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 [less than] 0.001), lower CBF and ATT variance (p [less than] 0.001), increased SNR (p [less than] 0.001), and improved repeatability (p [less than] 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

Imaging Biomarkers in Acute Ischemic Stroke Trials: A Systematic Review

Imaging biomarkers are increasingly used to provide a better understanding of the pathophysiology of acute ischemic stroke. However, this approach of routinely using imaging biomarkers to inform treatment decisions has yet to be translated into successful randomized trials. The aim of this study was to systematically review the use of imaging biomarkers in randomized controlled trials in patients with acute ischemic stroke, exploring the purposes for which the imaging biomarkers were used.We performed a systematic review of imaging biomarkers used in randomized controlled trials of acute ischemic stroke, in which a therapeutic intervention was trialed within 48 hours of symptom onset. Data bases searched included MEDLINE, EMBASE, strokecenter.org, and the Virtual International Stroke Trials Archive (1995-2014).Eighty-four studies met the criteria, of which 49 used imaging to select patients; 31, for subgroup analysis; and 49, as an outcome measure. Imaging biomarkers were broadly used for 8 purposes. There was marked heterogeneity in the definitions and uses of imaging biomarkers and significant publication bias among post hoc analyses.Imaging biomarkers offer the opportunity to refine the trial cohort by minimizing participant variation, to decrease sample size, and to personalize treatment approaches for those who stand to benefit most. However, within imaging modalities, there has been little consistency between stroke trials. Greater effort to prospectively use consistent imaging biomarkers should help improve the development of novel treatment strategies in acute stroke and improve comparison between studies

A hierarachical data structure representation for fusing multisensor information

A major problem with MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the data element, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Data-element fusion has problems with coregistration. Attempts to fuse information using the features of segmented data relies on a Presumed similarity between the segmentation characteristics of each data stream. Symbolic-level fusion requires too much advance processing (including object identification) to be useful. MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Data Structure (HDS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The HDS is an intermediate representation between the raw or smoothed data stream and symbolic interpretation of the data. it represents the structural organization of the data. Fused HDSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based region interpretation

Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study

IntroductionIn a drip-and-ship model for endovascular thrombectomy (EVT), early identification of large vessel occlusion (LVO) and timely referral to a comprehensive center (CSC) are crucial when patients are admitted to an acute stroke center (ASC). Several artificial intelligence (AI) decision-aid tools are increasingly being used to facilitate the rapid identification of LVO. This retrospective cohort study aimed to evaluate the impact of deploying e-Stroke AI decision support software in the hyperacute stroke pathway on process metrics and patient outcomes at an ASC in the United Kingdom.MethodsExcept for the deployment of e-Stroke on 01 March 2020, there were no significant changes made to the stroke pathway at the ASC. The data were obtained from a prospective stroke registry between 01 January 2019 and 31 March 2021. The outcomes were compared between the 14 months before and 12 months after the deployment of AI (pre-e-Stroke cohort vs. post-e-Stroke cohort) on 01 March 2020. Time window analyses were performed using Welch’s t-test. Cochran–Mantel–Haenszel test was used to compare changes in disability at 3 months assessed by modified Rankin Score (mRS) ordinal shift analysis, and Fisher’s exact test was used for dichotomised mRS analysis.ResultsIn the pre-e-Stroke cohort, 19 of 22 patients referred received EVT. In the post-e-Stroke cohort, 21 of the 25 patients referred were treated. The mean door-in-door-out (DIDO) and door-to-referral times in pre-e-Stroke vs. post-e-Stroke cohorts were 141 vs. 79 min (difference 62 min, 95% CI 96.9–26.8 min, p < 0.001) and 71 vs. 44 min (difference 27 min, 95% CI 47.4–5.4 min, p = 0.01), respectively. The adjusted odds ratio (age and NIHSS) for mRS ordinal shift analysis at 3 months was 3.14 (95% CI 0.99–10.51, p = 0.06) and the dichotomized mRS 0–2 at 3 months was 16% vs. 48% (p = 0.04) in the pre- vs. post-e-Stroke cohorts, respectively.ConclusionIn this single-center study in the United Kingdom, the DIDO time significantly decreased since the introduction of e-Stroke decision support software into an ASC hyperacute stroke pathway. The reduction in door-in to referral time indicates faster image interpretation and referral for EVT. There was an indication of an increased proportion of patients regaining independent function after EVT. However, this should be interpreted with caution given the small sample size. Larger, prospective studies and further systematic real-world evaluation are needed to demonstrate the widespread generalisability of these findings

Prospects for investigating brain oxygenation in acute stroke: experience with a non-contrast quantitative BOLD based approach

Metabolic markers of baseline brain oxygenation and tissue perfusion have an important role to play in the early identification of ischaemic tissue in acute stroke. Although well established MRI techniques exist for mapping brain perfusion, quantitative imaging of brain oxygenation is poorly served. Streamlined‐qBOLD (sqBOLD) is a recently developed technique for mapping oxygenation that is well suited to the challenge of investigating acute stroke. In this study a noninvasive serial imaging protocol was implemented, incorporating sqBOLD and arterial spin labelling to map blood oxygenation and perfusion, respectively. The utility of these parameters was investigated using imaging based definitions of tissue outcome (ischaemic core, infarct growth and contralateral tissue). Voxel wise analysis revealed significant differences between all tissue outcomes using pairwise comparisons for the transverse reversible relaxation rate (R 2′), deoxygenated blood volume (DBV) and deoxyghaemoglobin concentration ([dHb];

Quantifying T2 relaxation time changes within lesions defined by apparent diffusion coefficient in grey and white matter in acute stroke patients

The apparent diffusion coefficient (ADC) of cerebral water, as measured by diffusion MRI, rapidly decreases in ischaemia, highlighting a lesion in acute stroke patients. The MRI T 2 relaxation time changes in ischaemic brain such that T 2 in ADC lesions may be informative of the extent of tissue damage, potentially aiding in stratification for treatment. We have developed a novel user-unbiased method of determining the changes in T 2 in ADC lesions as a function of clinical symptom duration based on voxel-wise referencing to a contralateral brain volume. The spherical reference method calculates the most probable pre-ischaemic T 2 on a voxel-wise basis, making use of features of the contralateral hemisphere presumed to be largely unaffected. We studied whether T 2 changes in the two main cerebral tissue types, i.e. in grey matter (GM) and white matter (WM), would differ in stroke. Thirty-eight acute stroke patients were accrued within 9 h of symptom onset and scanned at 3 T for 3D T 1-weighted, multi b-value diffusion and multi-echo spin echo MRI for tissue type segmentation, quantitative ADC and absolute T 2 images, respectively. T 2 changes measured by the spherical reference method were 1.94  ±  0.61, 1.50  ±  0.52 and 1.40  ±  0.54 ms h−1 in the whole, GM, and WM lesions, respectively. Thus, T 2 time courses were comparable between GM and WM independent of brain tissue type involved. We demonstrate that T 2 changes in ADC-delineated lesions can be quantified in the clinical setting in a user unbiased manner and that T 2 change correlated with symptom onset time, opening the possibility of using the approach as a tool to assess severity of tissue damage in the clinical setting

Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients

Amide proton transfer (APT) imaging is a pH mapping method based on the chemical exchange saturation transfer phenomenon that has potential for penumbra identification following stroke. The majority of the literature thus far has focused on generating pH‐weighted contrast using magnetization transfer ratio asymmetry analysis instead of quantitative pH mapping. In this study, the widely used asymmetry analysis and a model‐based analysis were both assessed on APT data collected from healthy subjects (n = 2) and hyperacute stroke patients (n = 6, median imaging time after onset = 2 hours 59 minutes). It was found that the model‐based approach was able to quantify the APT effect with the lowest variation in grey and white matter (≤ 13.8 %) and the smallest average contrast between these two tissue types (3.48 %) in the healthy volunteers. The model‐based approach also performed quantitatively better than the other measures in the hyperacute stroke patient APT data, where the quantified APT effect in the infarct core was consistently lower than in the contralateral normal appearing tissue for all the patients recruited, with the group average of the quantified APT effect being 1.5 ± 0.3 % (infarct core) and 1.9 ± 0.4 % (contralateral). Based on the fitted parameters from the model‐based analysis and a previously published pH and amide proton exchange rate relationship, quantitative pH maps for hyperacute stroke patients were generated, for the first time, using APT imaging

Frailty and cerebrovascular disease: Concepts and clinical implications for stroke medicine.

Frailty is a distinctive health state in which the ability of older people to cope with acute stressors is compromised by an increased vulnerability brought by age-associated declines in physiological reserve and function across multiple organ systems. Although closely associated with age, multimorbidity, and disability, frailty is a discrete syndrome that is associated with poorer outcomes across a range of medical conditions. However, its role in cerebrovascular disease and stroke has received limited attention. The estimated rise in the prevalence of frailty associated with changing demographics over the coming decades makes it an important issue for stroke practitioners, cerebrovascular research, clinical service provision, and stroke survivors alike. This review will consider the concept and models of frailty, how frailty is common in cerebrovascular disease, the impact of frailty on stroke risk factors, acute treatments, and rehabilitation, and considerations for future applications in both cerebrovascular clinical and research settings

A Comparison of T2 Relaxation-Based MRI Stroke Timing Methods in Hyperacute Ischemic Stroke Patients: A Pilot Study

Background: T2 relaxation-based magnetic resonance imaging (MRI) signals may provide onset time for acute ischemic strokes with an unknown onset. The ability of visual and quantitative MRI-based methods in a cohort of hyperacute ischemic stroke patients was studied.Methods: A total of 35 patients underwent 3T (3 Tesla) MRI (<9-hour symptom onset). Diffusion-weighted (DWI), apparent diffusion coefficient (ADC), T1-weighted (T1w), T2-weighted (T2w), and T2 relaxation time (T2) images were acquired. T2-weighted fluid attenuation inversion recovery (FLAIR) images were acquired for 17 of these patients. Image intensity ratios of the average intensities in ischemic and non-ischemic reference regions were calculated for ADC, DWI, T2w, T2 relaxation, and FLAIR images, and optimal image intensity ratio cut-offs were determined. DWI and FLAIR images were assessed visually for DWI/FLAIR mismatch.Results: The T2 relaxation time image intensity ratio was the only parameter with significant correlation with stroke duration (r = 0.49, P = .003), an area under the receiver operating characteristic curve (AUC = 0.77, P < .0001), and an optimal cut-off (T2 ratio = 1.072) that accurately identified patients within the 4.5-hour thrombolysis treatment window with sensitivity of 0.74 and specificity of 0.74. In the patients with the additional FLAIR, areas under the precision-recall-gain curve (AUPRG) and F1 scores showed that the T2 relaxation time ratio (AUPRG = 0.60, F1 = 0.73) performed considerably better than the FLAIR ratio (AUPRG = 0.39, F1 = 0.57) and the visual DWI/FLAIR mismatch (F1 = 0.25).Conclusions: Quantitative T2 relaxation time is the preferred MRI parameter in the assessment of patients with unknown onset for treatment stratification