A Hybrid Clustering Method for ROI Delineation in Small-animal Dynamic PET Images: Application to the Automatic Estimation of FDG Input Functions

Overview

Journal IEEE Trans Inf Technol Biomed

Date 2010 Oct 19

PMID 20952342

Citations 4

Authors

Xiujuan Zheng

Guangjian Tian

Sung-Cheng Huang

Dagan Feng

Affiliations

Soon will be listed here.

Abstract

Tracer kinetic modeling with dynamic positron emission tomography (PET) requires a plasma time-activity curve (PTAC) as an input function. Several image-derived input function (IDIF) methods that rely on drawing the region of interest (ROI) in large vascular structures have been proposed to overcome the problems caused by the invasive approach for obtaining the PTAC, especially for small-animal studies. However, the manual placement of ROIs for estimating IDIF is subjective and labor-intensive, making it an undesirable and unreliable process. In this paper, we propose a novel hybrid clustering method (HCM) that objectively delineates ROIs in dynamic PET images for the estimation of IDIFs, and demonstrate its application to the mouse PET studies acquired with [ (18)F]Fluoro-2-deoxy-2-D-glucose (FDG). We begin our HCM using k-means clustering for background removal. We then model the time-activity curves using polynomial regression mixture models in curve clustering for heart structure detection. The hierarchical clustering is finally applied for ROI refinements. The HCM achieved accurate ROI delineation in both computer simulations and experimental mouse studies. In the mouse studies, the predicted IDIF had a high correlation with the gold standard, the PTAC derived from the invasive blood samples. The results indicate that the proposed HCM has a great potential in ROI delineation for automatic estimation of IDIF in dynamic FDG-PET studies.

Citing Articles

Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents.

Tomonari Y, Onishi Y, Hashimoto F, Ote K, Okamoto T, Ohba H Ann Nucl Med. 2024; 38(7):544-552.

PMID: 38717535 DOI: 10.1007/s12149-024-01937-1.

Automated extraction of the arterial input function from brain images for parametric PET studies.

Moradi H, Vashistha R, Ghosh S, OBrien K, Hammond A, Rominger A EJNMMI Res. 2024; 14(1):33.

PMID: 38558200 PMC: 11372015. DOI: 10.1186/s13550-024-01100-x.

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo.

Wang H, Stout D, Chatziioannou A Med Image Anal. 2013; 17(4):401-16.

PMID: 23542374 PMC: 3667217. DOI: 10.1016/j.media.2013.02.009.

FDG-PET Quantification of Lung Inflammation with Image-Derived Blood Input Function in Mice.

Locke L, Williams M, Fairchild K, Zhong M, Kundu B, Berr S Int J Mol Imaging. 2011; 2011:356730.

PMID: 22187641 PMC: 3236466. DOI: 10.1155/2011/356730.

References

Gambhir S . Molecular imaging of cancer with positron emission tomography. Nat Rev Cancer. 2002; 2(9):683-93. DOI: 10.1038/nrc882. View

Liptrot M, Adams K, Martiny L, Pinborg L, Lonsdale M, Olsen N . Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling. Neuroimage. 2004; 21(2):483-93. DOI: 10.1016/j.neuroimage.2003.09.058. View

Zanotti-Fregonara P, Maroy R, Comtat C, Jan S, Gaura V, Bar-Hen A . Comparison of 3 methods of automated internal carotid segmentation in human brain PET studies: application to the estimation of arterial input function. J Nucl Med. 2009; 50(3):461-7. DOI: 10.2967/jnumed.108.059642. View

Willmann J, van Bruggen N, Dinkelborg L, Gambhir S . Molecular imaging in drug development. Nat Rev Drug Discov. 2008; 7(7):591-607. DOI: 10.1038/nrd2290. View

Shattuck D, Prasad G, Mirza M, Narr K, Toga A . Online resource for validation of brain segmentation methods. Neuroimage. 2008; 45(2):431-9. PMC: 2757629. DOI: 10.1016/j.neuroimage.2008.10.066. View

Fang Y, Muzic Jr R . Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies. J Nucl Med. 2008; 49(4):606-14. DOI: 10.2967/jnumed.107.047613. View

Wong K, Feng D, Meikle S, Fulham M . Simultaneous estimation of physiological parameters and the input function--in vivo PET data. IEEE Trans Inf Technol Biomed. 2001; 5(1):67-76. DOI: 10.1109/4233.908397. View

Feng D, Wong K, Wu C, Siu W . A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study. IEEE Trans Inf Technol Biomed. 2000; 1(4):243-54. DOI: 10.1109/4233.681168. View

Kim J, Cai W, Feng D, Eberl S . Segmentation of VOI from multidimensional dynamic PET images by integrating spatial and temporal features. IEEE Trans Inf Technol Biomed. 2006; 10(4):637-46. DOI: 10.1109/titb.2006.874192. View

10.

Iida H, Rhodes C, De Silva R, Araujo L, Bloomfield P, Lammertsma A . Use of the left ventricular time-activity curve as a noninvasive input function in dynamic oxygen-15-water positron emission tomography. J Nucl Med. 1992; 33(9):1669-77. View

11.

Eberl S, Anayat A, Fulton R, Hooper P, Fulham M . Evaluation of two population-based input functions for quantitative neurological FDG PET studies. Eur J Nucl Med. 1997; 24(3):299-304. DOI: 10.1007/BF01728767. View

12.

Chow P, Rannou F, Chatziioannou A . Attenuation correction for small animal PET tomographs. Phys Med Biol. 2005; 50(8):1837-50. PMC: 1479306. DOI: 10.1088/0031-9155/50/8/014. View

13.

Ohtake T, Kosaka N, Watanabe T, Yokoyama I, Moritan T, Masuo M . Noninvasive method to obtain input function for measuring tissue glucose utilization of thoracic and abdominal organs. J Nucl Med. 1991; 32(7):1432-8. View

14.

PATLAK C, Blasberg R . Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab. 1985; 5(4):584-90. DOI: 10.1038/jcbfm.1985.87. View

15.

van den Hoff J, Burchert W, Muller-Schauenburg W, Meyer G, Hundeshagen H . Accurate local blood flow measurements with dynamic PET: fast determination of input function delay and dispersion by multilinear minimization. J Nucl Med. 1993; 34(10):1770-7. View

16.

Maroy R, Boisgard R, Comtat C, Frouin V, Cathier P, Duchesnay E . Segmentation of rodent whole-body dynamic PET images: an unsupervised method based on voxel dynamics. IEEE Trans Med Imaging. 2008; 27(3):342-54. DOI: 10.1109/TMI.2007.905106. View

17.

Dogdas B, Stout D, Chatziioannou A, Leahy R . Digimouse: a 3D whole body mouse atlas from CT and cryosection data. Phys Med Biol. 2007; 52(3):577-87. PMC: 3006167. DOI: 10.1088/0031-9155/52/3/003. View

18.

Watabe H, Channing M, Riddell C, Jousse F, Libutti S, Carrasquillo J . Noninvasive estimation of the aorta input function for measurement of tumor blood flow with. IEEE Trans Med Imaging. 2001; 20(3):164-74. DOI: 10.1109/42.918468. View

19.

Kimura Y, Senda M, Alpert N . Fast formation of statistically reliable FDG parametric images based on clustering and principal components. Phys Med Biol. 2002; 47(3):455-68. DOI: 10.1088/0031-9155/47/3/307. View

20.

Vriens D, de Geus-Oei L, Oyen W, Visser E . A curve-fitting approach to estimate the arterial plasma input function for the assessment of glucose metabolic rate and response to treatment. J Nucl Med. 2009; 50(12):1933-9. DOI: 10.2967/jnumed.109.065243. View