NOISE SUPPRESSION FOR LOW X-RAY DOSE CONE-BEAM IMAGE RECONSTRUCTION

• US 20130051516A1
• Filed: 08/31/2011
• Published: 02/28/2013
• Est. Priority Date: 08/31/2011
• Status: Abandoned Application

• Current Assignee: CareStream Health Incorporated
• Original Assignee: CareStream Health Incorporated
• Inventors: Yang, Dong, Packard, Nathan J.
• Application Number:
  US13/222,432
• Publication Number:
  US 20130051516A1
• Time in Patent Office: Days
• US Class Current: 378/4
• CPC Class Codes: A61B 6/03 Computed tomography [CT] echo-tomography A61B8/14
• CPC Class Codes: A61B 6/4085 Cone-beams
• CPC Class Codes: A61B 6/5223 generating planar views from image data, e.g. extracting a coronal view from a 3D image
• CPC Class Codes: A61B 6/5258 involving detection or reduction of artifacts or noise
• CPC Class Codes: A61B 6/5282 due to scatter
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating

Abstract

Embodiments of methods and/or apparatus for 3-D volume image reconstruction of a subject, executed at least in part on a computer for use with a digital radiographic apparatus can obtain image data for 2-D projection images over a range of scan angles. For each of the plurality of projection images, an enhanced projection image can be generated. In embodiments of imaging apparatus, CBCT systems, and methods for operating the same can, through a de-noising application based on a different corresponding object, maintain image reconstruction characteristics (e.g., for a prescribed CBCT examination) while reducing exposure dose, reducing noise or increase a SNR while an exposure setting is unchanged.

Claims

1. A method for digital radiographic 3D volume image reconstruction of a subject, executed at least in part on a computer, comprising:

obtaining image data at a first examination setting for a plurality of first 2D projection images over a range of scan angles;

generating, for each of the plurality of first 2D projection images, a corresponding second 2D projection image by;

concurrently passing each of the plurality of 2D projection images through a plurality of de-noising filters;

providing a low noise image representation of a different corresponding object;

determining an image data transformation for the first examination setting according to the image representation using outputs of the plurality of de-noising filters and the low noise image representation of a different corresponding object;

applying the image data transformation individually to the plurality of first 2D projection images to generate the corresponding plurality of second 2D projection images; and

storing the plurality of second 2D projection images in a computer-accessible memory.

2. The method of claim 1 wherein the transformed plurality of second 2D projection images comprises a lower noise 2D projection images, higher SNR 2D projection images or higher CNR 2D projection images than the plurality of first 2D projection images.

3. The method of claim 1 wherein the image data transformation is provided by a computational unit, a neural network interpolator, a plurality of neural network interpolators, a machine-based regression learning device or a SVM machine regression learning device.

4. The method of claim 3 wherein the machine-based regression learning unit is based on an examination type or x-ray radiation source exposure setting.

5. The method of claim 3 wherein the image data transformation is angularly independent.

6. The method of claim 1 wherein the reduced noised projection data for the current 2D projection image comprises a SNR of an exposure dose 100%, 200% or greater than 400% higher.

7. The method of claim 1 wherein applying the image data transformation individually to the plurality of first 2D projection images comprises weighting a plurality of outputs of the plurality of de-noising filters,

wherein the machine-based regression learning unit is configured to operate on a pixel-by-pixel basis.

8. The method of claim 1 further comprising processing the transformed plurality of second 2D projection images to reconstruct the 3D volume image reconstruction of the subject.

9. The method of claim 1 wherein determining an image data transformation for the first examination setting comprises training a machine-based regression learning unit by:

determining a first image of a corresponding object;

passing scanned projection data of the corresponding object for a prescribed examination setting through the plurality of de-noising filters;

inputting the de-noised data from the plurality of de-noising filters into a machine-based regression learning unit to obtain a second estimated image of the corresponding object;

determining a difference between the second estimated image of the corresponding object and the first image; and

iteratively processing the de-noised data from the plurality of de-noising filters to determine an image data transformation to reduce the difference between the first image and the second estimated image.

10. The method of claim 9 wherein the training is completed for the prescribed examination setting when the difference for a projection image is less than a prescribed threshold, further comprising training for a plurality of prescribed examination settings.

11. The method of claim 9 wherein the training comprises training using a plurality of different corresponding objects.

12. The method of claim 1 wherein obtaining image data for the plurality of first 2D projection images comprises obtaining image data from a cone-beam computerized tomography apparatus or a tomography imaging apparatus.

13. The method of claim 1 further comprising:

processing the plurality of second 2D projection images to reconstruct a 3D volume image reconstruction of the subject;

displaying the 3D volume image reconstruction; and

storing the 3D volume image reconstruction in the computer-accessible memory, wherein the 3D volume image reconstruction is a orthopedic medical image, a dental medical image or a pediatric medical image.

14. The method of claim 13 wherein processing the processing the plurality of second 2D projection images comprises:

performing one or more of geometric correction, scatter correction, beam-hardening correction, and gain and offset correction on the plurality of 2D projection images;

performing a logarithmic operation on the plurality of 2D reduced noise projection images to obtain line integral data; and

performing a row-wise ramp linear filtering to the line integral data.

15. The method of claim 1 wherein the subject is a limb, an extremity, a weight bearing extremity or a portion of a dental arch.

16. The method of claim 1 wherein the image transformation is based on an examination type or x-ray radiation source exposure setting.

17. A method for digital radiographic 3D volume image reconstruction of a subject, executed at least in part on a computer, comprising:

obtaining cone-beam computed tomography image data at a prescribed exposure setting for a plurality of 2D projection images over a range of scan angles;

generating, for each of the plurality of 2D projection images, a lower noise projection image by;

(i) providing an image data transformation for the prescribed exposure setting according to image data from a different corresponding subject based on a set of noise-reducing filters;

(ii) applying the image data transformation individually to the plurality of 2D projection images obtained by;

(a) concurrently passing each of the plurality of 2D projection images through the set of noise-reducing filters; and

(b) applying the image data transformation individually to the plurality of first 2D projection images pixel-by-pixel to use the outputs of the set of noise-reducing filters to generate the corresponding plurality of lower noise projection images; and

storing the lower noise projection images in a computer-accessible memory.

18. A digital radiography CBCT imaging system for digital radiographic 3D volume image reconstruction of a subject, comprising:

a DR detector to obtain a plurality of CBCT 2D projection images over a range of scan angles at a first exposure setting;

a computational unit to generate, for each of the plurality of 2D projection images, an reduced-noise 2D projection image, the set of noise-reducing filters to select (i) an image data transformation for a prescribed exposure setting, a corresponding different subject, and a plurality of imaging filters, and (ii) apply the image data transformation individually to the plurality of 2D projection images obtained at the first exposure setting to generate the plurality of reduced-noise 2D projection images; and

a processor to store the reduced-noise plurality of 2D projection images in a computer-readable memory.

19. The digital radiography CBCT imaging system of claim 18, where the computational unit is a machine based regression learning unit.