Absolute quantification of cerebral blood flow in normal volunteers: Correlation between Xe-133 SPECT and dynamic susceptibility contrast MRI

Linda Knutsson — 2007-10-10T05:31:15-00:00

Journal of Magnetic Resonance Imaging, Vol. 26, No. 4. (2007), pp. 913-920.

To compare absolute cerebral blood flow (CBF) estimates obtained by dynamic susceptibility contrast MRI (DSC-MRI) and Xe-133 SPECT.CBF was measured in 20 healthy volunteers using DSC-MRI at 3T and Xe-133 SPECT. DSC-MRI was accomplished by gradient-echo EPI and CBF was calculated using a time-shift-insensitive deconvolution algorithm and regional arterial input functions (AIFs). To improve the reproducibility of AIF registration the time integral was rescaled by use of a venous output function. In the Xe-133 SPECT experiment, Xe-133 gas was inhaled over 8 minutes and CBF was calculated using a biexponential analysis.The average whole-brain CBF estimates obtained by DSC-MRI and Xe-133 SPECT were 85 ± 23 mL/(min 100 g) and 40 ± 8 mL/(min 100 g), respectively (mean ± SD, n = 20). The linear CBF relationship between the two modalities showed a correlation coefficient of r = 0.76 and was described by the equation CBF(MRI) = 2.4 · CBF(Xe)-7.9 (CBF in units of mL/(min 100 g)).A reasonable positive linear correlation between MRI-based and SPECT-based CBF estimates was observed after AIF time-integral correction. The use of DSC-MRI typically results in overestimated absolute perfusion estimates and the present study indicates that this trend is further enhanced by the use of high magnetic field strength (3T). J. Magn. Reson. Imaging 2007;26:913-920. © 2007 Wiley-Liss, Inc.

Denoising of complex MRI data by wavelet-domain filtering: Application to high-<I>b</I>-value diffusion-weighted imaging

Ronnie Wirestam — 2006-12-06T14:51:46-00:00

Magnetic Resonance in Medicine, Vol. 56, No. 5. (2006), pp. 1114-1120.

The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images). Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc.

CiteULike: xtizons Wirestam

Absolute quantification of cerebral blood flow in normal volunteers: Correlation between Xe-133 SPECT and dynamic susceptibility contrast MRI

Denoising of complex MRI data by wavelet-domain filtering: Application to high-<I>b</I>-value diffusion-weighted imaging