Usefulness of noise adaptive non-linear gaussian filter in FDG-PET study.

OBJECTIVE: In positron emission tomography (PET) studies, shortening transmission (TR) scan time can improve patient comfort and increase scanner throughput. However, PET images from short TR scans may be degraded due to the statistical noise included in the TR image. The purpose of this study was to apply non-linear Gaussian (NLG) and noise adaptive NLG (ANLG) filters to TR images, and to evaluate the extent of noise reduction by the ANLG filter in comparison with that by the NLG filter using phantom and clinical studies. METHODS: In phantom studies, pool phantoms of various diameters and injected doses of 2-deoxy-2-[18F]fluoro-D-glucose (FDG) were used and the coefficients of variation (CVs) of the counts in the TR images processed with the NLG and ANLG filters were compared. In clinical studies, two normal volunteers and 13 patients with tumors were studied. In volunteer studies, the CV values in the liver were compared. In patient studies, the standardized uptake values (SUVs) of tumors in the emission images were obtained after processing the TR images using the NLG and ANLG filters. RESULTS: In phantom studies, the CV values in the TR images processed with the ANLG filter were smaller than those in the images processed with the NLG filter. When using the ANLG filter, their dependency on the phantom size, injected dose of FDG and TR scan time was smaller than when using the NLG filter. In volunteer studies, the CV values in the images processed with the ANLG filter were smaller than those in the images processed with the NLG filter, and were almost constant regardless of the TR scan time. In patient studies, there was an excellent correlation between the SUVs obtained from the images with a TR scan time of 7 min processed with the NLG filter (x) and those obtained from the images with a TR scan time of 4 min processed with the ANLG filter (y) (r = 0.995, y = 1.034x - 0.075). CONCLUSIONS: Our results suggest that the ANLG filter is effective and useful for noise reduction in TR images and shortening TR scan time while maintaining the quantitative accuracy of FDG-PET studies.

Extraction of arterial input function for measurement of brain perfusion index with 99mTc compounds using fuzzy clustering.

Cerebral blood flow (CBF) can be quantified non-invasively using the brain perfusion index (BPI) determined from radionuclide angiographic data generated with 99mTc-hexamethylpropylene amine oxime (99mTc-HMPAO). When measuring the BPI, manual drawing of regions of interest (ROIs) (manual ROI method) for the extraction of the arterial input function (AIF) can lead to serious individual differences. The purpose of this study was to apply the fuzzy c-means (FCM) clustering method to determine AIF, and to investigate its usefulness in comparison with the manual ROI method. Radionuclide angiography was performed using a bolus injection of about 555 MBq of 99mTc-HMPAO, followed by sequential imaging (1 sec/frame x 120 s) using a solid-state gamma camera, and the BPI values were calculated using spectral analysis. To investigate the dependence of BPI on the ROI size, we drew five ROIs with different sizes over the aortic arch, and calculated the BPI using the manual ROI method [BPI(manual)] and the FCM clustering method [BPI(FCM)]. Furthermore, we asked 10 individuals to draw ROIs to investigate the inter-operator variability of the two methods. The mean and standard deviation (SD) of BPI(manual) increased with increasing ROI size, whereas the mean of BPI(FCM) was almost constant regardless of the ROI size; the SD of BPI(FCM) was smaller than that of BPI(manual). The inter-operator variability of the FCM clustering method was smaller than that of the manual ROI method. These results suggest that the FCM clustering method appears to be useful for the measurement of BPI, because it allows a reliable and objective determination of AIF.