TY - JOUR
T1 - Single-input-dual-output modeling of image-based input function estimation
AU - Su, Yi
AU - Shoghi, Kooresh I.
N1 - Funding Information:
Acknowledgments. This project was supported by internal funding to KIS and partly by funding from the NIH/NHLBI grant 5-PO1-HL-13851 and the Washington University Small Animal Imaging Resource (WUSAIR) R24-CA83060.
PY - 2010/6
Y1 - 2010/6
N2 - Purpose: Quantification of small-animal positron emission tomography (PET) images necessitates knowledge of the plasma input function (PIF). We propose and validate a simplified hybrid single-input-dual-output (HSIDO) algorithm to estimate the PIF. Procedures: The HSIDO algorithm integrates the peak of the input function from two region-of-interest time-activity curves with a tail segment expressed by a sum of two exponentials. Partial volume parameters are optimized simultaneously. The algorithm is validated using both simulated and real small-animal PET images. In addition, the algorithm is compared to existing techniques in terms of area under curve (AUC) error, bias, and precision of compartmental model micro-parameters. Results: In general, the HSIDO method generated PIF with significantly (P<0.05) less AUC error, lower bias, and improved precision of kinetic estimates in comparison to the reference method. Conclusions: HSIDO is an improved modeling-based PIF estimation method. This method can be applied for quantitative analysis of small-animal dynamic PET studies.
AB - Purpose: Quantification of small-animal positron emission tomography (PET) images necessitates knowledge of the plasma input function (PIF). We propose and validate a simplified hybrid single-input-dual-output (HSIDO) algorithm to estimate the PIF. Procedures: The HSIDO algorithm integrates the peak of the input function from two region-of-interest time-activity curves with a tail segment expressed by a sum of two exponentials. Partial volume parameters are optimized simultaneously. The algorithm is validated using both simulated and real small-animal PET images. In addition, the algorithm is compared to existing techniques in terms of area under curve (AUC) error, bias, and precision of compartmental model micro-parameters. Results: In general, the HSIDO method generated PIF with significantly (P<0.05) less AUC error, lower bias, and improved precision of kinetic estimates in comparison to the reference method. Conclusions: HSIDO is an improved modeling-based PIF estimation method. This method can be applied for quantitative analysis of small-animal dynamic PET studies.
KW - Compartment model
KW - Input function
KW - PET
KW - Small-animal imaging
UR - http://www.scopus.com/inward/record.url?scp=77956668328&partnerID=8YFLogxK
U2 - 10.1007/s11307-009-0273-5
DO - 10.1007/s11307-009-0273-5
M3 - Article
C2 - 19949986
AN - SCOPUS:77956668328
SN - 1536-1632
VL - 12
SP - 286
EP - 294
JO - Molecular Imaging and Biology
JF - Molecular Imaging and Biology
IS - 3
ER -