DocumentCode :
1356915
Title :
Kinetic Quantitation of Cerebral PET-FDG Studies Without Concurrent Blood Sampling: Statistical Recovery of the Arterial Input Function
Author :
Sullivan, F.O. ; Kirrane, J. ; Muzi, M. ; Sullivan, J. N O ; Spence, A.M. ; Mankoff, D.A. ; Krohn, K.A.
Author_Institution :
Stat. Dept., Univ. Coll. Cork, Cork, Ireland
Volume :
29
Issue :
3
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
610
Lastpage :
624
Abstract :
Kinetic quantitation of dynamic positron emission tomography (PET) studies via compartmental modeling usually requires the time-course of the radio-tracer concentration in the arterial blood as an arterial input function (AIF). For human and animal imaging applications, significant practical difficulties are associated with direct arterial sampling and as a result there is substantial interest in alternative methods that require no blood sampling at the time of the study. A fixed population template input function derived from prior experience with directly sampled arterial curves is one possibility. Image-based extraction, including requisite adjustment for spillover and recovery, is another approach. The present work considers a hybrid statistical approach based on a penalty formulation in which the information derived from a priori studies is combined in a Bayesian manner with information contained in the sampled image data in order to obtain an input function estimate. The absolute scaling of the input is achieved by an empirical calibration equation involving the injected dose together with the subject´s weight, height and gender. The technique is illustrated in the context of 18 F -Fluorodeoxyglucose (FDG) PET studies in humans. A collection of 79 arterially sampled FDG blood curves are used as a basis for a priori characterization of input function variability, including scaling characteristics. Data from a series of 12 dynamic cerebral FDG PET studies in normal subjects are used to evaluate the performance of the penalty-based AIF estimation technique. The focus of evaluations is on quantitation of FDG kinetics over a set of 10 regional brain structures. As well as the new method, a fixed population template AIF and a direct AIF estimate based on segmentation are also considered. Kinetics analyses resulting from these three AIFs are compared with those resulting from radially sampled AIFs. The proposed penalty-based AIF extr- ction method is found to achieve significant improvements over the fixed template and the segmentation methods. As well as achieving acceptable kinetic parameter accuracy, the quality of fit of the region of interest (ROI) time-course data based on the extracted AIF, matches results based on arterially sampled AIFs. In comparison, significant deviation in the estimation of FDG flux and degradation in ROI data fit are found with the template and segmentation methods. The proposed AIF extraction method is recommended for practical use.
Keywords :
belief networks; blood vessels; brain; feature extraction; image segmentation; medical image processing; positron emission tomography; statistical analysis; 18F -Fluorodeoxyglucose; Bayesian manner; Image-based extraction; PET; a priori characterization; arterial blood; arterial input function; cerebrum; compartmental modeling; direct arterial sampling; dynamic positron emission tomography; empirical calibration equation; fixed population template input function; hybrid statistical approach; kinetic quantitation; penalty-based AIF estimation; radio-tracer concentration; region of interest; scaling characteristics; segmentation; statistical recovery; Animals; Bayesian methods; Blood; Calibration; Data mining; Humans; Image sampling; Kinetic theory; Positron emission tomography; Sampling methods; Blood curve representation; image segmentation; kinetics; mixture modeling; no blood sampling; penalty method; Arteries; Bayes Theorem; Blood Specimen Collection; Brain; Female; Fluorodeoxyglucose F18; Humans; Male; Models, Biological; Models, Statistical; Positron-Emission Tomography; Radiopharmaceuticals; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2009.2029096
Filename :
5223572
Link To Document :
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