WO2009004571A1

WO2009004571A1 - Method and apparatus for image reconstruction

Info

Publication number: WO2009004571A1
Application number: PCT/IB2008/052632
Authority: WO
Inventors: Peter Forthmann; Roland Proksa
Original assignee: Koninklijke Philips Electronics N.V.; Philips Intellectual Property & Standards Gmbh
Priority date: 2007-07-05
Filing date: 2008-07-01
Publication date: 2009-01-08

Abstract

To reduce artifacts comprised in a CT image, the present invention provides a method comprising the steps of: Reconstructing a first image by processing one first set of CT data using a reconstruction algorithm, the first image comprising an artifact; Generating a second image by processing the first image, the second image comprising a reduced artifact; and Generating a third image by processing the first image and the second image, the third image comprising a further-reduced artifact. In an exemplary embodiment of the present invention, the step of generating the third image comprises the steps of: generating an error image representing the artifacts, based on the second image, and generating the third image by subtracting the error image from the first image. By using the provided method, the artifacts can be greatly reduced and hence a better-quality CT image can be obtained.

Description

METHOD AND APPARATUS FOR IMAGE RECONSTRUCTION

Field of the Invention

The present invention relates generally to image reconstruction, and, more particularly, to artifact reduction in medical images.

Background of the Invention

Computer Tomography (CT) X-ray data is computationally compiled from absorption data of X-rays that pass through an object and is reconstructed into an image. It is known that an image with low attenuation regions, such as soft biologic tissue, can contain artifacts generated by high attenuation objects. Artifacts degrade the quality of a CT image, obstruct identification and/or diagnosis and should be removed or reduced to obtain an accurate image. Artifact reduction is sometimes accomplished through reprojection and reconstruction of the image, using a number of mathematical systems.

US patent No. 6,266,388 to Jiang Hsieh, describes a two-pass cone beam image reconstruction method that can reduce artifacts in a CT image by generating an error image using reprojection and reconstruction methods based on a portion of an initial reconstruction image, which is generated based on a collected cone beam image data set, and subtracting the error image from the initial reconstruction image.

Summary of the Invention

An aspect of some embodiments of the present invention relates to reducing artifact in a medical image reconstructed from a set of input data, which is generally generated by a medical scan system, like a CT X-ray system, a MRI or any 3D X-ray system. The basic idea of the present invention is: after a first pass, wherein a first image is reconstructed based on a set of input data, and a second pass, wherein the artifact comprised in the first image is reduced, a third pass is added to further process the output image of the second pass, referred to as the second image, to further determine an error image representing the artifact comprised in the first image, and subtract the error image from the first image, to obtain a third image, in which the artifact is further reduced.

Any algorithms known in the art that are appropriate for reconstructing images from a set of CT data, segmenting a CT image into a plurality of images, reprojecting images to form at least a set of data, can be used in the present invention.

There is thus provided, in accordance with an exemplary embodiment of the invention, a method for artifact reduction in an image, comprising the steps of: reconstructing a first image by processing a first set of input data using a reconstruction algorithm, the first image comprising an artifact; generating a second image by processing the first image, the second image comprising a reduced artifact; and generating a third image by processing the first image and the second image, the third image comprising a further-reduced artifact.

In an embodiment of the present invention, the step of generating the third image comprises the steps of: generating an error image representing the artifacts comprised in the first image, based on the second image, and generating the third image by subtracting the error image from the first image. By processing the second image which comprises a reduced artifact, the error image can further accurately represent the artifact in the first image, thus the third image comprises less artifact after the more accurate error image is subtracted from the first image.

In an embodiment of the present invention, the step of generating the error image comprises: segmenting the second image to provide an image with high attenuation objects separate from low attenuation objects; reprojecting the segmented image to form at least a set of data; reconstructing an image from the set of data; and determining the error image based on the reconstructed image.

In an embodiment of the present invention, to obtain an image in which the artifact is further reduced, the method can further comprise at least one set of iterative steps of utilizing an output image of a previous step comprising a reduced artifact and the first image to generate a new image, in which the artifact is further reduced than the artifact of the output image of the previous step. Thus, a better-quality image with fewer artifacts can be obtained after an image reconstruction process having more than three passes.

There is thus provided, in accordance with an exemplary embodiment of the invention, an apparatus for reconstructing an image using data collected in a cone beam scan, the apparatus comprising a processor configured for: generating a first image by processing the collected data using a reconstruction algorithm, the first image comprising an artifact; generating a second image by processing the first image, the second image comprising a reduced artifact; and generating a third image by processing the first image and the second image, the third image comprising a further-reduced artifact.

The processor can be configured in an image reconstructor, a computer, or a device capable of processing a medical data set, of a medical system, e.g., a CT system and a MRI system.

There is thus provided, in accordance with an exemplary embodiment of the invention, a computer program configured to perform the methods described above.

Other objects and effects of the present invention will become apparent from the following description and the appended claims when taken in conjunction with the accompanying drawings, and a more comprehensive understanding of the present invention will be obtained. Brief Description of the Drawings

Fig. 1 is a schematic drawing of a cone beam CT system;

Fig. 2 is a schematic drawing of a cone beam CT system comprising a processor configured to reduce artifact in accordance with an exemplary embodiment of the invention;

Fig. 3 is a block diagram showing a method used to reduce artifact in a cone beam CT image in accordance with an exemplary embodiment of the invention; and

Fig. 4 is a detailed block diagram showing a method used to reduce artifact in a cone beam

CT image in accordance with an exemplary embodiment of the invention.

Fig. 5 illustrates a block of a computer program implementing the artifact reduction methods in accordance with an exemplary embodiment of the invention.

Throughout the above drawings, like reference numerals will be understood to refer to like, similar or corresponding features or functions.

Detailed Description of Exemplary Embodiments

Referring to Fig. 1, a computed tomography (CT) imaging system 100 is schematically shown as including a gantry 110, a control mechanism 120, a data acquisition system (DAS) 130, an image reconstructor 140 and a computer 150. The gantry 110 can be a "third generation" CT scanner having an X-ray source and detector array for scanning an object. The control mechanism 120 can provide power and time signals to control the gantry's X-ray source and the gantry's rotational speed and position. The DAS 130 samples data collected from the gantry 110 and converts the data to digital signals for subsequent processing. The image reconstructor 140 receives sampled and digitized x-ray data from DAS 130 and performs image reconstruction. The reconstructed image is applied as an input to the computer 150 for storing, displaying and other subsequent processing operations. The computer 150 also can send commands to the control mechanism 120 to control the gantry 110. In some CT systems, the control mechanism 120 and DAS 130 are integrated together. The algorithms described below may be performed by a processor 210 in image reconstructor 140, as shown in Fig. 2. Alternatively, the processor 210 also can be located in computer 150, or other devices coupled to the CT system.

Fig. 3 is a block diagram illustrating a CT X-ray imaging reconstruction method 300 according to an exemplary embodiment of the present invention, used to process CT data collected from a CT scanner and reduce artifacts in the image.

In step S310, the collected CT data are used for reconstruction purposes in order to form a first image, i.e., a first reconstruction image. Normally, reconstruction S310 creates many images of the scanned object from the cone beam data, each image recording an X-ray slice taken through a single plane passing through an object. The used reconstruction algorithm can be an exact reconstruction algorithm or an inexact reconstruction algorithm. Generally, an exact reconstruction algorithm cannot reconstruct an artifact- free image from an incomplete trajectory like a circle. For Helical acquisition, i.e., a complete trajectory, cone beam artifacts can be produced by an inexact reconstruction algorithm. Step S310 can be referred to as 1^st pass.

In step S320, firstly, the first image is processed to generate an image representing artifacts in the first image. Then the image representing artifacts is subtracted from the first image to form a second image, in which the artifact is reduced and which is thus referred to as artifact- reduced image. Step S320 can be referred to as 2^nd pass.

As regards step S330, firstly, in step S332, the output image of the 2^nd pass, i.e., the second image, is processed to generate an error image, which better represents the artifacts in the first image. Then, in step S334, the error image is subtracted from the first image to form a third image, in which the artifacts are further reduced. In S330, the error image can represent the artifact more accurately than the image representing the artifacts generated in S320, so the artifacts in the third image are further reduced than in the second image. Step S330 can be referred to as 3^rd pass. If the quality of the third image is good enough, the artifact-reduction process can be stopped, and the third image can be fed into the computer 150 of Fig. 2. If not, one or more steps can follow to pursue higher quality, i.e., reducing artifact further. In each following step, the input image is the output image of a directly preceding step, and is processed to generate an error image representing the artifacts in the first image. A higher quality image can be generated by subtracting the generated error image from the first image. The number of iterative steps depends on the requirements.

In S320, S330, and subsequent steps, if applicable, the algorithms used in the segmentation, reprojection, reconstruction, filtering and determination processes, can be the respective algorithms known in the art, that are appropriate for each individual step. The algorithms used in each segmentation process can be the same, like the use of a certain threshold on the value of each pixel, gradient identification, continuity reconstruction, or other methods known in the art that yield identified high attenuation regions that are separate from the image. Similarly, the algorithms used in the reprojection process, reconstruction process, filtering process, and determination process of each step, can be the same. The skilled person in the art should know that the segmentation algorithms, reprojection algorithms, reconstruction algorithms, filtering algorithms and determination algorithms used in different passes can be similar, as long as they can reproduce the artifacts as accurately as possible and do not introduce artifacts.

Fig. 4 is a detailed block diagram showing a method used to reduce artifact in a cone beam CT image in accordance with an exemplary embodiment of the invention. Step S4100 comprises two steps: step S4110 for acquiring CT data and step S4120 for reconstructing a first image by processing the acquired CT data using a reconstruction algorithm. The reconstruction algorithm can be an inexact reconstruction algorithm or an exact reconstruction algorithm. Step S4100 performs the function of 1^st pass, reconstructing a first image. Step S4200 performs the function of 2^nd pass, generating a second image, i.e., an artifact- reduced image. In step S4210, the first image, i.e., reconstructed data generated by S4120, is segmented. Generally, segmentation refers to a process where the volume is separated into high attenuation and low attenuation regions. An exemplary method of accomplishing this separation can be one that uses a threshold on the value of each pixel, a gradient identification method, a continuity reconstruction method, or any other appropriate method. The segmented data set has separate areas identified as the high attenuation objects, and these high attenuation objects are separated from the first image. The resultant high attenuation object image is filtered in step S4220 to remove any high frequency degradation that may have occurred during processing. For example, if a simple threshold segmentation algorithm is used in a segmentation process, a filtering process can smooth out the edges in the segmented image. Step S4220 is optional and can be skipped, especially when the used segmentation algorithm doesn't introduce sharp edges in the segmented image or comprises a smoothing/filtering operation. In step S4230, the high attenuation object is reprojected. In step S4240, the reprojected high attenuation data is reconstructed. Normally, reconstruction step S4240 uses the same algorithms utilized in step S4120 to generate an image of the high attenuation objects together with artifacts that these high attenuation objects created in the low attenuation objects through a reconstruction algorithm. Alternatively, a consistent CT data set can replace an inconsistent CT data set.

In step S4260, the artifact portion of the reconstructed image is determined. In step S4270, artifacts so determined are subtracted from the data set, generated by segmentation step S4210 of the consistent data set containing segmented high attenuation objects and low attenuation objects. It can also be achieved by subtracting the output image of S4260 from the first image, i.e., the output image of S4120, to obtain the second image. The second image contains reduced artifacts.

Step S4300 performs the function of 3^rd pass, generating a third image, i.e., a further artifact- reduced image. S4300 comprises segmentation step S4310, optional filtering step S4320, reprojection step S4330, reconstruction step S4340, determination step S4360 and artifact subtraction step S4370. These steps perform the same or similar functions as the corresponding steps comprised in S4200, the major differences being that the input of S4310 is the second image, i.e., the artifact-reduced image, and that the determined image of S4360 is subtracted from the first image, i.e., the initial reconstruction image reconstructed based on the collected CT data, instead of the second image or its corresponding data set. After the step S4300, a better-quality image, i.e., the artifact is further reduced, is generated. It can be fed into a computer or other image processing and displaying devices. Alternatively, if the output image of S4300 is not good enough, which means the artifact level still needs to be further reduced, other similar steps can be performed. Each follow-up step uses the output image of its previous step as input of its segmentation step, determines a corresponding artifact image, and subtracts the determined artifact image from the initial reconstruction image.

As shown in Fig. 5, another embodiment of the present invention further provides a computer program configured to perform the abovementioned artifact-reduction methods. The computer program comprises three reconstruction modules. A first reconstruction module, configured to generate a first image by processing a set of collected data using a reconstruction algorithm, the first image comprising an artifact. A second reconstruction module, configured to generate a second image by processing the first image, the second image comprising a reduced artifact. And a third reconstruction module, configured to generate a third image by processing the first image and the second image, the third image comprising a further-reduced artifact. The second reconstruction module is further configured to: segment the first image to provide at least a segmented image with high attenuation objects separate from low attenuation objects; reproject the segmented image to form at least a second set of data; reconstruct an image from the second set of data using a reconstruction algorithm; determine an image representing the artifact based on the reconstructed image; and subtract the determined image from the first image to obtain the second image. The third reconstruction module is further configured to: segment the second image to provide at least a segmented image with high attenuation objects separate from low attenuation objects; reproject the segmented image to form at least a third set of data; reconstruct an image from the third set of data using a reconstruction algorithm; determine an image representing the artifact based on the reconstructed image; and subtract the determined image from the first image to obtain the third image.

Optionally, all the reconstruction algorithms used in these three reconstruction modules can be the same.

It should be known to the person skilled in the art that the method described above also can be used in other medical imaging systems, like magnetic resonance imaging, and any 3D X-ray imaging system. Thus, these MRI and 3D X-ray systems can have corresponding apparatuses to perform the artifact reduction methods.

The embodiments described above are only illustrative, and not intended to limit the technical approach of the present invention. Those skilled in the art will understand that the technique approaches of the present invention can be modified or equally displaced without departing from the spirit and scope of the technique approaches of the present invention, which will also fall within the protective scope of the claims of the present invention.

Claims

Claims:

1. A method for artifact reduction in an image comprising the steps of: a) Reconstructing a first image by processing a first set of input data using a reconstruction algorithm, the first image comprising an artifact; b) Generating a second image by processing the first image, the second image comprising a reduced artifact; and c) Generating a third image by processing the first image and the second image, the third image comprising a further-reduced artifact.

2. A method as claimed in claim 1, wherein the step c) further comprises the steps of:

I) Generating an error image representing the artifact in the first image, based on the second image; and

II) Generating the third image by subtracting the error image from the first image.

3. A method as claimed in claim 2, wherein said step I) further comprises the steps of: i) Segmenting the second image to provide at least a segmented image with high attenuation objects separate from low attenuation objects; ii) Reprojecting the segmented image to form at least a second set of data; iii) Reconstructing an image from the second set of data using a reconstruction algorithm; and iv) Determining the error image based on the reconstructed image.

4. A method as claimed in claim 1, wherein said step b) further comprises the steps of:

I) Segmenting the first image to provide at least a segmented image with high attenuation objects separate from low attenuation objects;

II) Reprojecting the segmented image to form at least a third set of data;

III) Reconstructing an image from the third set of data using a reconstruction algorithm; IV) Determining an image representing the artifact in the first image, based on the reconstructed image; and

V) Subtracting the determined image from the first image to obtain the second image.

5. A method as claimed in claim 1, further comprising the steps of: d) Generating a fourth image by processing the first image and the third image, the fourth image comprising an artifact that is further reduced than the artifact of the third image.

6. A method as claimed in claim 1, wherein the first set of input data is at least part of data collected from any one of a cone beam X-ray device and a magnetic resonance device.

7. A method as claimed in claim 1, wherein the first set of input data is a set of data collected from a 3D X-ray scan device.

8. A method as claimed in any one of claims 1, 3 and 4, wherein the reconstruction algorithm is compatible with cone beam X-ray data.

9. Apparatus for reducing artifact in an image, the apparatus comprising a processor configured to:

Generate a first image by processing a set of collected data using a reconstruction algorithm, the first image comprising an artifact;

Generate a second image by processing the first image, the second image comprising a reduced artifact; and

Generate a third image by processing the first image and the second image, the third image comprising a further-reduced artifact.

10. Apparatus as claimed in claim 9, wherein, to generate the second image, the processor is further configured to: I) Segment the first image to provide at least a segmented image with high attenuation objects separate from low attenuation objects;

II) Reproject the segmented image to form at least a second set of data;

III) Reconstruct an image from the second set of data, using a reconstruction algorithm;

IV) Determine an image representing the artifact, based on the reconstructed image; and

V) Subtract the determined image from the first image to obtain the second image.

11. Apparatus as claimed in claim 9, wherein, to generate the third image, the processor is further configured to: i) Segment the second image to provide at least a segmented image with high attenuation objects separate from low attenuation objects; ii) Reproject the segmented image to form at least a third set of data; iii) Reconstruct an image from the third set of data, using a reconstruction algorithm; iv) Determine the error image based on the reconstructed image; and v) Subtract the error image from the first image to generate the third image.

12. Apparatus as claimed in claim 9, wherein the set of collected data is collected from any one of a cone beam X-ray device and a magnetic resonance device.

13. A computer program for reducing artifact in an image, the computer program comprising: a) A first reconstruction module, configured to generate a first image by processing a set of collected data using a reconstruction algorithm, the first image comprising an artifact; b) A second reconstruction module, configured to generate a second image by processing the first image, the second image comprising a reduced artifact; and c) A third reconstruction module, configured to generate a third image by processing the first image and the second image, the third image comprising a further-reduced artifact.

14. A computer program as claimed in claim 13, wherein the second reconstruction module is further configured to:

I) Segment the first image to provide at least a segmented image with high attenuation objects separate from low attenuation objects;

II) Reproject the segmented image to form at least a second set of data;

15. A computer program as claimed in claim 14, wherein the third reconstruction module is further configured to:

I) Segment the second image to provide at least a segmented image with high attenuation objects separate from low attenuation objects;

II) Reproject the segmented image to form at least a third set of data;

III) Reconstruct an image from the third set of data, using a reconstruction algorithm;

V) Subtract the determined image from the first image to obtain the third image.

16. A computer program as claimed in claim 15, wherein the reconstruction algorithms used in the first reconstruction module, the second reconstruction module and the third reconstruction module are the same reconstruction algorithms.