Predicting Tissue Outcome in Acute Human Cerebral Ischemia Using Combined Diffusion- and Perfusion-Weighted MR Imaging

Zhou

J Cereb Blood Flow Metab

et al. 2006

312

342

The classic definition of the ischemic penumbra is a hypoperfused region in which metabolism is impaired, but still sufficient to maintain cellular polarization. Perfusion-and diffusion-weighted MRI (PWI, DWI) can identify regions of reduced perfusion and cellular depolarization, respectively, but it often remains unclear whether a PWI-DWI mismatch corresponds to benign oligemia or a true penumbra. We hypothesized that pH-weighted MRI (pHWI) can subdivide the PWI-DWI mismatch into these regions. Twenty-one rats underwent permanent middle cerebral artery occlusion and ischemic evolution over the first 3.5 h post-occlusion was studied using multiparametric MRI. End point was the stroke area defined by T 2 -hyperintensity at 24 h. In the acute phase, areas of reduced pH were always larger than or equal to DWI deficits and smaller than or equal to PWI deficits. Group analysis showed that pHWI deficits during this phase coincided with the resulting infarct area at endpoint. Final infarcts were smaller than PWI deficits (range 65% to 90%, depending on the severity of the occlusion) and much larger than acute DWI deficits. These data suggest that the outer boundary of the hypoperfused area showing a decrease in pH without DWI abnormality may correspond to the outer boundary of the ischemic penumbra, while the hypoperfused region at normal pH may correspond to benign oligemia. These first results show that pHWI can provide information complementary to PWI and DWI in the delineation of ischemic tissue.

Section: Discussionmentioning

confidence: 99%

Detection of the Ischemic Penumbra Using pH-Weighted MRI

Zhou

J Cereb Blood Flow Metab

et al. 2006

312

342

“…[6][7][8] For example, DTI is presently being explored as a research tool to study brain development, 9,10 multiple sclerosis, 11,12 amyotrophic lateral sclerosis (ALS), 13 stroke, 14,15, schizophrenia 16,17 and reading disability, 18 while trace imaging has become an essential part of clinical acute stroke assessment. [19][20][21][22] As first shown by Basser et al, 4 the diffusion ellipsoid obtained from DTI can not only provide a quantitative orientation-independent measure of diffusion anisotropy, 23,24 but also the predominant direction of water diffusion in image voxels. [25][26][27] However, it was not until the end of the decade and the beginning of the new millenium that the first successful in vivo fiber tracking results were published.…”

Section: Introductionmentioning

confidence: 91%

Fiber tracking: principles and strategies – a technical review

2002

The state of the art of reconstruction of the axonal tracts in the central nervous system (CNS) using diffusion tensor imaging (DTI) is reviewed. This relatively new technique has generated much enthusiasm and high expectations because it presently is the only approach available to non-invasively study the three-dimensional architecture of white matter tracts. While there is no doubt that DTI fiber tracking is providing exciting new opportunities to study CNS anatomy, it is very important to understand its limitations. In this review we therefore assess the basic principles and the assumptions that need to be made for each step of the study, including both data acquisition and the elaborate fiber reconstruction algorithms. Special attention is paid to situations where complications may arise, and possible solutions are reviewed. Validation issues and potential future directions and improvements are also discussed.

Magnetic Resonance in Med

“…Various models have been proposed, and the first-order difference operator (L 1 , a bidiagonal matrix (see Eq. [7] below)) is commonly used to reduce the oscillations in r (14,15). After an initial comparison between the solutions obtained with different-order difference operators (L i ) for the R lor (t) model (note that since the derivatives of an exponential function are proportional to the function, all of the L i matrices give similar results for the R exp (t) model), the first-order difference operator L 1 was chosen because it produced the closest solution to the simulated model (data not shown).…”

Section: Tikhonov Regularizationmentioning

confidence: 99%

Quantification of bolus‐tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization

Calamante

Gadian

Connelly

2003

123

129

Quantification of cerebral blood flow (CBF) and the tissue residue function (R) using bolus-tracking MRI requires deconvolution of the arterial input function (AIF). Currently, the most commonly used deconvolution method is singular value decomposition (SVD), which has been shown to produce accurate estimations of CBF. However, this method introduces unwanted oscillations in the time course of R, and there are situations in which the actual shape is of interest (e.g., in calculating flow heterogeneity and assessing bolus dispersion). In such cases, the conventional SVD method may no longer be suitable, and an alternative approach may be required. This work describes the implementation of Tikhonov regularization with the L-curve criterion to quantify CBF and obtain a better characterization of R. The methodology is tested on simulated and patient data, and the results are compared to those found using the conventional SVD approach. Although both methods produce similar CBF values, the deconvolved R shape obtained using SVD is dominated by oscillations and fails to characterize the shape in the presence of dispersion. On the other hand, the use of the proposed regularization method improves the char- Key words: perfusion; dynamic susceptibility contrast MRI; deconvolution; singular value decomposition; arterial input function Bolus-tracking MRI, also known as dynamic susceptibility contrast MRI, is becoming a well-established technique for measuring cerebral blood flow (CBF) (1). The most important clinical application by far is in the investigation of cerebral ischemia. Although some limitations have been reported (2,3), this technique is playing an increasingly important role in the diagnosis, assessment, and management of acute stroke patients (4 -7).The fundamental equation for CBF quantification is (8):where C(t) is the tissue concentration time curve, C a (t) is the arterial input function (AIF; i.e., the concentration of contrast entering the tissue of interest at time t), R(t) is the tissue residue function (i.e., the fraction of contrast agent concentration at time t for the case of an ideal instantaneous bolus injected at t ϭ 0), and the symbol V indicates the convolution operation (1). Bolus-tracking MRI relies on the deconvolution of the AIF to obtain the product function CBF ⅐ R(t), and the calculation of CBF from the initial (or maximum) value of this function (8). Currently, the most common deconvolution method is singular value decomposition (SVD), which has been shown to produce fairly accurate estimations of CBF (8). However, this method introduces unwanted oscillations 1 in the shape of R(t) (9,10), and there are situations in which the actual shape of R(t)-not just its initial value-is of interest. For example, the determination of flow heterogeneity and oxygen delivery relies on characterization of the whole residue function (11). In addition, the presence of bolus dispersion has been shown to be a significant source of error in CBF quantification (11,12), and accurate determination of the sha...