Method and apparatus for identifying and correcting image inaccuracies caused by simplified processing of masked cone beam projection data

• US 6,324,245 B1
• Filed: 10/17/2000
• Issued: 11/27/2001
• Est. Priority Date: 10/17/2000
• Status: Expired due to Fees

• Current Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Tam, Kwok
• Primary Examiner(s): DUNN, DREW A
• Application Number:
  US09/690,630
• Time in Patent Office: 406 Days
• Field of Search: 378/4, 378/8, 378/15, 378/901, 382/131
• US Class Current: 378/4
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 2211/416 Exact reconstruction
• CPC Class Codes: Y10S 378/901 Computer tomography program or processor

Abstract

A method and apparatus for three dimensional (3D) computerized tomographic (CT) imaging of an object, wherein image reconstruction processing is applied to a plurality of sets of 2D cone beam projection data, each set being acquired on a 2D detector at a corresponding plurality of scan path source positions. A first image reconstruction processing step comprises applying a mask to each set of the projection data so that data inside the boundaries of each mask form a corresponding plurality of masked 2D data sets. Next, the data inside each masked 2D data set is processed along line segments formed in the masked 2D data set, and having their endpoints determined by the mask boundaries, to develop a first 2D estimate of data determined from a given set of the 2D cone beam projection data. The next step comprises identifying portions of said line segments which is not used to develop said first 2D estimate of data, to develop 2D correction data for each of the first 2D estimates of data. The final step comprises combining each of the first estimates of data and the 2D correction data calculated therefore, in a process which reconstructs an exact 3D image of the object.

Claims

1. A method for three dimensional (3D) computerized tomographic (CT) imaging of an object, comprising:

acquiring a plurality of sets of 2D cone beam projection data by irradiating the object with energy from a cone beam source that is directed toward a 2D detector at a corresponding plurality of scan path source positions located about the object;

applying a mask to each set of the 2D cone beam projection data to form a corresponding plurality of masked 2D data sets;

processing line integrals in the 2D cone beam projection data along a plurality of line segments L(s,θ

) having their end points determined by the boundaries of each of the masked 2D data sets to develop a corresponding plurality of processed 2D data sets, each processed 2D data set corresponding to calculation of a first data estimate determined for a given set of the 2D cone beam projection data;

developing 2D correction data for each of the first data estimates by identifying and processing a portion of each of said line segments which is formed by a second intersection with said data combination mask and therefore is not used to develop said first data estimates, and processing said identified portions of said line segments to develop said 2D correction data; and

combining each of said first data estimates with the 2D correction data developed therefore during an image reconstruction process which reconstructs an exact 3D image of the object.

2. The method of claim 1, wherein said step which develops said 2D correction data determines line integral derivative data for the 2D cone beam projection data in the masked 2D data sets that lie along only a portion of an identified sub-set of a plurality of line segments L(s,θ

) having their end points bound by the mask in each of the masked 2D data sets, and backprojecting the line integral derivative data into a 2D space along a corresponding backprojection direction (θ

), so as to develop a 2D correction image from each of said processed 2D data sets.

3. The method of claim 2, wherein the developing of said 2D correction data comprises calculating line integral derivative data for portions of said line segments L(s,θ

) identified as having two intersections with a top or bottom boundary of the mask, and backprojecting said line integral derivative data into a 2D space to develop said 2D correction data.

4. The method of claim 2, wherein said line segments L(s,θ

) which intersect a top or bottom boundary of the mask twice are identified as those line segments where;

5. The method of claim 2, wherein those portions of said line segments which intersect the mask twice and are therefore used to develop said 2D correction data comprise those portions of each of the line segments which are inside said masked data set and are positioned subsequent to the second intersection point with the mask.

6. The method of claim 2, wherein said step which develops said first data estimate determines line integral derivative data for the 2D cone beam projection data in the masked 2D data sets that lie along a plurality of line segments L(s,θ

) having their end points bound by the mask in each of the masked 2D data sets, and backprojecting the line integral data into a 2D space along a corresponding backprojection direction (θ

), so as to develop a 2D image from each of said processed 2D data sets.

7. The method of claim 2, wherein said step which develops said first data estimate processes the 2D cone beam projection data inside the mask boundaries by Feldcamp ramp filtering of said data along a plurality of parallel lines formed therein.

8. The method of claim 7 wherein said combining step includes subtraction of the 2D correction image from each of said corresponding 2D images, for creating a corrected 2D image.

9. The method of claim 2 wherein said combining step includes weighted 3D backprojection of said corrected 2D image into a common 3D space, thereby reconstructing in said 3D space a corrected 3D image of the object.

10. The method of claim 3 wherein said combining step includes weighted 3D backprojection of said corrected 2D image into a common 3D space, thereby reconstructing in said 3D space a corrected 3D image of the object.

11. The method of claim 9 wherein before said 3D backprojection step, said 2D corrected image is filtered.

12. Apparatus for three dimensional (3D) computerized tomographic (CT) imaging of an object, comprising:

a cone beam source for applying radiation energy to at least a portion of the object;

a 2D area detector for detecting radiation energy;

means for defining a source scanning trajectory as a scan path traversed by the source;

a manipulator for causing the cone beam source, fixed relative to an area detector with both source and detector movably positioned relative to the object, to scan about the object at a plurality of source positions in a direction along the scan path to cause said area detector to acquire a set of 2D cone beam projection data at each of said source positions;

a masking means for applying a mask to each set of the acquired 2D projection data to form masked data sets;

estimating means for processing line integrals in the 2D cone beam projection data along a plurality of line segments L(s,θ

) having their end points determined by the boundaries of each of the masked 2D data sets to develop a corresponding plurality of processed 2D data sets, each processed 2D data set corresponding to calculation of a first data estimate determined for a given set of the 2D cone beam projection data;

a correction processor means for developing 2D correction data for each of the first data estimates by identifying and processing a portion of each of said line segments which is formed by a second intersection with said data combination mask and therefore is not used to develop said first data estimates, and processing said identified portions of said line segments to develop said 2D correction data; and

combining means for combining each estimated 2D data set and the 2D correction data developed therefore during an image reconstruction process which reconstructs an exact 3D image of the object.

13. The apparatus of claim 12, wherein the estimating means comprises a ramp filter processor for ramp filtering the 2D cone beam projection data inside the mask boundaries of each masked 2D data along a plurality of parallel lines formed therein.