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Cerebral Blood Volume

Cerebral Blood Volume (CBV) refers to the total volume of blood present within the brain at a given time.
This parameter is a crucial indicator of brain function and perfusion, as it reflects the balance between blood supply and metabolic demand.
Accurate assessment of CBV can provide valuable insights into various neurological conditions, such as stroke, traumatic brain injury, and neurodegenerative diseases.
Advances in neuroimageing techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), have enabled non-invasive measurement of CBV, allowing researchers and clinicians to monitor changes in cerebral perfusion and oxygenation.
Understanding the dynamics of CBV and its relationship to brain health is an active area of research, with the potential to inform the development of new diagnostic and therapeutic strategies.

Most cited protocols related to «Cerebral Blood Volume»

1
Multimodal Perfusion Imaging in Acute Stroke
Data analysis was performed with Interactive Data Language (IDL (Boulder, CO, USA)) software programs developed in-house. ASL images were corrected for motion, pairwise subtracted between label and control images followed by averaging to generate the mean difference image (ΔM). Quantitative CBF (f) maps were calculated based on the following equation 14 (link),
f=λΔMR1a2αM0[exp(−wR1a)−exp(−(τ+w)R1a)] where R1a (=0.72/0.61sec−1 at 1.5/3T) is the longitudinal relaxation rate of blood, M0 is the equilibrium magnetization of brain tissue, α (=0.8) is the tagging efficiency, τ (=1.5sec) is the duration of the labeling pulse, w (=2sec) is the post-labeling delay time and λ (=0.9g/ml) is blood/tissue water partition coefficient. Equation [1] assumes that the labeled blood spins remain primarily in the vasculature rather than exchanging completely with tissue water, which is justified in stroke patients in whom arterial transit times are likely prolonged 13 (link).
Post-processing of DSC images yielded multi-parametric perfusion maps including CBF, cerebral blood volume (CBV), Tmax and mean transit time (MTT), according to previously described analysis procedures 15 (link). Two CBF values were calculated from DSC data, namely CBFr0 and CBFrm, based on the value at time 0 and Tmax of the tissue residual function (R(t)), respectively. The calculation of CBFr0 may not represent the standard processing of DSC perfusion MRI, but was used to inform the comparison with ASL CBF. In each case, all structural, diffusion and perfusion images were aligned using SPM8 (Wellcome Department of Cognitive Neurology, UCL, UK). Two neuroradiologists and one perfusion MRI expert blinded to treatment and clinical information independently and separately reviewed ASL and DSC perfusion maps, which were scored on a scale of 0–3 to rate image quality and lesion severity/conspicuity, respectively. Both hypo- and hyper-perfusion were noted.
ASL and DSC perfusion images were further normalized into the Montreal Neurological Institute (MNI) template space using SPM8. Subsequently, segmentation of ASL and DSC perfusion images into major vascular territories was performed using an automated region-of-interest (ROI) analysis based on a published template of vascular territories in both hemispheres 16 (link). The vascular territories studied were anterior cerebral artery (ACA), posterior cerebral artery (PCA), and leptomeningeal and lenticulostriate (perforator) distributions of the middle cerebral artery (MCA). In addition, in AIS patients demonstrating hypoperfusion, ROIs defined by Tmax > 6s, 2s < Tmax < 6s and Tmax < 2s were used to extract corresponding ASL and DSC CBF values respectively. Manual restriction of the ROIs was applied when necessary.
Wang D.J., Alger J.R., Qiao J.X., Hao Q., Hou S., Fiaz R., Gunther M., Pope W.B., Saver J.L., Salamon N, & Liebeskind D.S. (2012). The Value of Arterial Spin-Labeled Perfusion Imaging in Acute Ischemic Stroke – Comparison with Dynamic Susceptibility Contrast Enhanced MRI. Stroke; a Journal of Cerebral Circulation, 43(4), 1018-1024.
Publication 2012
Arteries BLOOD Blood Vessel Brain Cerebral Arteries, Anterior Cerebral Blood Volume Cerebrovascular Accident Cognition Diffusion Microtubule-Associated Proteins Middle Cerebral Artery Patients Perfusion Posterior Cerebral Artery Pulse Rate Tissues
2
Multisite CO2 Inhalation BOLD CVR Study

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Publication 2019
Adult Capnography Cerebral Blood Volume Cerebrospinal Fluid Clip ECHO protocol Ethics Committees, Research fMRI Hypersensitivity Inhalation Medical Devices Nose Obstetric Delivery Radionuclide Imaging
3
Measuring Cerebral Autoregulation in Neonatal Hypothermia
Neonates received bilateral forehead NIRS monitoring with an INVOS 5100 (Medtronic, Minneapolis, MN). HVx was calculated from the NIRS and arterial blood pressure signals as previously described during hypothermia, rewarming (defined as rectal temperature 34.1–36.5°C), and the first six hours of normothermia. [3 (link)–5 , 11 (link)] Briefly, HVx is calculated from deoxygenated and oxygenated hemoglobin optical densities, [11 (link)] which decreases this index’s sensitivity to changes in systemic oxygenation when compared to metrics based solely on oxyhemoglobin. We synchronously sampled MAP from the arterial blood pressure catheter and NIRS signals using ICM+ software (Cambridge Enterprises, Cambridge, UK). HVx is calculated by a continuous, moving correlation coefficient between MAP and the NIRS rTHb (rTHb=1-optical density_A*50), a surrogate measure of cerebral blood volume, [11 (link)] after removal of signal artifacts and high-frequency waves from respiration and pulse. [3 (link)–5 , 14 (link)] When autoregulatory vasoreactivity is functional, rTHb and MAP have negative or near-zero correlation, and HVx is negative or near-zero. During periods of dysfunctional autoregulation, rTHb and MAP become positively correlated, and HVx approaches +1 with progressive impairments in autoregulation. [11 (link)] We verified that neonates did not have unilateral intracranial lesions and averaged the right and left HVx values for sorting into 5-mmHg bins of MAP. The most negative HVx identified the MAPOPT at which autoregulation was most robust with maximal vasoreactivity to changes in MAP during hypothermia, rewarming, and normothermia. The neonate was coded as “unidentifiable MAPOPT” if a nadir in HVx could not be identified. [3 (link)–5 ] An investigator (JKL) blinded to outcomes and medical histories identified the MAPOPT values with corroboration by additional investigators (FJN, MG). Blood pressure was analyzed as the (1) maximal blood pressure deviation below or above MAPOPT; (2) duration with blood pressure below, within, or above MAPOPT analyzed as a percentage of the autoregulation monitoring period; and (3) area under the curve (AUC; min* mmHg/h) to combine time (min) spent with blood pressure below MAPOPT and blood pressure deviation (mmHg) below MAPOPT normalized for the monitoring duration (h) in each period. [4 (link), 15 (link)] We also calculated the percentage of the hypothermia, rewarming, and normothermia periods that neonates spent with MAP below gestational age (weeks)+5, a common clinical guide for neonatal hemodynamic goals.[16 ]
Lee J.K., Poretti A., Perin J., Huisman T.A., Parkinson C., Chavez-Valdez R., O’Connor M., Reyes M., Armstrong J., Jennings J.M., Gilmore M.M., Koehler R.C., Northington F.J, & Tekes A. (2016). Optimizing cerebral autoregulation may decrease neonatal regional hypoxic-ischemic brain injury. Developmental neuroscience, 39(1-4), 248-256.
Publication 2016
Blood Pressure Catheters Cell Respiration Cerebral Blood Volume Forehead Gestational Age Hemodynamics Homeostasis Hypersensitivity Infant, Newborn Oxyhemoglobin Pulse Rate Rectum Spectroscopy, Near-Infrared Vision
4
Perfusion CT Analysis for Acute Stroke
All perfusion CT data were analysed with the same commercial software (MiStar). Perfusion data were processed using a single value deconvolution algorithm with delay and dispersion correction with cerebral blood flow and cerebral blood volume being determined by the peak height and area under the curve of the input residue function, respectively, with mean transit time calculated as the ratio of cerebral blood volume to cerebral blood flow (Bivard and Parsons, 2012 (link)). Arterial input function and venous outflow function were automatically selected by the software from the non-stroke middle cerebral artery/anterior cerebral artery and superior sagittal sinus, respectively. Previously validated thresholds were applied to measure the volume of the acute perfusion lesion (relative delay time, delay time >3 s) and acute ischaemic core (relative cerebral blood flow <40 and relative delay time >3 s). Major reperfusion was defined as a reduction in the acute 24-h perfusion lesion volume of >80% (Wintermark et al., 2006 (link)). Penumbral volume was calculated from the volume of the perfusion lesion (delay time threshold >3 s) minus the volume of the ischaemic core (relative cerebral blood flow threshold <40% within the delay time >3-s lesion).
Using the above post-processing, patients were classified based on quantitative perfusion CT lesion volumes as either: (i) ‘target’ mismatch (perfusion lesion-core mismatch ratio >1.8 and perfusion lesion volume >15 ml, core <70 ml); (ii) large perfusion CT ischaemic core (>70 ml); or (iii) no ‘target’ mismatch (perfusion lesion-core mismatch ratio <1.8 or volume <15 ml, core <70 ml, Lansberg et al., 2012 (link)). Symptomatic intracranial haemorrhage was defined as the presence of parenchymal haematoma type 2 and deterioration on NIHSS of ≥4 within the first 36 h.
Bivard A., Levi C., Krishnamurthy V., McElduff P., Miteff F., Spratt N.J., Bateman G., Donnan G., Davis S, & Parsons M. (2015). Perfusion computed tomography to assist decision making for stroke thrombolysis. Brain, 138(7), 1919-1931.
Publication 2015
Arteries Cerebral Arteries, Anterior Cerebral Blood Volume Cerebrovascular Accident Cerebrovascular Circulation Cone-Beam Computed Tomography Hematoma Intracranial Hemorrhage Middle Cerebral Artery Patients Perfusion Reperfusion Sinus, Superior Sagittal Veins
5
Quantitative Perfusion Imaging in Sturge-Weber Syndrome
Relative Cerebral Blood Flow (rCBF), relative Mean Transit Time (rMTT), and relative Cerebral Blood Volume (CBV) maps were derived from the perfusion data using software developed in-house (Signal process in neuroradiology, SPIN). To create perfusion maps, deconvolution with singular value decomposition (SVD) was used to create quantitative maps of rCBF, rCBV and MTT (19 (link), 20 (link)). The position of the arterial input function (AIF) was automatically determined by using the maximum concentration (Cmax), time to peak (TTP) and first moment MTT (fMTT). The concentration-time curve for arteries has short fMTT, short TTP and high Cmax. Twenty voxels, which best fit these properties were selected. Then the concentration-time curves of these voxels were averaged, smoothed and truncated to avoid the second pass of the tracer. rCBF, rCBV and MTT values in the white matter ipsilateral to the angioma and in contralateral homotopic white matter were manually measured by two neuroradiologists. White matter, rather than cortex, was analyzed in this study, as selective and accurate measurement of cortical perfusion would be difficult in children with SWS due to the confounding effect of the low-flow leptomeningeal angioma directly overlaying affected cortical regions, as well as artifact from cortical calcification. Affected white matter was defined as the presence of abnormally enhancing vessels or the presence of a leptomeningeal venous angioma overlying the cortical surface. Two to three regions of interest (ROIs; size: 20–40 pixels) were placed on every slice with an apparent vessel abnormality in the affected and contralateral white matter (2–8 slices in individual patients, depending on the extent of vessel abnormality; mean: 4.5 slices), as seen in figure 2. An effort was made to avoid placing the ROIs over vessels, cortex and ventricle. ROI placement was performed using the CBF map, a map which shades the vessels and cortex with a red and green scale while the white matter is shaded blue, allowing for accurate placement of ROIs solely in white matter. If cortical calcification was seen in the affected lobe, ROIs were set in the WM in the same lobe. Furthermore, all parameters were measured slice by slice, covering all affected regions. The final CBF values quoted for each patient were the mean of measurements in all slices of affected cortex.
Wu J., Tarabishy B., Hu J., Miao Y., Cai Z., Xuan Y., Behen M., Li M., Ye Y., Shoskey R., Haacke E.M, & Juhász C. (2011). Cortical Calcification in Sturge-Weber Syndrome on MRI-SWI: Relation to Brain Perfusion Status and Seizure Severity. Journal of magnetic resonance imaging : JMRI, 34(4), 791-798.
Publication 2011
Angioma Arteries Blood Vessel Calcinosis Cerebral Blood Volume Cerebral Ventricles Cerebrovascular Circulation Child Cortex, Cerebral Kidney Cortex Maritally Unattached Microtubule-Associated Proteins Patients Perfusion Temporal Arteries Veins Vision White Matter

Most recents protocols related to «Cerebral Blood Volume»

1
Multimodal CT Imaging for Acute Stroke
Baseline CT imaging included brain non-contrast CT, CTP, and CTA, obtained with different CT scanners (64, 128, 256, or 320 detectors, with Toshiba [Tokyo, Japan], Siemens [Munich, Germany], or GE [Cleveland, OH, USA] scanners). The axial coverage ranged from 80 to 160 mm.
The CTP data were processed by commercial software MIStar (Apollo Medical Imaging Technology, Melbourne, Vic, Australia). CTP parameters were generated by applying the mathematical algorithm of singular value decomposition with delay and dispersion correction (20 (link), 21 (link)). The following four CTP parameters were generated: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and delay time (DT). The penumbra and core volume were measured on acute CTP with dual threshold setting (22 (link)): DT at the threshold of 3 s for whole ischemic lesion volume and CBF at the threshold setting of 30% for acute core volume. The collateral index was defined by the ratio of DT >6 s/DT >2 s volume.
Wang X., Zhang H., Wang Q., Li G., Shen H., Xiao Y., Xu L., Long Y., Chen C., Huang Z, & Zhang Y. (2023). Effect of intravenous thrombolysis on core growth rate in patients with acute cerebral infarction. Frontiers in Neurology, 14, 1096605.
Publication 2023
Brain CAT SCANNERS X RAY Cerebral Blood Volume Cerebrovascular Circulation Maritally Unattached
2
Validating Acute Stroke Perfusion Imaging
We analyzed CTP images from the International Stroke Perfusion Imaging Registry (INSPIRE), which is a database of acute stroke perfusion imaging and associated clinical information. For this study we used consecutive patients presenting with acute ischemic stroke who had whole brain CTP and who were recruited into INSPIRE between 2010 and 2017 at the John Hunter Hospital, Newcastle, Australia. For standardization, only one site was used at this stage. As is routine in INSPIRE, patients all underwent baseline multimodal CT imaging with non-contrast CT, CTA, and CTP. Written informed consent was obtained from all participants, and the INSPIRE study was approved by the site's ethics committee (23 (link)).
To obtain the perfusion images, a total of 19 acquisitions occurred over 60 s. The CTP data were processed by commercial software MIStar (Apollo Medical Imaging Technology, Melbourne, VIC, Australia). CTP parameters were generated by applying the mathematical algorithm of singular value decomposition with delay and dispersion correction (24 (link)). The following four CTP parameters were generated: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and delay time (DT). The penumbra and core volumes were defined with dual thresholds: DT at the threshold of 3 s for total ischemic lesion volume and CBF at the threshold setting of 30% for acute core volume (8 (link), 16 (link), 25 (link)). After single-value thresholding, core/penumbra areas were limited to a single lesion and artifactual or erroneous regions were removed. The resulting map was used as the ground truth (GT). Core/penumbra were reviewed by experts to ensure they were accurate.
To develop the model, we used 86 acute ischemic stroke patients with a large vessel occlusion (LVO): M1 segment of the middle cerebral artery (MCA) or internal carotid artery (ICA). To provide additional testing and external validation, 25 patients were used, with both LVO and non-LVO occlusions. This was done to observe whether a model trained only on lesions resulting from an occlusion of large vessel will perform as well when testing on a variety of occlusion sites. Each patient in the test set underwent follow-up MR diffusion-weighted imaging (DWI) between 24 and 72 h after onset. The volume (mL) of the infarct core, as estimated by MR-DWI, was recorded and used for external validation. On follow-up imaging, all patients had a thrombolysis in cerebral infarction (TICI) score of at least 2b, indicating relatively complete reperfusion of initially hypoperfused regions. In these cases, the volume of the acute CTP core should more closely match that of the follow-up infarct core and could therefore be used to validate the predictions.
Werdiger F., Parsons M.W., Visser M., Levi C., Spratt N., Kleinig T., Lin L, & Bivard A. (2023). Machine learning segmentation of core and penumbra from acute stroke CT perfusion data. Frontiers in Neurology, 14, 1098562.
Publication 2023
Acute Cerebrovascular Accidents Acute Ischemic Stroke Blood Vessel Brain Cerebral Blood Volume Cerebral Infarction Cerebrovascular Accident Cerebrovascular Circulation Dental Occlusion Diffusion Ethics Committees Fibrinolytic Agents Infarction Internal Carotid Arteries Middle Cerebral Artery Multimodal Imaging Patients Perfusion Reperfusion
3
Comprehensive Ischemic Stroke Biomarker Analysis
Peripheral venous blood samples of ICH patients were collected after at least 8 h within 24 h of patients’ hospital admission and 7 days after ICH. Those of healthy controls were collected at enrollment in our hospital from January 2022 to May 2022. Relevant information such as age, gender, vascular risk factors (hypertension and diabetes mellitus), cigarette smoking, alcohol consumption, leucocyte count, blood glucose, and potassium level were collected. The Siemens Leonardo V software for semiautomatic CT volumetry has been used for assessment of hematoma and perihemorrhagic edema volumes (Kollmar et al., 2012 (link)). CT perfusion images were transferred to a workstation (Philips Healthcare) to generate perfusion parameter maps of the cerebral blood flow, cerebral blood volume, time to peak, mean transit time and permeability surface (PS).
Wu M., Chen K., Jiang M., Xie F., Cao X., Chen L., Chen Z, & Yin X. (2023). High plasma complement C4 levels as a novel predictor of clinical outcome in intracerebral hemorrhage. Frontiers in Aging Neuroscience, 15, 1103278.
Publication 2023
BLOOD Blood Glucose Blood Vessel Cerebral Blood Volume Cerebrovascular Circulation Diabetes Mellitus Edema Gender Hematoma High Blood Pressures Leukocyte Count Microtubule-Associated Proteins Patient Admission Patients Perfusion Permeability Potassium Tomography, Spiral Computed Veins
4
Ultrafast Compound Doppler Imaging for Brain Activity Mapping
To discriminate blood signals from tissue clutter, the ultrafast compound Doppler frame stack was filtered via Singular Value Decomposition33 (link), removing the N = 60 first components. The Cerebral Blood Volume (CBV) frames obtained were further normalized by a baseline image corresponding to the average of the first 3 min of the acquisition, leading to n∆CBV frames. For each pixel, the mean value of the baseline distribution is subtracted and divided to obtain a ∆F/F (activity expressed as a percent of change relative to the baseline). ROIs were extracted from the Paxinos Atlas21 and carefully overlayed onto the fUS images using salient anatomical and vascular structures (cortex edges, sine veins, Willis polygon when visible). This led to 71 and 82 ROIs used for locomotion and sleep/wake state decoding respectively (cf. Fig. 1).
In order to apply a model trained on one acquisition to a different acquisition, only ROIs present on all acquisitions were kept within the locomotion and the sleep/wake datasets. ROIs smaller than 20 pixels on the original image were included in the closest anatomical ROI in terms of location and function. This led to 26 and 22 ROIs for the decoding of movement and sleep/wake state respectively.
For the identification of the sleep/wake state on several coronal planes of the same rat, the regions were grouped into 53 symmetric anatomical regions to keep a coherence between the planes. These regions as well as the corresponding acronyms and Paxinos regions included are given in Fig. S4 of the supplementary materials.
Berthon B., Bergel A., Matei M, & Tanter M. (2023). Decoding behavior from global cerebrovascular activity using neural networks. Scientific Reports, 13, 3541.
Publication 2023
BLOOD Blood Vessel Body Regions Cerebral Blood Volume Cluttering Conditioning, Psychology Cortex, Cerebral Locomotion Maritally Unattached Movement Reading Frames Short Interspersed Nucleotide Elements Sleep Tissues Veins
5
Biomarkers for Vasospasm After aSAH
SAH was determined by computerized tomography (CT) scan. Patients were clinically assessed and/or, if possible, daily screened by transcranial Doppler ultrasound (TCD) for vasospasm. On suspicion, CT angiography (CT-A) and CT perfusion (CT-P) were performed to confirm CVS. Vasospasm was defined as a mean transit time (MTT) of over 5 s in CT-P or as reduced vessel diameter by more than 50% in digital subtraction angiography (DSA). MTT was computed from the CT-P as the ratio of cerebral blood volume (CBV) and cerebral blood flow (CBF), which, in turn, were computed by deconvoluting the tracer response signal from the arterial input function (AIF). The deconvolution was performed using singular value decomposition (SVD) (STROKETOOL-CT, Digital Image Solutions, Frechen, Germany). The decision whether to rely on DSA or CT-P was based on clinical considerations. DSA allows for direct confirmation of CVS, while in CT-P only its effects are measurable. On the other hand, CT-P visualizes the brain cross-section, while DSA only provides a flattened projection. Patient outcome at discharge was quantified using the Glasgow outcome scale (GOS) and modified Rankin scale (mRS).
Besides the demographic and the clinical data, serum concentrations of 10 potential biomarkers were quantified as described elsewhere [18 (link)–23 (link)]. These included damage-associated molecular patterns (DAMPs) (HMGB1, mitochondrial DNA gene fragments such as cytochrome B (Cyt-B), D-loop, and cytochrome c oxidase subunit-1 (Cox-1)), pro- and anti-inflammatory cytokines (IL-6, IL-17, IL-23, IL-10, CCL5), and leukocytes. Blood samples collected within 24 h after aSAH and processed using a previously described protocol [23 (link)] were used for this study. Also, the information whether and on which day after aSAH the patient developed CVS was recorded.
Fischer I., Chaudhry S.R., Hänggi D, & Muhammad S. (2023). Clustering of serum biomarkers involved in post-aneurysmal subarachnoid hemorrhage (aSAH) complications. Neurosurgical Review, 46(1), 63.
Publication 2023
Alarmins Angiography, Digital Subtraction Anti-Inflammatory Agents Arteries BLOOD Blood Vessel Brain CCL5 protein, human Cerebral Blood Volume Cerebrovascular Circulation Computed Tomography Angiography Cytochrome c1 Cytochromes b Cytokine DNA, Mitochondrial Genes Genes, Mitochondrial HMGB1 Protein IL10 protein, human IL17A protein, human Leukocytes Maritally Unattached Oxidases Patient Discharge Patients Perfusion Protein Subunits PTGS1 protein, human Radionuclide Imaging X-Ray Computed Tomography

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More about "Cerebral Blood Volume"

Cerebral Perfusion, Brain Oxygenation, Neuroimaging Techniques, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Stroke, Traumatic Brain Injury, Neurodegenerative Diseases, MATLAB, SOMATOM Definition Flash, Siemens Multimodality Workplace Workstation, OsiriX v.8.0.2, Syngo.via, Omniscan, 6002 Stimulator, Syngo Neuro Perfusion CT, ParaVision 6.0.1, IntelliSpace Portal system.
Cerebral blood volume (CBV) is a critical parameter that reflects the balance between blood supply and metabolic demand within the brain.
Accurate assessment of CBV can provide valuable insights into various neurological conditions, such as stroke, traumatic brain injury, and neurodegenerative diseases.
Advances in neuroimaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), have enabled non-invasive measurement of CBV, allowing researchers and clinicians to monitor changes in cerebral perfusion and oxygenation.
The dynamics of CBV and its relationship to brain health is an active area of research, with the potential to inform the development of new diagnostic and therapeutic strategies.
Leveraging cutting-edge technologies like MATLAB, SOMATOM Definition Flash, Siemens Multimodality Workplace Workstation, OsiriX v.8.0.2, Syngo.via, Omniscan, 6002 Stimulator, Syngo Neuro Perfusion CT, ParaVision 6.0.1, and IntelliSpace Portal system, researchers can optimize cerebral blood volume studies and uncover new insights that can lead to improved patient outcomes.
By understaning the power of cerebral blood volume analysis, clinicians and researchers can make more informed decisions, enhance diagnostic accuracy, and develop more effective treatments for a wide range of neurological conditions.
The future of cerebral blood volume research is bright, with the potential to transform how we approach brain health and neurological care.