Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
/content/aapm/journal/medphys/41/12/10.1118/1.4901552
1.
1.W. W. Moses and S. E. Derenzo, “Empirical observation of resolution degradation in positron emission tomographs utilizing block detectors,” J. Nucl. Med. 34, 101P (1993).
2.
2.J. R. Stickel and S. R. Cherry, “High-resolution PET detector design: Modelling components of intrinsic spatial resolution,” Phys. Med. Biol. 50(2), 179195 (2005).
http://dx.doi.org/10.1088/0031-9155/50/2/001
3.
3.S. Cherry et al., “MicroPET: A high resolution PET scanner for imaging small animals,” IEEE Trans. Nucl. Sci. 44(3, Part 2), 11611166 (1997).
http://dx.doi.org/10.1109/23.596981
4.
4.Y. Yang, S. St. James, Y. Wu, H. Du, J. Qi, R. Farrell, P. A. Dokhale, K. S. Shah, K. Vaigneur, and S. R. Cherry, “Tapered LSO arrays for small animal PET,” Phys. Med. Biol. 56(1), 139153 (2011).
http://dx.doi.org/10.1088/0031-9155/56/1/009
5.
5.J. A. Kennedy, O. Israel, A. Frenkel, R. Bar-Shalom, and H. Azhari, “Super-resolution in PET imaging,” IEEE Trans. Med. Imaging 25(2), 137147 (2006).
http://dx.doi.org/10.1109/TMI.2005.861705
6.
6.J. Y. Suk, C. J. Thompson, A. Labuda, and A. L. Goertzen, “Improvement of the spatial resolution of the MicroPET R4 scanner by wobbling the bed,” Med. Phys. 35(4), 12231231 (2008).
http://dx.doi.org/10.1118/1.2868760
7.
7.C. J. Thompson, S. St. James, and N. Tomic, “Under-sampling in PET scanners as a source of image blurring,” Nucl. Instrum. Methods Phys. Res., Sect. A 545(1–2), 436445 (2005).
http://dx.doi.org/10.1016/j.nima.2005.01.329
8.
8.A. Dagher and C. J. Thompson, “Real-time data rebinning in PET to obtain uniformly sampled projections,” IEEE Trans. Nucl. Sci. 32(1), 811817 (1985).
http://dx.doi.org/10.1109/TNS.1985.4336946
9.
9.M. N. Wernick and C. T. Chen, “Superresolved tomography by convex projections and detector motion,” J. Opt. Soc. Am. A 9(9), 15471553 (1992).
http://dx.doi.org/10.1364/JOSAA.9.001547
10.
10.Z. H. Cho, K. S. Hong, J. B. Ra, and S. Y. Lee, “A new sampling scheme for the ring positron camera: Dichotomic ring sampling,” IEEE Trans. Nucl. Sci. 28(1), 9498 (1981).
http://dx.doi.org/10.1109/TNS.1981.4331146
11.
11.G. Chang, T. Pan, J. W. Clark, Jr., and O. R. Mawlawi, “Optimization of super-resolution processing using incomplete image sets in PET imaging,” Med. Phys. 35(12), 57485757 (2008).
http://dx.doi.org/10.1118/1.3021117
12.
12.M. Irani and S. Peleg, “Motion analysis for image enhancement: Resolution, occlusion, and transparency,” J. Vis. Commun. Image Represent 4(4), 324335 (1993).
http://dx.doi.org/10.1006/jvci.1993.1030
13.
13.C. M. Kao and C. T. Chen, “A direct sinogram-restoration method for fast image reconstruction in compact DOI-PET systems,” IEEE Trans. Nucl. Sci. 49(1), 208214 (2002).
http://dx.doi.org/10.1109/TNS.2002.998753
14.
14.K. Y. Jeong, K. Choi, W. H. Nam, and J. B. Ra, “Sinogram-based super-resolution in PET,” Phys. Med. Biol. 56(15), 48814894 (2011).
http://dx.doi.org/10.1088/0031-9155/56/15/015
15.
15.J. Verhaeghe and A. J. Reader, “A PET supersets data framework for exploitation of known motion in image reconstruction,” Med. Phys. 37(9), 47094721 (2010).
http://dx.doi.org/10.1118/1.3466832
16.
16.A. J. Reader, P. J. Julyan, H. Williams, D. L. Hastings, and J. Zweit, “EM algorithm system modeling by image-space techniques for PET reconstruction,” IEEE Trans. Nucl. Sci. 50(5), 13921397 (2003).
http://dx.doi.org/10.1109/TNS.2003.817327
17.
17.A. Rahmim, J. Qi, and V. Sossi, “Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls,” Med. Phys. 40(6), 064301 (15pp.) (2013).
http://dx.doi.org/10.1118/1.4800806
18.
18.A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. Ser. B 39(1), 138 (1977).
19.
19.H. H. Barrett, D. W. Wilson, and B. M. W. Tsui, “Noise properties of the EM algorithm: I. Theory,” Phys. Med. Biol. 39(5), 833846 (1994).
http://dx.doi.org/10.1088/0031-9155/39/5/004
20.
20.H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, New York, NY, 2004).
21.
21.L. Landweber, “An iteration formula for fredholm integral equations of the first kind,” Am. J. Math. 73(3), 615624 (1951).
http://dx.doi.org/10.2307/2372313
22.
22.M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging (Institute of Physics, Bristol, UK, 1998).
23.
23.P. C. Hansen, J. G. Nagy, and P. O’Leary, Deblurring Images: Matrices, Spectra, and Filtering (SIAM, Philadelphia, PA, 2006).
24.
24.M. A. King, R. B. Schwinger, P. W. Doherty, and B. C. Penney, “Two-dimensional filtering of SPECT images using the Metz and Wiener filters,” J. Nucl. Med. 25(11), 12341240 (1984).
25.
25.G. L. Zeng, “A filtered backprojection algorithm with characteristics of the iterative Landweber algorithm,” Med. Phys. 39(2), 603607 (2012).
http://dx.doi.org/10.1118/1.3673956
26.
26.A. M. Alessio, P. E. Kinahan, and T. K. Lewellen, “Modeling and incorporation of system response functions in 3-D whole body PET,” IEEE Trans. Med. Imaging 25(7), 828837 (2006).
http://dx.doi.org/10.1109/TMI.2006.873222
27.
27.M. E. Daube-Witherspoon et al., “PET performance measurements using the NEMA NU 2-2001 standard,” J. Nucl. Med. 43(10), 13981409 (2002).
28.
28.S. D. Metzler, S. Matej, and J. S. Karp, “Resolution enhancement in PET reconstruction using collimation,” IEEE Trans. Nucl. Sci. 60(1), 6575 (2013).
http://dx.doi.org/10.1109/TNS.2012.2214444
29.
29.Y. Li, S. Matej, J. S. Karp, and S. D. Metzler, “LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation,” Phys. Med. Biol. 59(24 ) (2014) (to be published).
http://dx.doi.org/10.1109/NSSMIC.2012.6551775
30.
30.D. L. Snyder, M. I. Miller, L. J. Thomas, Jr., and D. G. Politte, “Noise and edge artifacts in maximum-likelihood reconstructions for emission tomography,” IEEE Trans. Med. Imaging 6(3), 228238 (1987).
http://dx.doi.org/10.1109/TMI.1987.4307831
31.
31.M. Defrise, P. E. Kinahan, D. W. Townsend, C. Michel, M. Sibomana, and D. F. Newport, “Exact and approximate rebinning algorithms for 3-D PET data,” IEEE Trans. Med. Imaging 16(2), 145158 (1997).
http://dx.doi.org/10.1109/42.563660
32.
32.H. H. Barrett, T. White, and L. C. Parra, “List-mode likelihood,” J. Opt. Soc. Am. A 14(11), 29142923 (1997).
http://dx.doi.org/10.1364/JOSAA.14.002914
33.
33.L. Parra and H. H. Barrett, “List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET,” IEEE Trans. Med. Imaging 17(2), 228235 (1998).
http://dx.doi.org/10.1109/42.700734
34.
34.H. M. Hudson and R. Larkin, “Accelerated image reconstruction using ordered subsets of projection data,” IEEE Trans. Med. Imaging 13(4), 601609 (1994).
http://dx.doi.org/10.1109/42.363108
35.
35.C. L. Byrne, “Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods,” IEEE Trans. Image Process. 7(1), 100109 (1998).
http://dx.doi.org/10.1109/83.650854
36.
36.S. D. Metzler, Y. Li, J. S. Karp, and S. Matej, “Super-resolution pet using stepping of a deliberately misaligned bed,” in Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Lake Tahoe, CA, 2013), pp. 388391.
37.
37.R. L. Baer and D. J. Thivent, “Super-resolution based on optical image stabilization,” U.S. patent 2014/0125825 (8 May 2014).
38.
38.R. E. Crochiere and L. R. Rabiner, “Interpolation and decimation of digital signals—A tutorial review,” in Proc. IEEE (IEEE, New York, NY, 1981), Vol. 69, pp. 300331.
http://dx.doi.org/10.1109/PROC.1981.11969
39.
39.R. A. Horn and C. R. Johnson, Topics in Matrix Analysis (Cambridge University, Cambridge, UK, 1994).
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/12/10.1118/1.4901552
Loading
/content/aapm/journal/medphys/41/12/10.1118/1.4901552
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/41/12/10.1118/1.4901552
2014-12-01
2016-09-27

Abstract

Spatial resolution in positron emission tomography (PET) is still a limiting factor in many imaging applications. To improve the spatial resolution for an existing scanner with fixed crystal sizes, mechanical movements such as scanner wobbling and object shifting have been considered for PET systems. Multiple acquisitions from different positions can provide complementary information and increased spatial sampling. The objective of this paper is to explore an efficient and useful reconstruction framework to reconstruct super-resolution images from super-sampled low-resolution data sets.

The authors introduce a super-sampling data acquisition model based on the physical processes with tomographic, downsampling, and shifting matrices as its building blocks. Based on the model, we extend the MLEM and Landweber algorithms to reconstruct images from super-sampled data sets. The authors also derive a backprojection-filtration-like (BPF-like) method for the super-sampling reconstruction. Furthermore, they explore variant methods for super-sampling reconstructions: the separate super-sampling resolution-modeling reconstruction and the reconstruction without downsampling to further improve image quality at the cost of more computation. The authors use simulated reconstruction of a resolution phantom to evaluate the three types of algorithms with different super-samplings at different count levels.

Contrast recovery coefficient (CRC) versus background variability, as an image-quality metric, is calculated at each iteration for all reconstructions. The authors observe that all three algorithms can significantly and consistently achieve increased CRCs at fixed background variability and reduce background artifacts with super-sampled data sets at the same count levels. For the same super-sampled data sets, the MLEM method achieves better image quality than the Landweber method, which in turn achieves better image quality than the BPF-like method. The authors also demonstrate that the reconstructions from super-sampled data sets using a fine system matrix yield improved image quality compared to the reconstructions using a coarse system matrix. Super-sampling reconstructions with different count levels showed that the more spatial-resolution improvement can be obtained with higher count at a larger iteration number.

The authors developed a super-sampling reconstruction framework that can reconstruct super-resolution images using the super-sampling data sets simultaneously with known acquisition motion. The super-sampling PET acquisition using the proposed algorithms provides an effective and economic way to improve image quality for PET imaging, which has an important implication in preclinical and clinical region-of-interest PET imaging applications.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/41/12/1.4901552.html;jsessionid=N4ohV6SiVPm6RbxpcLdWvtKO.x-aip-live-02?itemId=/content/aapm/journal/medphys/41/12/10.1118/1.4901552&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=online.medphys.org/41/12/10.1118/1.4901552&pageURL=http://scitation.aip.org/content/aapm/journal/medphys/41/12/10.1118/1.4901552'
Right1,Right2,Right3,