Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible

Here, we present two different brain diagnostic devices based on microwave technology and the associated two first proof-of-principle measurements that show that the systems can differentiate hemorrhagic from ischemic stroke in acute stroke patients, as well as differentiate hemorrhagic patients from healthy volunteers. The system was based on microwave scattering measurements with an antenna system worn on the head. Measurement data were analyzed with a machine-learning algorithm that is based on training using data from patients with a known condition. Computer tomography images were used as reference. The detection methodology was evaluated with the leave-one-out validation method combined with a Monte Carlo-based bootstrap step. The clinical motivation for this project is that ischemic stroke patients may receive acute thrombolytic treatment at hospitals, dramatically reducing or abolishing symptoms. A microwave system is suitable for prehospital use, and therefore has the potential to allow significantly earlier diagnosis and treatment than today

Acute Stroke Care: Strategies For Improving Diagnostics

Stroke is one of the leading causes of death and disability, with a high incidence of over 11 million cases annually worldwide. Costs of treatment and rehabilitation, loss of work, and the hardships resulting from stroke are a major burden both at the individual and at the societal level. Importantly, stroke therapies need to be initiated early for them to be effective. Thrombolytic therapy and mechanical thrombectomy are early treatment options of ischemic stroke. In hemorrhagic stroke, optimization of hemodynamic and hemostatic parameters is central, and surgery is considered in a subset of patients. Efficient treatment of stroke requires early and precise recognition of stroke at all stages of the treatment chain. This includes identification of patients with suspected acute stroke by emergency medical dispatchers and emergency medical services staff, and precise admission diagnostics by the receiving on-call stroke team. Success requires grasping the complexity of stroke symptoms that depend on the brain areas affected, and the plethora of medical conditions that can mimic stroke. The Helsinki Ultra-acute Stroke Biomarker Study includes a cohort of 1015 patients transported to hospital due to suspected acute stroke, as candidates for revascularization therapies. Based on this cohort, this thesis work has explored new avenues to improve early stroke diagnostics in all stages of the treatment chain. In a detailed investigation into the identification of stroke by emergency medical dispatchers, we analyzed emergency phone calls with missed stroke identification. We also combined data on dispatch and EMS and hospital records to identify causes for missing stroke during emergency calls. Most importantly, we found that a patient’s fall at onset and patient confusion were strongly associated with missed identification. Regarding the Face Arm Speech Test (FAST), the most likely symptom to be misidentified was acute speech disturbance. Using prehospital blood sampling of stroke patients, and ultrasensitive measurement, we investigated the early dynamics of the plasma biomarkers glial fibrillary acidic protein (GFAP) and total tau. Utilizing serial sampling, we demonstrate for the first time that monitoring the early release rate of GFAP can improve the diagnostic performance of this biomarker for early differentiation between ischemic and hemorrhagic stroke. In our analysis of early GFAP levels, we were able to differentiate with high accuracy two-thirds of all patients with acute cerebral ischemia from those with hemorrhagic stroke, supporting further investigation of this biomarker as a promising point-of-care tool for prehospital stroke diagnostics. We performed a detailed review of the admission diagnostics of our cohort of 1015 patients to explore causes and predictors of admission misdiagnosis. We then investigated the consequences of misdiagnosis on outcomes. We demonstrate in this large cohort that the highly optimized and rapid admission evaluation in our hospital district (door-to-needle times below 20 minutes) did not compromise the accuracy and safety of admission evaluation. In addition, we discovered targets for improving future diagnostics. Finally, our detailed neuropathological investigation of a case of cerebral amyloid angiopathy (CAA) -related hemorrhage after stroke thrombolysis provided unique tissue-level evidence for this common vasculopathy as a notable risk factor for intracranial hemorrhagic complications in the setting of stroke. These findings support research to improve the diagnostics of CAA, and the prediction of hemorrhagic complications associated with stroke thrombolysis. In conclusion, these proposed targets and strategies will aid in the future improvement and development of this highly important field of diagnostics. Our proof-of-concept discoveries on early GFAP kinetics help guide further study into this diagnostic approach just as highly sensitive point-of-care GFAP measurement instruments are becoming available. Finally, our results support the safety of worldwide efforts to optimize emergency department door-to- needle times when care is taken to ensure sufficient expertise is in place, highlighting the role of the on-call vascular neurologist as a central diagnostic asset.Aivohalvaus on yksi yleisimpiä kuolinsyitä ja pitkäkestoisen työkyvyttömyyden aiheuttajia. Aivohalvauksen aiheuttamat hoito- ja kuntoutuskustannukset, työkyvyn menetys ja arkielämän vaikeudet ovat mittava taakka sekä yksilön, läheisten että yhteiskunnan tasoilla. Tehokkaiden hoitojen vaatima nopeus edellyttää aivohalvauksen varhaista ja tarkkaa tunnistamista hoitoketjun kaikilla askelmilla. Tässä väitöskirjatyössä etsittiin uusia keinoja aivohalvauksen varhaisdiagnostiikan kehittämiseksi hätäkeskuksessa, ensihoidossa ja vastaanottavan sairaalan HYKS:n päivystyspoliklinikalla. Yksityiskohtainen analyysi aivohalvauksen tunnistamisesta hätäkeskuksessa osoitti, että potilaan kaatuminen ja sekavuus olivat puutteellisen tunnistamisen keskeisiä tekijöitä. Face Arm Speech Test (FAST) -seulontaoireista puhehäiriö oli todennäköisimmin väärin tunnistettu. Akuuttivaiheen verinäytteitä ja äärimmäisen herkkää määritysmenetelmää hyödyntäen tutkimme kahden verestä mitattavan merkkiaineen, aivojen tukikudoksen tähtisolujen säikeisen happaman proteiinin (GFAP) ja taun varhaista dynamiikkaa aivohalvauspotilailla. Osoitimme ensimmäistä kertaa, että GFAP:n varhaisen vapautumisnopeuden seurantaa sarjanäytteistä voidaan hyödyntää parantamaan tämän merkkiaineen erottelukykyä iskeemisen ja hemorragisen aivokudosvaurion varhaisdiagnostiikassa. Tulokset viittaavat siihen, että GFAP merkkiaine voisi olla jatkossa kehitettävissä ambulansseissa hyödynnettäväksi pikaverikokeeksi, joka auttaisi aivohalvauksen eri muotojen varhaisessa erottelussa. Päivystysdiagnostiikkaan keskittyvässä osatyössä osoitimme ensimmäistä kertaa suuressa aineistossa, että sairaanhoitopiirissämme vuosia optimoitu erittäin nopea vastaanottoarviointi (liuotushoidon mediaaniviive alle 20 minuuttia sisältäen pään kuvauksen) ei vaaranna aivohalvauspotilaiden diagnostiikan tarkkuutta ja hoidon turvallisuutta. Tässä väitöskirjatyössä esitetyt kehityskohteet ja menetelmät auttavat tämän erittäin tärkeän diagnostisen alan tulevassa kehitystyössä. Työssä kuvatut tulokset sisältävät uraauurtavia havaintoja verestä mitattavan GFAP merkkiaineen kinetiikan käytöstä aivohalvauksen varhaisdiagnostiikassa ja tukevat sairaalapäivystysarvion diagnostista tarkkuutta HYKS:n tunnetusti erittäin nopeassa liuotushoitoketjussa

A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients

Microwave technology offers the possibility for pre-hospital stroke detection as we have pre- viously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diag- nostics. A priori information can be obtained by segmenting the patient’s head tissues from mag- netic resonance images. Expert manual segmentation is presently the gold standard, but it is labo- rious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real da- ta from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accu- racy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accu- racy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intrace- rebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intra- cerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmenta- tion can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system

Combining transcranial ultrasound with intelligent communication methods to enhance the remote assessment and management of stroke patients : framework for a technology demonstrator

With over 150,000 strokes in the United Kingdom every year, and more than 1 million living survivors, stroke is the third most common cause of death and the leading cause of severe physical disability among adults. A major challenge in administering timely treatment is determining whether the stroke is due to vascular blockage (ischaemic) or haemorrhage. For patients with ischaemic stroke, thrombolysis (i.e. pharmacological 'clot-busting') can improve outcomes when delivered swiftly after onset, and current National Health Service Quality Improvement Scotland guidelines are for thrombolytic therapy to be provided to at least 80 per cent of eligible patients within 60 min of arrival at hospital. Thrombolysis in haemorrhagic stroke could severely compound the brain damage, so administration of thrombolytic therapy currently requires near-immediate care in a hospital, rapid consultation with a physician and access to imaging services (X-ray computed tomography or magnetic resonance imaging) and intensive care services. This is near impossible in remote and rural areas, and stroke mortality rates in Scotland are 50 per cent higher than in London. We here describe our current project developing a technology demonstrator with ultrasound imaging linked to an intelligent, multi-channel communication device - connecting to multiple 2G/3G/4G networks and/or satellites - in order to stream live ultrasound images, video and two-way audio streams to hospital-based specialists who can guide and advise ambulance clinicians regarding diagnosis. With portable ultrasound machines located in ambulances or general practices, use of such technology is not confined to stroke, although this is our current focus. Ultrasound assessment is useful in many other immediate care situations, suggesting potential wider applicability for this remote support system. Although our research programme is driven by rural need, the ideas are potentially applicable to urban areas where access to imaging and definitive treatment can be restricted by a range of operational factors

New technology and potential for telemedicine in battlefield brain injury diagnostics

Peer reviewedPublisher PD

Parallel preconditioners for high order discretizations arising from full system modeling for brain microwave imaging

This paper combines the use of high order finite element methods with parallel preconditioners of domain decomposition type for solving electromagnetic problems arising from brain microwave imaging. The numerical algorithms involved in such complex imaging systems are computationally expensive since they require solving the direct problem of Maxwell's equations several times. Moreover, wave propagation problems in the high frequency regime are challenging because a sufficiently high number of unknowns is required to accurately represent the solution. In order to use these algorithms in practice for brain stroke diagnosis, running time should be reasonable. The method presented in this paper, coupling high order finite elements and parallel preconditioners, makes it possible to reduce the overall computational cost and simulation time while maintaining accuracy

3-D printed UWB microwave bodyscope for biomedical measurements

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this letter, a three-dimensional (3-D) printed compact ultrawideband (UWB) extended gap ridge horn (EGRH) antenna designed to be used for biological measurements of the human body is described. The operational frequency covers the microwave band of interest from 0.5 to 3.0 GHz (for an S 11 under -7 dB). The 3-D printed EGRH antenna is dielectrically matched to the permittivity of the human body, and because of its compactness, it can be visualized as a general-purpose microwave probe among the RF biomedical community. The probe has proven its capability as a pass-through propagation sensor for different parts of the human body and as a sensor detecting a 1 cm diameter object placed inside an artificial head phantom.Peer ReviewedPostprint (author's final draft

Hemorrhagic brain stroke detection by using microwaves: Preliminary two-dimensional reconstructions

Preliminary numerical results concerning the application of a Gauss-Newton method for diagnostic purposes of hemorrhagic brain strokes are reported. Interrogating microwaves are used in a multistatic and multiview arrangement. The reported results concern a two-dimensional model under transverse magnetic illumination conditions

Electromagnetic biomedical imaging in Banach spaces: A numerical case study

This paper reports the results of the application of a microwave imaging method developed in Banach spaces to a model of human head in presence of a hemorrhagic brain stroke. The approach is based on the integral equations of the inverse scattering problem. A Gauss-Newton scheme is adopted as a solving procedure. Being developed in Banach spaces, the method turns out to be quite efficient in reducing the over-smoothing effects usually associated to Hilbert-space reconstructions. Numerical simulations are reported involving a realistic model of human head

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student\u27s t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity