Statistical approaches in quantitative positron emission tomography

被引:166
作者
Leahy, RM [1 ]
Qi, JY [1 ]
机构
[1] Univ So Calif, Dept Elect Engn, Inst Signal & Image Proc, Los Angeles, CA 90089 USA
关键词
positron emission tomography; computed tomography; image reconstruction; maximum likelihood estimation; Bayesian imaging;
D O I
10.1023/A:1008946426658
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Positron emission tomography is a medical imaging modality for producing 3D images of the spatial distribution of biochemical tracers within the human body. The images are reconstructed from data formed through detection of radiation resulting from the emission of positrons from radioisotopes tagged onto the tracer of interest. These measurements are approximate line integrals from which the image can be reconstructed using analytical inversion formulae. However these direct methods do not allow accurate modeling either of the detector system or of the inherent statistical fluctuations in the data. Here we review recent progress in developing statistical approaches to image estimation that can overcome these limitations. We describe the various components of the physical model and review different formulations of the inverse problem. The wide range of numerical procedures for solving these problems are then reviewed. Finally, we describe recent work aimed at quantifying the quality of the resulting images, both in terms of classical measures of estimator bias and variance, and also using measures that are of more direct clinical relevance.
引用
收藏
页码:147 / 165
页数:19
相关论文
共 120 条
[1]   Observer signal-to-noise ratios for the ML-EM algorithm. [J].
Abbey, CK ;
Barrett, HH ;
Wilson, DW .
IMAGE PERCEPTION - MEDICAL IMAGING 1996, 1996, 2712 :47-58
[2]   EVALUATION OF BREAST MASSES AND AXILLARY LYMPH-NODES WITH [F-18] 2-DEOXY-2-FLUORO-D-GLUCOSE PET [J].
ADLER, LP ;
CROWE, JP ;
ALKAISI, NK ;
SUNSHINE, JL .
RADIOLOGY, 1993, 187 (03) :743-750
[3]  
Alpert N M, 1982, IEEE Trans Med Imaging, V1, P142, DOI 10.1109/TMI.1982.4307561
[4]  
BAKER J, 1991, THESIS LAWRENCE BERK
[5]  
Barrett H. H., 1981, RADIOLOGICAL IMAGING
[6]   NOISE PROPERTIES OF THE EM ALGORITHM .1. THEORY [J].
BARRETT, HH ;
WILSON, DW ;
TSUI, BMW .
PHYSICS IN MEDICINE AND BIOLOGY, 1994, 39 (05) :833-846
[7]   OBJECTIVE ASSESSMENT OF IMAGE QUALITY - EFFECTS OF QUANTUM NOISE AND OBJECT VARIABILITY [J].
BARRETT, HH .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1990, 7 (07) :1266-1278
[8]   BAYESIAN COMPUTATION AND STOCHASTIC-SYSTEMS [J].
BESAG, J ;
GREEN, P ;
HIGDON, D ;
MENGERSEN, K .
STATISTICAL SCIENCE, 1995, 10 (01) :3-41
[9]  
BESAG J, 1986, J R STAT SOC B, V48, P259
[10]   MEAN FIELD ANNEALING - A FORMALISM FOR CONSTRUCTING GNC-LIKE ALGORITHMS [J].
BILBRO, GL ;
SNYDER, WE ;
GARNIER, SJ ;
GAULT, JW .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1992, 3 (01) :131-138