x .
• Step 61 Fix a reconstruction point x . If all the subsequent CB projections are not needed for reconstructing the image at this point, divide the value of the computed image at x by -2 ⁇ 2 and display the image at on the computer display 6 of Fig. 1. Repeat this step for all the reconstruction points.
• Step 62 If not all the CB projections have been processed, go to Step 10 and load the next CB projection into computer memory. The algorithm concludes if the remaining CB projections are not needed for image reconstruction at any of the reconstruction points x or if there are no more CB projections to process.
• e,(s,x) is a unit vector in the plane through y(s) and spanned by ⁇ (s,x),y(s) .
• e t (s,x) 1 ⁇ (s,x) .
• e 2 (s,x) is a unit vector in the plane through x,y(s), and tangent to C p ⁇ (x) at y(s lan ) .
• Equation (30) is of convolution type. Hence, one application of FFT to the integral in equation (30) gives values of ⁇ ,(s,/?) for all ⁇ e YL( ⁇ ) at once.
• Equation (35) is of convolution type and one application of FFT gives values of
• the points on L( ⁇ ) and L(s wn ) can be parameterized by polar angle in the corresponding plane.
• Equations (27), (30), and (33), (35) demonstrate that the resulting algorithm is of the FBP type.
• the second step is back-projection according to (27) and (33).
• Step 10 is the same as Step 10 in Embodiment One.
• Step 20 Selecting the two sets of lines.
• Step 21 Choose a discrete set of values of the parameter ⁇ inside the interval ⁇ mm ⁇ ⁇ ⁇ max (see Fig. 12). This will give a collection of planes Ll( ⁇ > y ) containing y(s 0 ) and parallel to y(s 0 ) .
• Step 22 Intersections of LI( ⁇ > ) with the detector plane DP(s 0 ) generates the first family of lines L( ⁇ ) parallel to L 0 (see Fig. 12).
• Step 23 Choose a discrete set of values of the parameter _? inside the interval [s 0 - 2 ⁇ + A, s 0 + 2 ⁇ - A] .
• the family of lines L(s lan J ) obtained by intersecting .(s lan j ) , selected in Step 24, with the detector plane DP(s 0 ) is the required second family of lines (see Fig. 13). Step 30. Preparation for filtering
• Step 30 of Embodiment One This step is essentially the same as Step 30 of Embodiment One. The minor differences are as follows. Here this step is used twice. The first time it is applied to the first family of lines L( ⁇ ) and gives the first processed CB data
• Step 40 Filtering
• the filtering step is also essentially the same as Step 40 of Embodiment One.
• the back-projection step is also essentially the same as Step 50 of Embodiment One. The only difference is that here Steps 51-56 are used twice.
• Step 57 Following equation (24), add the said contributions from the first and second back-projected CB data to the image being reconstructed at the point x according to a pre-selected scheme (for example, the Trapezoidal scheme) for approximate evaluation of the integrals in equation (25).
• a pre-selected scheme for example, the Trapezoidal scheme
• Step 58 is the same as Step 58 in Embodiment One.
• Step 60 is the same as Step 60 in Embodiment One.
• Embodiments of the invention are possible. For example, one can integrate by parts in equation (25) (similarly to what was done with equation (10) - see equation (17)), to get an exact FBP-type inversion formula which requires keeping only one CB projection in computer memory.
• the algorithmic implementation of this alternative embodiment will be very similar to the algorithmic implementation of Embodiment Two.

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