System and method for performing tomographic image acquisition and reconstruction

1. A method that produces images, comprising:

acquiring a k-space data set of an imaged object;

collecting a portion of the k-space data set according to a data collecting pattern, the data collecting pattern having a degree of incoherence incorporated into the data collecting pattern by a process comprising rotating the entire data collecting pattern along an axis containing a trajectory plane of the data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane; and

reconstructing an image from the collected portion of the k-space data set according to a convex optimization model,

wherein the reconstructing of the image according to the convex optimization model includes generating image data using an iterative process, the iterative process including updating a value of a norm weighting factor to prevent penalizing of discontinuities in the reconstructed image, and

wherein the acquiring, the collecting, and the reconstructing are performed by one or more processors.

2. The method of claim 1 , wherein the convex optimization model includes a weighting factor representative of expected noise properties within the k-space data set.

3. The method of claim 1 , wherein the convex optimization model includes a weighting factor representative of a priori attributes of the imaged object.

4. The method of claim 1 , wherein the data collecting pattern includes a spiral pattern.

5. The method of claim 1 , wherein the data collecting pattern includes a radial pattern.

6. The method of claim 1 , wherein the data collecting pattern includes a pattern comprising a plurality of parallel sampling lines.

7. The method of claim 1 , wherein the generating of image data uses an approximation of an l=0 norm of a discretization of total variation of image intensities.

8. The method of claim 7 , wherein an iteration of the iterative process includes updating a value of a homotopic parameter and updating a value of a quadratic relaxation parameter.

9. The method of claim 8 , wherein respective values of the homotopic parameter and the quadratic relaxation parameter are fixed in relation to each other according to a predetermined relationship.

10. The method of claim 9 , wherein each iteration of the iterative process includes:

increasing the value of the quadratic relaxation parameter according to a predetermined rate; and

decreasing the value of the homotopic parameter according to the value of the quadratic relaxation parameter and the predetermined relationship between the quadratic relaxation parameter and the homotopic parameter.

11. The method of claim 8 , wherein said iterative process is an outer iterative process, and wherein each iteration of the outer iterative process includes one or more iterations of an inner iterative process.

12. The method of claim 11 , wherein each iteration of the inner iterative process includes updating a value of a relaxation variable based at least in part on the value of the homotopic parameter and the value of the quadratic relaxation parameter.

13. The method of claim 12 , wherein each iteration of the inner iterative process includes updating image data based at least in part on the value of the relaxation variable.

14. The method of claim 1 , wherein the generating of image data uses one of an l=1 norm of a discretization of total variation of image intensities and an l=2 norm of a discretization of total variation of image intensities.

15. The method of claim 1 , wherein the norm weighting factor is based at least in part on a smoothed image data.

16. The method of claim 15 , wherein the updating of the value of the norm weighting factor includes generating the smoothed image data using a Gaussian kernel.

17. The method of claim 1 , wherein said iterative process is an outer iterative process, wherein each iteration of the outer iterative process includes one or more iterations of an inner iterative process.

18. The method of claim 17 , wherein each iteration of the inner iterative process includes updating a value of a relaxation variable based at least in part on the value of the homotopic parameter and the value of the quadratic relaxation parameter.

19. The method of claim 18 , wherein each iteration of the inner iterative process includes updating image data based at least in part on the value of the relaxation variable.

20. The method of claim 1 , wherein the reconstructing of the image includes generating image data representative of the imaged object.

21. The method of claim 20 , wherein the reconstructing of the image includes outputting the image data to at least one of a display, a printer, and a memory device.

22. The method of claim 1 , wherein the acquiring is performed by a magnetic resonance imaging system generating a first image of the imaged object, the first image comprising the k-space data set.

23. The method of claim 1 , wherein the acquiring, collecting, and reconstructing are performed substantially simultaneously with delivery of radiation to a patient from a radiation source during image-guided radiation therapy.

24. A method that produces images, comprising:

acquiring a k-space data set of an imaged object;

collecting a subset of the k-space data set according to a predetermined data collecting pattern having a degree of incoherence, thereby generating a sampled k-space data set, the data collecting pattern having the degree of incoherence incorporated into the predetermined data collecting pattern by a process comprising rotating the entire predetermined data collecting pattern along an axis containing a trajectory plane of the predetermined data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane;

generating a first set of image data using the sampled k-space data set; and

performing an iterative process using the first set of image data to generate a second set of image data,

wherein the iterative process includes modifying the first set of image data according to an optimization model that includes combining image data from the first set of image data with k-space data from the sampled k-space data set according to a plurality of weighting factors, the plurality of weighting factors including a norm weighting factor to prevent penalizing large discontinuities in the image data, and

wherein the acquiring, the collecting, the generating, and the performing are performed by one or more processors.

25. The method of claim 24 , further comprising generating the first set of image data based at least in part on an inverse Fourier transform of the portion of the k-space data set.

26. The method of claim 24 , wherein the plurality of weighting factors includes an importance weighting factor for attributes of the image data.

27. The method of claim 24 , wherein the plurality of weighting factors includes a weighting factor for applying a respective weights to different attributes of the image data.

28. A method that produces images, comprising:

receiving a k-space data set from a magnetic resonance imaging system;

collecting a subset of the k-space data set according to a predetermined data collecting pattern having a degree of incoherence incorporated into the predetermined data collecting pattern by a process comprising rotating the entire predetermined data collecting pattern along an axis containing a trajectory plane of the predetermined data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane, and the predetermined data collecting pattern including a spiral pattern;

generating a first set of image data using the sampled k-space data set; and

performing an iterative process using the first set of image data to generate a second set of image data,

wherein the iterative process includes modifying the first set of image data according to an optimization model that includes combining image data from the first set of image data with k-space data from the sampled k-space data set according to a plurality of weighting factors, the plurality of weighting factors including a norm weighting factor to prevent penalizing large discontinuities in the image data, and

wherein the receiving, the collecting, the generating, and the performing are performed by one or more processors.

29. The method of claim 28 , further comprising generating the first set of image data based at least in part on an inverse Fourier transform of the portion of the k-space data set.

30. The method of claim 28 , wherein the plurality of weighting factors includes an importance weighting factor for attributes of the image data.

31. The method of claim 28 , wherein the plurality of weighting factors includes a weighting factor for applying a respective weights to different attributes of the image data.

32. An imaging system that produces images, comprising:

a computer memory for receiving and storing a k-space data set of an imaged object; and

a computing unit for collecting a portion of the k-space data set according to a data collecting pattern having a degree of incoherence and reconstructing an image from the collected portion of the k-space data set according to a convex optimization model, the data collecting pattern having the degree of incoherence incorporated into the data collecting pattern by a process comprising rotating the entire data collecting pattern along an axis containing a trajectory plane of the data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane,

wherein the reconstructing of the image according to the convex optimization model includes generating image data using an iterative process, the iterative process including updating a value of a norm weighting factor to prevent penalizing of discontinuities in the reconstructed image.

33. The system of claim 32 , wherein the convex optimization model includes a weighting factor representative of expected noise properties within the k-space data set.

34. The system of claim 32 , wherein the convex optimization model includes a weighting factor representative of a priori attributes of the imaged object.

35. The system of claim 32 , wherein the generating of the image data by the computing unit uses an approximation of an l=0 norm of a discretization of total variation of image intensities.

36. The system of claim 35 , wherein an iteration of the iterative process includes updating a value of a homotopic parameter and updating a value of a quadratic relaxation parameter.

37. The system of claim 36 , wherein respective values of the homotopic parameter and the quadratic relaxation parameter are fixed in relation to each other according to a predetermined relationship.

38. The system of claim 32 , wherein the generating of the image data by the computing unit uses one of an l=1 norm of a discretization of total variation of image intensities and an l=2 norm of a discretization of total variation of image intensities.

39. The system of claim 32 , wherein the norm weighting factor is based at least in part on a smoothed image data.

40. The system of claim 32 , wherein the computing unit generates image data representative of the imaged object.

41. The system of claim 40 , wherein computing unit outputs the image data to at least one of a display, a printer, and a memory device.

42. The system of claim 32 , wherein the k-space data set is generated by a magnetic resonance imaging (MRI) system.

43. An imaging system that produces images, comprising:

a computer memory receiving and storing a k-space data set of an imaged object; and

a computing unit performing operations comprising:

collecting a subset of the k-space data set according to a predetermined data collecting pattern having a degree of incoherence, thereby generating a sampled k-space data set, the predetermined data collecting pattern having the degree of incoherence incorporated into the predetermined data collecting pattern by a process comprising rotating the entire predetermined data collecting pattern along an axis containing a trajectory plane of the predetermined data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane;

generating a first set of image data using the sampled k-space data set; and

performing an iterative process using the first set of image data to generate a second set of image data,

wherein the iterative process includes modifying the first set of image data according to an optimization model that includes combining image data from the first set of image data with k-space data from the sampled k-space data set according to a plurality of weighting factors, the plurality of weighting factors including a norm weighting factor to prevent penalizing large discontinuities in the image data.

44. The system of claim 43 , further comprising an interface receiving the k-space data set from an image capturing system.

45. The system of claim 43 , further comprising an image capturing system.

46. The system of claim 43 , wherein the predetermined data collecting pattern includes a spiral pattern.

47. The system of claim 46 , wherein the k-space data set is generated by a magnetic resonance imaging (MRI) system.

48. A non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:

acquiring a k-space data set of an imaged object;

collecting a portion of the k-space data set according to a data collecting pattern having a degree of incoherence incorporated into the data collecting pattern by a process comprising rotating the entire data collecting pattern along an axis containing a trajectory plane of the data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane; and

reconstructing an image from the collected portion of the k-space data set according to a convex optimization model,

wherein the reconstructing of the image according to the convex optimization model includes generating image data using an iterative process, the iterative process including updating a value of a norm weighting factor to prevent penalizing of discontinuities in the reconstructed image.

49. A non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:

acquiring a k-space data set of an imaged object;

collecting a subset of the k-space data set according to a predetermined data collecting pattern having a degree of incoherence, thereby generating a sampled k-space data set, the predetermined data collecting pattern having the degree of incoherence incorporated into the predetermined data collecting pattern by a process comprising rotating the entire predetermined data collecting pattern along an axis containing a trajectory plane of the predetermined data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane;

generating a first set of image data using the sampled k-space data set; and

performing an iterative process using the first set of image data to generate a second set of image data,

wherein the iterative process includes modifying the first set of image data according to an optimization model that includes combining image data from the first set of image data with k-space data from the sampled k-space data set according to a plurality of weighting factors, the plurality of weighting factors including a norm weighting factor to prevent penalizing large discontinuities in the image data.

50. A non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:

receiving a k-space data set from a magnetic resonance imaging system;

collecting a subset of the k-space data set according to a predetermined data collecting pattern having a degree of incoherence, the predetermined data collecting pattern including a spiral pattern, the predetermined data collecting pattern having the degree of incoherence incorporated into the predetermined data collecting pattern by a process comprising rotating the entire predetermined data collecting pattern along an axis containing a trajectory plane of the predetermined data collecting pattern, the rotating introducing a pseudo-random shift into the trajectory plane;

generating a first set of image data using the sampled k-space data set; and

performing an iterative process using the first set of image data to generate a second set of image data,

wherein the iterative process includes modifying the first set of image data according to an optimization model that includes combining image data from the first set of image data with k-space data from the sampled k-space data set according to a plurality of weighting factors, the plurality of weighting factors including a norm weighting factor to prevent penalizing large discontinuities in the image data.