Almost optimal adaptive LQ control: observed state case

In this paper we propose an almost optimal indirect adaptive controller for input/state dynamical systems. The control part of the adaptive scheme is based on a modified LQ control law: by adding a time varying gain to the certainty equivalent control law we avoid the conflict between identification and contro

An analysis of the Bayesian track labelling problem

In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called ``mixed labelling'' phenomenon that has been observed in MTT algorithms. In this memorandum, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the memorandum, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms

A theoretical analysis of Bayes-optimal multi-target tracking and labelling

In multi-target tracking (MTT), we are often interested not only in finding the position of the multiple objects, but also allowing individual objects to be uniquely identified with the passage of time, by placing a label on each track. While there are many MTT algorithms that produce uniquely identified tracks as output, most of them make use of certain heuristics and/or unrealistic assumptions that makes the global result suboptimal of Bayesian sense. An innovative way of performing MTT is the so-called joint multi-target tracking, where the raw output of the algorithm, rather than being already the collection of output tracks, is a multi-target density calculated by approximating the Bayesian recursion that considers the entire system to have a single multidimensional state. The raw output, i.e. the calculated multi-target density, is thereafter processed to obtain output tracks to be displayed to the operator. This elegant approach, at least in theory, would allow us to precisely represent multi-target statistics. However, most joint MTT methods in the literature handle the problem of track labelling in an ad-hoc, i.e. non-Bayesian manner. A number of methods, however, have suggested that the multi-target density, calculated using the Bayesian recursion, should contain information not only about the location of the individual objects but also their identities. This approach, that we refer as joint MTTL (joint multi-target tracking and labelling), looks intuitively advantageous. It would allow us, at least in theory, to obtain an output consisting of labelled tracks that is optimal in Bayesian sense. Moreover, it would allow us to have statistical information about the assigned labels; for instance, we would know what is the probability that track swap may have occurred after some approximation of targets (or, in simpler words, we would know how much we can believe that a target is what the display says that it is). However, the methods proposed in the still emerging joint MTTL literature do not address some problems that may considerably reduce the usefulness of the approach. These problems include: track coalescence after targets move closely to each other, gradual loss of ambiguity information when particle filters or multiple hypotheses approaches are used, and dealing with unknown/varying number of targets. As we are going to see, each of the previously proposed methods handles only a subset of these problems. Moreover, while obtaining a Bayes-optimal output of labelled tracks is one of the main motivations for joint MTTL, how such output should be obtained is a matter of debate. This work will tackle the joint MTTL problem together with a companion memorandum. In this work, we look at the problem from a theoretical perspective, i.e. we aim to provide an accurate and algorithm-independent picture of the aforementioned problems. An algorithm that actually handles these problems will be proposed in the companion memorandum. As one of the contributions of the memorandum, we clearly characterize the so-called "mixed labelling" phenomenon that leads to track coalescence and other problems, and we verify that, unlike implied in previous literature, it is a physical phenomenon inherent of the MTTL problem rather than specific to a particular approach. We also show how mixed labelling leads to nontrivial issues in practical implementations of joint MTTL. As another of the contributions of the memorandum, we propose a conceptual, algorithm-independent track extraction method for joint MTTL estimators, that gives an output with clear physical interpretation for the user

Particle filter approximations for general open loop and open loop feedback sensor management

Sensor management is a stochastic control problem where the control mechanism is directed at the generation of observations. Typically, sensor management attempts to optimize a certain statistic derived from the posterior distribution of the state, such as covariance or entropy. However, these statistics often depend on future measurements which are not available at the moment the control decision is taken, making it necessary to consider their expectation over the entire measurement space. Though the idea of computing such expectations using a particle filter is not new, so far it has been applied only to specific sensor management problems and criterions. In this memorandum, for a considerably broad class of problems, we explicitly show how particle filters can be used to approximate general sensor management criterions in the open loop and open loop feedback cases. As examples, we apply these approximations to selected sensor management criterions. As an additional contribution of this memorandum, we show that every performance metric can be used to define a corresponding estimate and a corresponding task-driven sensor management criterion, and both of them can be approximated using particle filters. This is used to propose an approximate sensor management scheme based on the OSPA metric for multi-target tracking, which is included among our examples

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

Posttreatment Ischemic Lesion Evolution Is Associated With Reduced Favorable Functional Outcome in Patients With Stroke

BACKGROUND AND PURPOSE: Ischemic lesion volume can increase even 24 hours after onset of an acute ischemic stroke. In this study, we investigated the association of lesion evolution with functional outcome and the influence of successful recanalization on this association. METHODS: We included patients from the MR CLEAN trial (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) who received good quality noncontrast CT images 24 hours and 1 week after stroke onset. The ischemic lesion delineations included infarct, edema, and hemorrhagic transformation. Lesion evolution was defined as the difference between the volumes measured on the 1-week and 24-hour noncontrast CTs. The association of lesion evolution with functional outcome was evaluated using unadjusted and adjusted logistic regression. Adjustments were made for baseline, clinical, and imaging parameters that were associated P<0.10) in univariate analysis with favorable functional outcome, defined as modified Rankin Scale score of ≤2. Interaction analysis was performed to evaluate the influence of successful recanalization, defined as modified Arterial Occlusion Lesion score of 3 points, on this association. RESULTS: Of the 226 patients who were included, 69 (31%) patients achieved the favorable functional outcome. Median lesion evolution was 22 (interquartile range, 10–45) mL. Lesion evolution was significantly inversely correlated with favourable functional outcome: unadjusted odds ratio, 0.76 (95% CI, 0.66–0.86; per 10 mL of lesion evolution; P<0.01) and adjusted odds ratio: 0.85 (95% CI, 0.72–0.97; per 10 mL of lesion evolution; P=0.03). There was no significant interaction of successful recanalization on the association of lesion evolution and favorable functional outcome (odds ratio, 1.01 [95% CI, 0.77–1.36]; P=0.94). CONCLUSIONS: In our population, subacute ischemic lesion evolution is associated with unfavorable functional outcome. This study suggests that even 24 hours after onset of stroke, deterioration of the brain continues, which has a negative effect on functional outcome. This finding may warrant additional treatment in the subacute phase

Risk factors of late lesion growth after acute ischemic stroke treatment

BackgroundEven days after treatment of acute ischemic stroke due to a large vessel occlusion, the infarct lesion continues to grow. This late, subacute growth is associated with unfavorable functional outcome. In this study, we aim to identify patient characteristics that are risk factors of late, subacute lesion growth. MethodsPatients from the MR CLEAN trial cohort with good quality 24 h and 1-week follow up non-contrast CT scans were included. Late Lesion growth was defined as the difference between the ischemic lesion volume assessed after 1-week and 24-h. To identify risk factors, patient characteristics associated with lesion growth (categorized in quartiles) in univariable ordinal analysis (p < 0.1) were included in a multivariable ordinal regression model. ResultsIn the 226 patients that were included, the median lesion growth was 22 (IQR 10-45) ml. In the multivariable model, lower collateral capacity [aOR: 0.62 (95% CI: 0.44-0.87); p = 0.01], longer time to treatment [aOR: 1.04 (1-1.08); p = 0.04], unsuccessful recanalization [aOR: 0.57 (95% CI: 0.34-0.97); p = 0.04], and larger midline shift [aOR: 1.18 (95% CI: 1.02-1.36); p = 0.02] were associated with late lesion growth. ConclusionLate, subacute, lesion growth occurring between 1 day and 1 week after ischemic stroke treatment is influenced by lower collateral capacity, longer time to treatment, unsuccessful recanalization, and larger midline shift. Notably, these risk factors are similar to the risk factors of acute lesion growth, suggesting that understanding and minimizing the effects of the predictors for late lesion growth could be beneficial to mitigate the effects of ischemia

Follow-up infarct volume as a mediator of endovascular treatment effect on functional outcome in ischaemic stroke

Objective: The putative mechanism for the favourable effect of endovascular treatment (EVT) on functional outcome after acute ischaemic stroke is preventing follow-up infarct volume (FIV) progression. We aimed to assess to what extent difference in FIV explains the effect of EVT on functional outcome in a randomised trial of EVT versus no EVT (MR CLEAN). Methods: FIV was assessed on non-contrast CT scan 5–7 days after stroke. Functional outcome was the score on the modified Rankin Scale at 3 months. We tested the causal pathway from intervention, via FIV to functional outcome with a mediation model, using linear and ordinal regression, adjusted for relevant baseline covariates, including stroke severity. Explained effect was assessed by taking the ratio of the log odds ratios of treatment with and without adjustment for FIV. Results: Of the 500 patients included in MR CLEAN, 60 died and four patients underwent hemicraniectomy before FIV was assessed, leaving 436 patients for analysis. Patients in the intervention group had better functional outcomes (adjusted common odds ratio (acOR) 2.30 (95% CI 1.62–3.26) than controls and smaller FIV (median 53 vs. 81 ml) (difference 28 ml; 95% CI 13–41). Smaller FIV was associated with better outcome (acOR per 10 ml 0.60, 95% CI 0.52–0.68). After adjustment for FIV the effect of intervention on functional outcome decreased but remained substantial (acOR 2.05, 95% CI 1.44–2.91). This implies that preventing FIV progression explains 14% (95% CI 0–34) of the beneficial effect of EVT on outcome. Conclusion: The effect of EVT on FIV explains only part of the treatment effect on functional outcome. Key Points: • Endovascular treatment in acute ischaemic stroke patients prevents progression of follow-up infarct volume on non-contrast CT at 5–7 days.• Follow-up infarct volume was related to functional outcome, but only explained a modest part of the effect of intervention on functional outcome.• A large proportion of treatment effect on functional outcome remains unexplained, suggesting FIV alone cannot be used as an early surrogate imaging marker of functional outcome

Time Since Stroke Onset, Quantitative Collateral Score, and Functional Outcome After Endovascular Treatment for Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES: In patients with ischemic stroke undergoing endovascular treatment (EVT), time to treatment and collateral status are important prognostic factors and may be correlated. We aimed to assess the relation between time to CT angiography (CTA) and a quantitatively determined collateral score and to assess whether the collateral score modified the relation between time to recanalization and functional outcome. METHODS: We analyzed data from patients with acute ischemic stroke included in the Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke Registry between 2014 and 2017, who had a carotid terminus or M1 occlusion and were treated with EVT within 6.5 hours of symptom onset. A quantitative collateral score (qCS) was determined from baseline CTA using a validated automated image analysis algorithm. We also determined a 4-point visual collateral score (vCS). Multivariable regression models were used to assess the relations between time to imaging and the qCS and between the time to recanalization and functional outcome (90-day modified Rankin Scale score). An interaction term (time to recanalization × qCS) was entered in the latter model to test whether the qCS modifies this relation. Sensitivity analyses were performed using the vCS. RESULTS: We analyzed 1,813 patients. The median time from symptom onset to CTA was 91 minutes (interquartile range [IQR] 65–150 minutes), and the median qCS was 49% (IQR 25%–78%). Longer time to CTA was not associated with the log-transformed qCS (adjusted β per 30 minutes, 0.002, 95% CI −0.006 to 0.011). Both a higher qCS (adjusted common odds ratio [acOR] per 10% increase: 1.06, 95% CI 1.03–1.09) and shorter time to recanalization (acOR per 30 minutes: 1.17, 95% CI 1.13–1.22) were independently associated with a shift toward better functional outcome. The qCS did not modify the relation between time to recanalization and functional outcome (p for interaction: 0.28). Results from sensitivity analyses using the vCS were similar. DISCUSSION: In the first 6.5 hours of ischemic stroke caused by carotid terminus or M1 occlusion, the collateral status is unaffected by time to imaging, and the benefit of a shorter time to recanalization is independent of baseline collateral status