Quantification of rodent cerebral blood flow (CBF) in normal and high flow states using pulsed arterial spin labeling magnetic resonance imaging

179

206

At its most fundamental level, cerebral blood flow (CBF) may be modeled as fluid flow driven through a network of resistors by pressure gradients. The composition of the blood as well as the cross-sectional area and length of a vessel are the major determinants of its resistance to flow. Here, we introduce a vascular graph modeling framework based on these principles that can compute blood pressure, flow and scalar transport in realistic vascular networks. By embedding the network in a computational grid representative of brain tissue, the interaction between the two compartments can be captured in a truly three-dimensional manner and may be applied, among others, to simulate oxygen extraction from the vessels. Moreover, we have devised an upscaling algorithm that significantly reduces the computational expense and eliminates the need for detailed knowledge on the topology of the capillary bed. The vascular graph framework has been applied to investigate the effect of local vascular dilation and occlusion on the flow in the surrounding network.

Section: Simulation Resultsmentioning

confidence: 50%

Vascular Graph Model to Simulate the Cerebral Blood Flow in Realistic Vascular Networks

Reichold

Stampanoni

Keller

et al. 2009

179

206

“…The mean cortical ASL signal at each of the short TIs was fitted to the following model, which takes advantage of the approximate linearity of the PASL signal as a function of TI at short TIs: 31,32 ASL signal ðshort…”

Section: (Part Ii) Multiple Inversion Time Acquisitions (N ¼ 9)mentioning

confidence: 99%

“…31,32 To estimate tissue transit time, we used a two compartment perfusion model first derived by Alsop and Detre 8 and later adapted by Wang et al 33 to incorporate the duration of the tagged bolus (t) (in a similar way to that described in Liu et al 20 but for a PASL rather than CASL sequence). Under the condition that TI4d…”

Section: (Part Ii) Multiple Inversion Time Acquisitions (N ¼ 9)mentioning

confidence: 99%

Measuring Biexponential Transverse Relaxation of the ASL Signal at 9.4 T to Estimate Arterial Oxygen Saturation and the Time of Exchange of Labeled Blood Water into Cortical Brain Tissue

Wells

Siow

Lythgoe

et al. 2012

The transverse decay of the arterial spin labeling (ASL) signal was measured at four inflow times in the rat brain cortex at 9.4 T. Biexponential T2 decay was observed that appears to derive from different T2 values associated with labeled water in the intravasculature (IV) and extravascular (EV) compartments. A two compartment biexponential model was used to assess the relative contribution of the IV and EV compartments to the ASL signal, without assuming a value for T2 of labeled blood water in the vessels. This novel methodology was applied to estimate the exchange time of blood water into EV tissue space and the oxygen saturation of blood on the arterial side of the vasculature. The mean exchange time of labeled blood water was estimated to be 370 ± 40 ms. The oxygen saturation of the arterial side of the vasculature was significantly less than 100% (B85%), which may have implications for quantitative functional magnetic resonance imaging studies where the arterial oxygen saturation is frequently assumed to be 100%.

“…18 In addition, diffusion-weighted imaging with generation of apparent diffusion coefficient maps and fractional anisotropy maps, as well as T1-and T2-weighted imaging were carried out. For details of the MRI sequences and analysis strategies please see also Wegener et al (2007 and. 17,19 Images from all animals at all time points were first co-registered and then averaged using codes written in Matlab (Mathworks, Natick, MA, USA) software.…”

Section: Magnetic Resonance Imaging Acquisition and Data Processingmentioning

confidence: 99%

“…For details of the MRI sequences and analysis strategies please see also Wegener et al (2007 and. 17,19 Images from all animals at all time points were first co-registered and then averaged using codes written in Matlab (Mathworks, Natick, MA, USA) software. Vasoreactivity (VR) maps were generated by subtracting the CBF map acquired in air from the one acquired in 5% CO 2. Voxels with significantly reduced apparent diffusion coefficient values were automatically selected as all voxels with a value of omean þ 2 s.d.…”

Section: Magnetic Resonance Imaging Acquisition and Data Processingmentioning

confidence: 99%

3-Nitropropionic Acid-Induced Ischemia Tolerance in the Rat Brain is Mediated by Reduced Metabolic Activity and Cerebral Blood Flow

Bracko

Pietro

Lazzarino

et al. 2014

Self Cite

Tissue tolerance to ischemia can be achieved by noxious stimuli that are below a threshold to cause irreversible damage ('preconditioning'). Understanding the mechanisms underlying preconditioning may lead to the identification of novel therapeutic targets for diseases such as stroke. We here used the oxidative chain inhibitor 3-nitropropionic acid (NPA) to induce ischemia tolerance in a rat middle cerebral artery occlusion (MCAO) stroke model. Cerebral blood flow (CBF) and structural integrity were characterized by longitudinal magnetic resonance imaging (MRI) in combination with behavioral, histologic, and biochemical assessment of NPA-preconditioned animals and controls. Using this approach we show that the ischemia-tolerant state is characterized by a lower energy charge potential and lower CBF, indicating a reduced baseline metabolic demand, and therefore a cellular mechanism of neural protection. Blood vessel density and structural integrity were not altered by NPA treatment. When subjected to MCAO, preconditioned animals had a characteristic MRI signature consisting of enhanced CBF maintenance within the ischemic territory and intraischemic reversal of the initial cytotoxic edema, resulting in reduced infarct volumes. Thus, our data show that tissue protection through preconditioning occurs early during ischemia and indicate that a reduced cellular metabolism is associated with tissue tolerance to ischemia.