Robust estimation of quantitative perfusion from multi-phase pseudo-continuous arterial spin labeling.

PURPOSE: Multi-phase PCASL has been proposed as a means to achieve accurate perfusion quantification that is robust to imperfect shim in the labeling plane. However, there exists a bias in the estimation process that is a function of noise in the data. In this work, this bias is characterized and then addressed in animal and human data. METHODS: The proposed algorithm to overcome bias uses the initial biased voxel-wise estimate of phase tracking error to cluster regions with different off-resonance phase shifts, from which a high-SNR estimate of regional phase offset is derived. Simulations were used to predict the bias expected at typical SNR. Multi-phase PCASL in 3 rat strains (n = 21) at 9.4 T was considered, along with 20 human subjects previously imaged using ASL at 3 T. The algorithm was extended to include estimation of arterial blood flow velocity. RESULTS: Based on simulations, a perfusion estimation bias of 6-8% was expected using 8-phase data at typical SNR. This bias was eliminated when a high-precision estimate of phase error was available. In the preclinical data, the bias-corrected measure of perfusion (107 ± 14 mL/100g/min) was lower than the standard analysis (116 ± 14 mL/100g/min), corresponding to a mean observed bias across strains of 8.0%. In the human data, bias correction resulted in a 15% decrease in the estimate of perfusion. CONCLUSIONS: Using a retrospective algorithmic approach, it was possible to exploit common information found in multiple voxels within a whole region of the brain, offering superior SNR and thus overcoming the bias in perfusion quantification from multi-phase PCASL.

ICA-based denoising for ASL perfusion imaging.

Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation. 72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data. The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data. The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.

Dual-calibrated fMRI measurement of absolute cerebral metabolic rate of oxygen consumption and effective oxygen diffusivity.

Dual-calibrated fMRI is a multi-parametric technique that allows for the quantification of the resting oxygen extraction fraction (OEF), the absolute rate of cerebral metabolic oxygen consumption (CMRO2), cerebral vascular reactivity (CVR) and baseline perfusion (CBF). It combines measurements of arterial spin labelling (ASL) and blood oxygenation level dependent (BOLD) signal changes during hypercapnic and hyperoxic gas challenges. Here we propose an extension to this methodology that permits the simultaneous quantification of the effective oxygen diffusivity of the capillary network (DC). The effective oxygen diffusivity has the scope to be an informative biomarker and useful adjunct to CMRO2, potentially providing a non-invasive metric of microvascular health, which is known to be disturbed in a range of neurological diseases. We demonstrate the new method in a cohort of healthy volunteers (n = 19) both at rest and during visual stimulation. The effective oxygen diffusivity was found to be highly correlated with CMRO2 during rest and activation, consistent with previous PET observations of a strong correlation between metabolic oxygen demand and effective diffusivity. The increase in effective diffusivity during functional activation was found to be consistent with previously reported increases in capillary blood volume, supporting the notion that measured oxygen diffusivity is sensitive to microvascular physiology.

Partial volume correction for quantitative CEST imaging of acute ischemic stroke.

PURPOSE: Contributions of cerebrospinal fluid (CSF) have not been previously taken into account in the quantification of APT CEST effects, and correction for the dilution of CEST effects by CSF may allow for more robust measurement of CEST signals. The objective of this study was to compare the robustness of a partial volume (PV) correction model against a standard (4-pool) multi-pool model as far as their ability to quantify CEST effects in healthy, normal, and pathological tissue. METHODS: MRI data from 12 patients presenting with ischemic stroke, and 6 healthy subjects, were retrospectively analyzed. CEST signals derived from a 4-pool model and a PV correction model were compared for repeatability and pathological tissue contrast. The effect of PV correction (PVC) was assessed within 3 ranges of tissue PV estimate (PVE): high PVE voxels, low PVE voxels, and the whole slice. RESULTS: In voxels with a high tissue PVE, PV correction did not make a significant difference to absolute APTR* . In low PVE voxels, the PVC model exhibited a significantly decreased ischemic core signal. The PVC measures exhibited higher repeatability between healthy subjects (4 pools: 3.4%, PVC: 2.4%) while maintaining a similar ischemic core CNR (0.7) to the 4-pool model. In whole slice analysis it was found that both models exhibited similar results. CONCLUSIONS: PV correction yielded a measure of APT effects that was more repeatable than standard 4-pool analysis while achieving a similar CNR in pathological tissue, suggesting that PV-corrected analysis was more robust at low values of tissue PVE.

Quantitative measurement of cerebral physiology using respiratory-calibrated MRI.

Functional magnetic resonance imaging typically measures signal increases arising from changes in the transverse relaxation rate over small regions of the brain and associates these with local changes in cerebral blood flow, blood volume and oxygen metabolism. Recent developments in pulse sequences and image analysis methods have improved the specificity of the measurements by focussing on changes in blood flow or changes in blood volume alone. However, FMRI is still unable to match the physiological information obtainable from positron emission tomography (PET), which is capable of quantitative measurements of blood flow and volume, and can indirectly measure resting metabolism. The disadvantages of PET are its cost, its availability, its poor spatial resolution and its use of ionising radiation. The MRI techniques introduced here address some of these limitations and provide physiological data comparable with PET measurements. We present an 18-minute MRI protocol that produces multi-slice whole-brain coverage and yields quantitative images of resting cerebral blood flow, cerebral blood volume, oxygen extraction fraction, CMRO(2), arterial arrival time and cerebrovascular reactivity of the human brain in the absence of any specific functional task. The technique uses a combined hyperoxia and hypercapnia paradigm with a modified arterial spin labelling sequence.

Quantitative CEST imaging of amide proton transfer in acute ischaemic stroke.

BACKGROUND: Amide proton transfer (APT) imaging may help identify the ischaemic penumbra in stroke patients, the classical definition of which is a region of tissue around the ischaemic core that is hypoperfused and metabolically stressed. Given the potential of APT imaging to complement existing imaging techniques to provide clinically-relevant information, there is a need to develop analysis techniques that deliver a robust and repeatable APT metric. The challenge to accurate quantification of an APT metric has been the heterogeneous in-vivo environment of human tissue, which exhibits several confounding magnetisation transfer effects including spectrally-asymmetric nuclear Overhauser effects (NOEs). The recent literature has introduced various model-free and model-based approaches to analysis that seek to overcome these limitations. OBJECTIVES: The objective of this work was to compare quantification techniques for CEST imaging that specifically separate APT and NOE effects for application in the clinical setting. Towards this end a methodological comparison of different CEST quantification techniques was undertaken in healthy subjects, and around clinical endpoints in a cohort of acute stroke patients. METHODS: MRI data from 12 patients presenting with ischaemic stroke were retrospectively analysed. Six APT quantification techniques, comprising model-based and model-free techniques, were compared for repeatability and ability for APT to distinguish pathological tissue in acute stroke. RESULTS: Robustness analysis of six quantification techniques indicated that the multi-pool model-based technique had the smallest contrast between grey and white matter (2%), whereas model-free techniques exhibited the highest contrast (>30%). Model-based techniques also exhibited the lowest spatial variability, of which 4-pool APTR∗ was by far the most uniform (10% coefficient of variation, CoV), followed by 3-pool analysis (20%). Four-pool analysis yielded the highest ischaemic core contrast-to-noise ratio (0.74). Four-pool modelling of APT effects was more repeatable (3.2% CoV) than 3-pool modelling (4.6% CoV), but this appears to come at the cost of reduced contrast between infarct growth tissue and normal tissue. CONCLUSION: The multi-pool measures performed best across the analyses of repeatability, spatial variability, contrast-to-noise ratio, and grey matter-white matter contrast, and might therefore be more suitable for use in clinical imaging of acute stroke. Addition of a fourth pool that separates NOEs and semisolid effects appeared to be more biophysically accurate and provided better separation of the APT signal compared to the 3-pool equivalent, but this improvement appeared be accompanied by reduced contrast between infarct growth tissue and normal tissue.

Feasibility of Flat Panel Detector CT in Perfusion Assessment of Brain Arteriovenous Malformations: Initial Clinical Experience.

The different results from flat panel detector CT in various pathologies have provoked some discussion. Our aim was to assess the role of flat panel detector CT in brain arteriovenous malformations, which has not yet been assessed. Five patients with brain arteriovenous malformations were studied with flat panel detector CT, DSC-MR imaging, and vessel-encoded pseudocontinuous arterial spin-labeling. In glomerular brain arteriovenous malformations, perfusion was highest next to the brain arteriovenous malformation with decreasing values with increasing distance from the lesion. An inverse tendency was observed in the proliferative brain arteriovenous malformation. Flat panel detector CT, originally thought to measure blood volume, correlated more closely with arterial spin-labeling-CBF and DSC-CBF than with DSC-CBV. We conclude that flat panel detector CT perfusion depends on the time point chosen for data collection, which is triggered too early in these patients (ie, when contrast agent appears in the superior sagittal sinus after rapid shunting through the brain arteriovenous malformation). This finding, in combination with high data variability, makes flat panel detector CT inappropriate for perfusion assessment in brain arteriovenous malformations.