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Nucl Med Commun. 2012 Jan;33(1):4-13. doi: 10.1097/MNM.0b013e32834c13b5.

Combining dynamic and ECG-gated ⁸²Rb-PET for practical implementation in the clinic.

Author information

  • 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, California 94107, USA. george.sayre@ucsf.edu

Abstract

OBJECTIVES:

For many cardiac clinics, list-mode PET is impractical. Therefore, separate dynamic and ECG-gated acquisitions are needed to detect harmful stenoses, indicate affected coronary arteries, and estimate stenosis severity. However, physicians usually order gated studies only because of dose, time, and cost limitations. These gated studies are limited to detection. In an effort to remove these limitations, we developed a novel curve-fitting algorithm [incomplete data (ICD)] to accurately calculate coronary flow reserve (CFR) from a combined dynamic-ECG protocol of a length equal to a typical gated scan.

METHODS:

We selected several retrospective dynamic studies to simulate shortened dynamic acquisitions of the combined protocol and compared (a) the accuracy of ICD and a nominal method in extrapolating the complete functional form of arterial input functions (AIFs); and (b) the accuracy of ICD and ICD-AP (ICD with a-posteriori knowledge of complete-data AIFs) in predicting CFRs.

RESULTS:

According to the Akaike information criterion, AIFs predicted by ICD were more accurate than those predicted by the nominal method in 11 out of 12 studies. CFRs predicted by ICD and ICD-AP were similar to complete-data predictions (PICD=0.94 and PICD-AP=0.91) and had similar average errors (eICD=2.82% and eICD-AP=2.79%).

CONCLUSION:

According to a nuclear cardiologist and an expert analyst of PET data, both ICD and ICD-AP predicted CFR values with sufficient accuracy for the clinic. Therefore, by using our method, physicians in cardiac clinics would have access to the necessary amount of information to differentiate between single-vessel and triple-vessel disease for treatment decision making.

PMID:
21934541
[PubMed - indexed for MEDLINE]
PMCID:
PMC3227790
Free PMC Article

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