title: Investigation of different sparsity transforms for the PICCS algorithm in small-animal respiratory gated CT.
abstract: Respiratory gating helps to overcome the problem of breathing motion in cardiothoracic small-animal imaging by acquiring multiple images for each projection angle and then assigning projections to different phases. When this approach is used with a dose similar to that of a static acquisition, a low number of noisy projections are available for the reconstruction of each respiratory phase, thus leading to streak artifacts in the reconstructed images. This problem can be alleviated using a prior image constrained compressed sensing (PICCS) algorithm, which enables accurate reconstruction of highly undersampled data when a prior image is available. We compared variants of the PICCS algorithm with different transforms in the prior penalty function: gradient, unitary, and wavelet transform. In all cases the problem was solved using the Split Bregman approach, which is efficient for convex constrained optimization. The algorithms were evaluated using simulations generated from data previously acquired on a micro-CT scanner following a high-dose protocol (four times the dose of a standard static protocol). The resulting data were used to simulate scenarios with different dose levels and numbers of projections. All compressed sensing methods performed very similarly in terms of noise, spatiotemporal resolution, and streak reduction, and filtered back-projection was greatly improved. Nevertheless, the wavelet domain was found to be less prone to patchy cartoon-like artifacts than the commonly used gradient domain.
authors.affiliation: Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
authors.forename: Juan F P J
authors.initials: JF
authors.lastname: Abascal
authors.affiliation: Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
authors.forename: Monica
authors.initials: M
authors.lastname: Abella
authors.affiliation: Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Sisniega
authors.affiliation: Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
authors.forename: Juan Jose
authors.initials: JJ
authors.lastname: Vaquero
authors.affiliation: Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación en Red de Salud Mental (CIBERSAM), Madrid, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Desco
mesh_terms: D000465:Algorithms; D000818:Animals; D014057:Tomography, X-Ray Computed / Q000379:methods*
references.citation: IEEE Trans Med Imaging. 2012 Sep;31(9):1682-97
references.pmid: 22542666
references.citation: Med Phys. 2008 Oct;35(10):4649-59
references.pmid: 18975711
references.citation: Phys Med Biol. 2008 Sep 7;53(17):4683-95
references.pmid: 18695300
references.citation: IEEE Trans Med Imaging. 2012 Mar;31(3):677-88
references.pmid: 22084046
references.citation: Phys Med Biol. 2008 Oct 21;53(20):5653-73
references.pmid: 18812650
references.citation: Med Phys. 2010 Aug;37(8):4377-88
references.pmid: 20879597
references.citation: Phys Med Biol. 2010 Apr 21;55(8):2333-50
references.pmid: 20360635
references.citation: Inverse Probl. 2009 Jan 1;25(12):1230009
references.pmid: 20376330
references.citation: Med Phys. 2011 Apr;38(4):2157-67
references.pmid: 21626949
references.citation: Med Phys. 2011 Nov;38(11):6275-84
references.pmid: 22047393
references.citation: Opt Express. 2011 Jun 6;19(12):11490-506
references.pmid: 21716381
references.citation: Phys Med Biol. 2011 Oct 21;56(20):6709-21
references.pmid: 21965275
references.citation: Med Phys. 2011 Mar;38(3):1416-24
references.pmid: 21520853
references.citation: IEEE Trans Image Process. 2005 Oct;14(10):1570-82
references.pmid: 16238062
references.citation: Phys Med Biol. 2011 Mar 21;56(6):1545-61
references.pmid: 21325707
references.citation: Phys Med Biol. 2009 Oct 7;54(19):5781-804
references.pmid: 19741274
references.citation: Phys Med Biol. 2009 May 7;54(9):2791-805
references.pmid: 19369711
references.citation: Phys Med Biol. 2012 May 7;57(9):2461-76
references.pmid: 22481501
references.citation: Phys Med Biol. 2012 Apr 21;57(8):2287-307
references.pmid: 22460008
references.citation: Med Phys. 2007 Dec;34(12):4772-81
references.pmid: 18196805
references.citation: Phys Med Biol. 2008 Sep 7;53(17):4777-807
references.pmid: 18701771
references.citation: Med Phys. 2008 Feb;35(2):660-3
references.pmid: 18383687
references.citation: Magn Reson Med. 2014 Aug;72(2):369-80
references.pmid: 24105815
references.citation: IEEE Trans Biomed Eng. 1981 Feb;28(2):123-7
references.pmid: 7287019
references.citation: Med Phys. 2010 Jun;37(6):2901-9
references.pmid: 20632601
references.citation: Comput Methods Programs Biomed. 2012 Aug;107(2):218-32
references.pmid: 21908068
references.citation: Med Phys. 2007 Nov;34(11):4476-83
references.pmid: 18072512