Assessment of regional cerebral blood flow by dynamic susceptibility contrast MRI using different deconvolution techniques

248

215

The principles of cerebral perfusion imaging by the method of dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) (bolus tracking) are described. The MRI signals underlying DSC‐MRI are discussed. Tracer kinetics procedures are defined to calculate images of cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT). Two general categories of numerical procedures are reviewed for deriving CBF from the residue function. Procedures that involve deconvolution, such as Fourier deconvolution or singular value decomposition (SVD), are classified as model‐independent methods because they do not require a model of the microvascular hemodynamics. Those methods in principle also yield a measure of the tissue impulse response function and the residue function, from which microvascular hemodynamics can be characterized. The second category of methods is the model‐dependent methods, which use models of tracer transport and retention in the microvasculature. These methods do not yield independent measures of the residue function and may introduce bias when the physiology does not follow the model. Statistical methods are sometimes used, which involve treating the residue function as a deconvolution kernel and optimizing (fitting) the kernel from the experimental data using procedures such as maximum likelihood. Finally, other hemodynamic indices that can be measured from DSC‐MRI data are described. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

Section: Determining Cbf and The Residue Function Using Model-indepenmentioning

confidence: 95%

Principles of cerebral perfusion imaging by bolus tracking

Østergaard

2005

248

215

“…More recently, many groups have adopted gradient-echo (GE) EPI and measured changes in 1/T2* relaxation rate (⌬R2*) for DSC because these methods offer increased sensitivity to contrast agents and the ability to acquire more slices per unit time to cover more of the brain anatomy. Some studies compared the ⌬R2* AIF sampled from a proximal artery to one from a more distal artery (15,19).…”

Section: Historical Background and Signalsmentioning

confidence: 99%

“…Interestingly, the ⌬ AIF signal is also prone to saturation, although to a lesser degree (39,50). Various research groups have addressed this significant practical problem by using a shorter TE for AIF imaging compared to brain imaging in FLASH (12,19,51,52) and EPI (37,53), or by using segmented double-echo EPI (54).…”

Section: Signal Saturation and Aliasingmentioning

confidence: 99%

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

Conturo

Akbudak

Kotys

et al. 2005

Cerebral perfusion imaging using dynamic susceptibility contrast (DSC) has been the subject of considerable research and shows promise for basic science and clinical use. In DSC, the MRI signals in brain tissue and feeding arteries are monitored dynamically in response to a bolus injection of paramagnetic agents, such as gadolinium (Gd) chelates. DSC has the potential to allow quantitative imaging of parameters such as cerebral blood flow (CBF) with a high signal‐to‐noise ratio (SNR) in a short scan time; however, quantitation depends critically on accurate and precise measurement of the arterial input function (AIF). We discuss many requirements and factors that make it difficult to measure the AIF. The AIF signal should be linear with respect to Gd concentration, convertible to the same concentration scale as the tissue signal, and independent of hematocrit. Complicated relationships between signal and concentration can violate these requirements. The additional requirements of a high SNR and high spatial/temporal resolution are technically challenging. AIF measurements can also be affected by signal saturation and aliasing, as well as dispersion/delay between the AIF sampling site and the tissue. We present new in vivo preliminary results for magnitude‐based (ΔR2*) and phase‐based (Δϕ) AIF measurements that show a linearity advantage of phase, and a disparity in the scaling of Δϕ AIFs, ΔR2* AIFs, and ΔR2* tissue curves. Finally, we discuss issues related to the choice of AIF signal for quantitative perfusion imaging. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

“…A number of deconvolution methods have been proposed in the recent past [4][5][6][7][8][9][10][11]. Each of these techniques has advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Cerebral blood flow estimation from perfusion-weighted MRI using FT-based MMSE filtering method

Sakoğlu

Sood

2008

Introduction-Perfusion weighted MRI can be used for estimating blood flow parameters using bolus tracking technique based on DSC MRI. In order to extract flow parameters, several deconvolution techniques have been proposed, of which, the SVD and FT based techniques are more popular and widely used. In this work, an FT based method has been proposed that involves derivation of an optimal shaped filter (defined as a filter function) estimated using minimum mean-squared error (MMSE) technique in the frequency domain. The proposed technique has been compared with the well established SVD technique using simulation experiments.