Biomedical subjects

Youichi Yamazaki

Publications and source records attributed to Youichi Yamazaki.

5 recordsLinked to original sources

PubMed2004

Automatic motion correction for quantification of myocardial perfusion with dynamic magnetic resonance imaging.

Respiratory motion makes it difficult to quantify myocardial perfusion with dynamic magnetic resonance imaging (MRI). The purpose of this study was to evaluate an automatic registration method for motion correction for quantification of myocardial perfusion with dynamic MRI. The present method was based on the gradient-based method with robust estimation of displacement parameters. For comparison, we also corrected for motion with manual registration as the benchmark. The myocardial kinetic parameters, K1 (rate constant for transfer of contrast agent from blood to myocardium) and k2 (rate constant for transfer from myocardium to blood), were calculated from dynamic images with a two-compartment model. The images corrected by the present method were similar to those corrected by manual registration. The kinetic parameters obtained after motion correction with the present method were close to those obtained after motion correction with manual registration. These results suggest that the present method is useful for motion correction for quantification of myocardial perfusion with dynamic MRI.

Contrast Media↗

PubMed2004

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.

Aged↗

PubMed2004

Deconvolution analysis of dynamic contrast-enhanced data based on singular value decomposition optimized by generalized cross validation.

PURPOSE: To present an implementation of generalized cross validation (GCV) for automatically determining the regularization parameter--i.e., the threshold value in deconvolution analysis based on truncated singular value decomposition (TSVD) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data--and to investigate the usefulness of this approach in comparison with TSVD with a fixed threshold value (TSVD-F). METHODS: Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various cerebral blood flows (CBFs), cerebral blood volumes (CBVs), and signal-to-noise ratios (SNRs) for three different types of residue functions (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The TSVD with GCV (TSVD-G) and TSVD-F with a fixed threshold value of 0.2 were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIF, and the estimated values were compared with the assumed values. Additionally, the optimal threshold value was determined from the threshold value in TSVD-F giving the mean CBF value closest to the assumed value and was compared with the threshold value determined with TSVD-G. RESULTS: With TSVD-G, the CBF estimation was substantially improved over a wide range of CBFs for all types of residue functions at the cost of more noise than was seen with TSVD-F. The dependency of the threshold value determined with TSVD-G on the CBF, CBV, and SNR was similar to that of the optimal threshold value, with some discrepancy being observed for the box-shaped residue function, although they did not always agree in terms of absolute value. CONCLUSION: Given an improved SNR, TSVD-G is useful for quantification of CBF with deconvolution analysis of DCE-MRI data.

Algorithms↗

PubMed2003

Autoregressive moving average (ARMA) model applied to quantification of cerebral blood flow using dynamic susceptibility contrast-enhanced magnetic resonance imaging.

PURPOSE: To investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD). METHODS: Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values. RESULTS: We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function. CONCLUSION: We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI.

Blood Flow Velocity↗

PubMed2002

Renal uptake rate measurement of 99m Tc-dimercaptosuccinic acid using spectral analysis.

We developed a new method for measuring the rate of renal uptake of 99mTc-dimercaptosuccinic acid (DMSA) using spectral analysis. The renal uptake rate (per minute) of DMSA (K) was calculated by averaging the tissue impulse response function values obtained by spectral analysis between 10 min and 15 min. The K values obtained by spectral analysis correlated well with the renal uptake rates (%) measured 2h after DMSA administration (r=0.921 with background correction; r=0.924 without background correction). There was a good agreement between the K values obtained by spectral analysis using the kidney time-activity curves with (x) and without (y) background correction (r=0.993, y=1.089x+0.004), suggesting that our method requires no background correction. There was excellent correlation between the K values obtained by spectral analysis using the kidney time-activity curves with (y) and without (x) kidney depth correction (r=0.992, y=1.721x+0.000 with background correction; r=0.990, y=1.720x+0.000 without background correction), suggesting that our method requires no kidney depth correction. These results indicate that spectral analysis is appropriate and useful for the quantification of renal uptake rate of DMSA. We believe that this method will facilitate even more widespread utilization of the quantitative assessment of DMSA uptake by planar scintigraphy, since it needs only 10-15 min for imaging, and background and kidney depth correction and blood sampling are not required.

Female↗