Makoto Nagayoshi

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×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.

Spectral analysis of99mTc-HMPAO for estimating cerebral blood flow: A comparison with H2 15O PET

Cerebral blood flow (CBF) can be quantified non-invasively using the brain perfusion index (BPI), which is determined using radionuclide angiographic data obtained through the use of technetium-99m hexamethylpropylene amine oxime (99mTc-HMPAO). The BPI is generally calculated using graphical analysis (GA). In this study, BPI was measured using spectral analysis (SA), and the usefulness of SA was compared with that of GA. Thirteen patients with various brain diseases and four healthy male volunteers were examined using radionuclide angiography with99mTc-HMPAO. The BPI was measured for each subject using both SA and GA. In the four healthy volunteers, the BPI was examined at rest and after the intravenous administration of 1 g of acetazolamide (ACZ). An H215O PET examination was also performed in the 13 patients; the BPIS and BPIG values were compared with the CBF measurements obtained using H215O PET (CBFPET). The BPI values obtained by SA (BPIS) (x) and by GA (BPIG) (y) were correlated (y = 0.568x + 0.055, r = 0.901) in the 13 patients and four healthy volunteers at rest, although the BPIG values were underestimated by 36.1 ± 7.5% (mean ± SD) compared with the BPIS values. The degree of underestimation tended to increase with increasing BPIS values. The increase in the BPIS was 32.1 ± 8.0% after the intravenous administration of ACZ, while the increase in BPIG was only 8.1 ± 2.8%. This discrepancy was considered to be the result of the BPIG values being affected by the first-pass extraction fraction of the tracer. Although both BPIS and BPIG values were significantly correlated with the CBFPET values, the correlation coefficient for BPIS was higher than that for BPIG (BPIS: r = 0.881; BPIG: r = 0.832). These results suggest that SA produces a more reliable BPI for quantifying CBF using99mTc-HMPAO than the conventional method using GA. The SA method should be especially useful for activation studies involving pharmacological intervention and/or clinical cases with an increased CBF.

Interobserver variability of cerebral blood flow measurements obtained using spectral analysis and technetium-99m labeled compounds

Radionuclide angiography with technetium-99m hexamethylpropylene amine oxime (99mTc-HMPAO) or technetium-99m ethyl cysteinate dimer (99mTc-ECD) enables the non-invasive estimation of absolute cerebral blood flow (CBF) to be determined by using spectral analysis (SA). We previously demonstrated the clinical use of SA; however, this method involves a few manual steps. The aim of this study was to evaluate the interobserver variability of CBF estimations made using SA and compare these results with those obtained by using graphical analysis (GA). In twenty patients with various brain diseases (27–74 years old), radionuclide angiography examinations were performed using99mTc-labeled compounds (10 patients,99mTc-HMPAO; 10 patients,99mTc-ECD). Bilateral cerebral hemispheres were studied in all patients, and the brain perfusion index (BPI) values were estimated using SA and GA. The interobserver variability between two observers was then assessed. A good correlation between the BPI values assessed using both SA (BPIS) and GA (BPIG) was obtained. The correlation coefficient for BPIS (r=0.987) was almost the same as that for BPIG (r=0.982). The degree of interobserver variability was not affected by the measurement of elevated BPI values. Measurements carried out by two observers using both SA and GA exhibited a similar degree of interobserver variability. SA appears to have a satisfactory interobserver variability and may be more suitable for clinical applications.

Survival prediction using artificial neural networks in patients with lung cancer treated by radiotherapy

It is generally known that artificial neural networks (ANNs) may be used for non-linear analysis of complex data. The purpose of this study was to construct the model of prediction using ANNs from radiation oncology database, and to evaluate the usefulness of ANNs for survival prediction in patients with lung cancer treated by radiation therapy.

Development of a simple and non-invasive method for measuring cerebral blood flow using Technetium-99m compounds and spectral analysis

Cerebral blood flow (CBF) can be quantified using the brain perfusion index (BPI), determined from radionuclide angiographic data generated by 99mTc-HMPAO or 99mTc-ECD. The BPI is generally calculated using graphical analysis (GA). However, the BPI values obtained by GA (BPIG) are not in proportion to CBF. In this study, we measured BPI using spectral analysis (SA) (BPIS), and compared them with BPIG to investigate the carefulness of BPIS.

Optimization of an anisotropic diffusion method for medical image processing

An anisotropic diffusion (AD) method is based on a new concept for image processing, in which smoothing is formulated as a diffusive process and is suppressed or stopped at boundaries by selecting locally adaptive diffusion strengths [1]. It is known that this method is capable of reducing image noise, while preserving the spatial resolution of images. However, the results depend on the parameters used in the diffusion function (DF) adopted in the AD method. Then, this study was undertaken to optimize the parameters used in DF by computer simulations.