FIRST:: Fast Iterative Reconstruction Software for (PET) tomography

被引:82
作者
Herraiz, J. L. [1 ]
Espana, S.
Vaquero, J. J.
Desco, M.
Udias, J. M.
机构
[1] Univ Complutense Madrid, Dpto Fis Atom Mol & Nucl, E-28040 Madrid, Spain
[2] Hosp GU Gregorio Maranon, Unidad Med & Cirugia Expt, Madrid, Spain
关键词
D O I
10.1088/0031-9155/51/18/007
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Small animal PET scanners require high spatial resolution and good sensitivity. To reconstruct high-resolution images in 3D-PET, iterative methods, such as OSEM, are superior to analytical reconstruction algorithms, although their high computational cost is still a serious drawback. The higher performance of modern computers could make iterative image reconstruction fast enough to be viable, provided we are able to deal with the large number of probability coefficients for the system response matrix in high-resolution PET scanners, which is a difficult task that prevents the algorithms from reaching peak computing performance. Considering all possible axial and in-plane symmetries, as well as certain quasi-symmetries, we have been able to reduce the memory requirements to store the system response matrix (SRM) well below 1 GB, which allows us to keep the whole response matrix of the system inside RAM of ordinary industry-standard computers, so that the reconstruction algorithm can achieve near peak performance. The elements of the SRM are stored as cubic spline profiles and matched to voxel size during reconstruction. In this way, the advantages of `on-the-fly' calculation and of fully stored SRM are combined. The on-the-fly part of the calculation ( matching the profile functions to voxel size) of the SRM accounts for 10-30% of the reconstruction time, depending on the number of voxels chosen. We tested our approach with real data from a commercial small animal PET scanner. The results ( image quality and reconstruction time) show that the proposed technique is a feasible solution.
引用
收藏
页码:4547 / 4565
页数:19
相关论文
共 30 条
[1]   Implementation and evaluation of a 3D one-step late reconstruction algorithm for 3D positron emission tomography brain studies using median root prior [J].
Bettinardi, V ;
Pagani, E ;
Gilardi, MC ;
Alenius, S ;
Thielemans, K ;
Teras, M ;
Fazio, F .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE, 2002, 29 (01) :7-18
[2]   ITERATIVE METHODS FOR IMAGE DEBLURRING [J].
BIEMOND, J ;
LAGENDIJK, RL ;
MERSEREAU, RM .
PROCEEDINGS OF THE IEEE, 1990, 78 (05) :856-883
[3]   A row-action alternative to the EM algorithm for maximizing likelihoods in emission tomography [J].
Browne, J ;
DePierro, AR .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1996, 15 (05) :687-699
[4]   An efficient four-connected parallel system for PET image reconstruction [J].
Chen, CM .
PARALLEL COMPUTING, 1998, 24 (9-10) :1499-1522
[5]   Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms for the IndyPET scanner [J].
Frese, T ;
Rouze, NC ;
Bouman, CA ;
Sauer, K ;
Hutchins, GD .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2003, 22 (02) :258-276
[6]   BAYESIAN RECONSTRUCTIONS FROM EMISSION TOMOGRAPHY DATA USING A MODIFIED EM ALGORITHM [J].
GREEN, PJ .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1990, 9 (01) :84-93
[7]   ACCELERATED IMAGE-RECONSTRUCTION USING ORDERED SUBSETS OF PROJECTION DATA [J].
HUDSON, HM ;
LARKIN, RS .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1994, 13 (04) :601-609
[8]   A data-parallel algorithm for iterative tomographic image reconstruction [J].
Johnson, CA ;
Sofer, A .
FRONTIERS '99 - THE SEVENTH SYMPOSIUM ON THE FRONTIERS OF MASSIVELY PARALLEL COMPUTATION, PROCEEDINGS, 1999, :126-137
[9]   LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms [J].
Kadrmas, DJ .
PHYSICS IN MEDICINE AND BIOLOGY, 2004, 49 (20) :4731-4744
[10]   SS3D - Fast fully 3-D PET iterative reconstruction using stochastic sampling [J].
Kudrolli, H ;
Worstell, W ;
Zavarzin, V .
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, 2002, 49 (01) :124-130