Brain injury and trauma monitoring using advanced photonics

Projektinformationen

BitMap

ID Finanzhilfevereinbarung: 675332

DOI

10.3030/675332

Projekt abgeschlossen

EK-Unterschriftsdatum 12 August 2015

Startdatum 1 Januar 2016

Enddatum 31 Dezember 2019

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 3 835 764,36

EU-Beitrag

€ 3 835 764,36

Koordiniert durch

THE UNIVERSITY OF BIRMINGHAM
United Kingdom

Periodic Reporting for period 2 - BitMap (Brain injury and trauma monitoring using advanced photonics)

Berichtszeitraum: 2018-01-01 bis 2019-12-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

We are developing, testing and aiming to utilise safe, non-invasive devices for neuro-intensive care that will provide clinicians with valuable information about brain health when managing (1) critical patients such as those who have suffered a severe traumatic brain injury (TBI) and (2) neonates with a brain injury incurred during pregnancy and/or during birth (neonatal encephalopathy – NE).

The early management and avoidance of brain tissue hypoxia and metabolic disturbances are the most significant factors influencing the outcome of treatment of severe TBI and NE. This requires adequate resuscitation and management in the early phase but unfortunately, significant improvements in treatment have been hampered by lack of knowledge of the biochemical, cellular and molecular changes involved in the pathophysiology of brain injury.

The project is motivated by the clinical evidence that optical based devices can lead to significantly improved healthcare and reduced socio-economic impact. Specifically, we are targeting biomarkers (BM) of tissue hypoxia, ischemia and oxygen metabolism and aim to develop, investigate and validate devices that will measure the clinically relevant hemodynamic parameters.

The ultimate goal of this work is for the first time to provide a set of standardised measurable quantities against which patients can be evaluated using biophotonic devices. Our approach is to use advanced photonic techniques that have already been shown to non-invasively provide hemodynamic parameters, namely: (1) near-infrared spectroscopy (NIRS) (2) time-resolved near-infrared spectroscopy (TRS-NIRS) and (3) diffuse correlation spectroscopy (DCS).
We have brought together the leading optical imaging groups in Europe, major clinical centres of excellence and SMEs to form a multi-disciplinary consortium of scientists, engineers and clinicians who will work to ensure that (a) we will train the future scientific leaders (b) technology development is driven by clinical need and together will (c) facilitate the development of these into a set of smart probes that can be used routinely at the bedside.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

We are exploiting the combined expertise of the host, partners and network through collaborative multi-disciplinary approach that will develop a step-change in the treatment and monitoring of brain injury, and will train highly competitive and employable ESRs who will be able to push the boundaries of clinical care during the course of this project and beyond.
The goal of BitMap is therefore three-fold: a) we will train the future scientific leaders while (b) the technology development is driven by clinical need and together will (c) facilitate the development of these into a set of smart probes that can be used routinely at the bedside.
1. We have recruited ESRs who have undergone a detailed recruitment criteria and have a solid background experience in a relative field.
2. All ESRs have a training schedule in place while we have used training and secondments at partners sites to enhance their experience.
3. We have developed a set of tools that allows us to computationally model different human subjects, under different conditions, taking into account the complex heterogeneous nature of the human head.
4. We have developed a set of rigid registration tools that allows us to computationally model different subjects, under different conditions, taking into account the complex heterogeneous nature of the human head.
5. We have developed new methods for reconstructing Diffuse Optical Tomography (DOT) images that accurately reflect the size and shape of the injury, and from which accurate indices that are indicative of the extent of the injury can be computed.
6. The underlying theory of modelling diffuse correlation spectroscopy within NIRFAST toolbox have been established and tested using analytical models.
7. Good improvement in computational speeds have been achieved using highly parallelised computation tools using GPU. This is being utilised for both Time Resolved and Diffuse Correlation Spectroscopy.
8. A general scheme for performance assessment of different imaging systems has been agreed and led to specifications of both protocols and phantoms.
9. We have organised all different optical devices in categories, TRL levels, ergonomics and application scenarios and analyzed the status of the network. This was then concluded by brief suggestions for continued directions.
10. In case of preliminary source and detector prototypes, first realization of new technologies have been fully accomplished at the laboratory level.
11. Most systems have completed the validation and some lower TRL level systems are continuously being tested as they develop.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

"The main advantages of the proposed technology to be developed is the additional clinically relevant information that it provides (i.e. in addition to blood oxygenation, it measures cerebral blood flow (CBF), cerebral blood volume (CBV), oxygen extraction fraction (OEF) and metabolism (CMRO2)), improved accuracy, performance and usability, competitiveness against other available technologies (i.e. against other modalities for similar parameters) and the potential to be utilized in larger populations due to its non-invasive and safe nature (out-patient, paediatrics etc). Through this project, we will are aiming to introduce new capabilities to medical devices developed and produced in Europe.

The development of a new prototype and its initial clinical validation for future commercialization is the key aspect of this project. By working closely with our partners to understand, develop and validate the technology we will differentiate ourselves from the ""traditional"" research prototypes that do not make it into the clinic.

We believe we are looking into the future with the highest information content for absolute value measurements and the potential to separate intra- and extra-cerebral contributions. By combining Time Resolved Spectroscopy (TRS) with Diffuse Correlation Spectroscopy (DCS), we have introduces two new parameters, namely the cerebral blood flow (CBF) and cerebral metabolic rate of oxygen extraction (CMRO2), which may enable a better understanding of the physiological status of the brain.

We have built upon the experience gained in our EU funded projects, and through collaboration of partners and ESRs started to develop TRS into a viable instrument for bedside monitoring in neurocritical care. At the sub-system level, we have made key advances in opto-electronics for Time Resolved Spectroscopy.

We have made advances in Diffuse Correlation Spectroscopy technology at sub-systems level by introducing new opto-electronic components. These are now being integrated into a system where we expect improvements in SNR in stability and clinical usability and accuracy.

We have increased our ability in computational tools for modeling and data analysis by incorporating the tools and algorithms for diffuse correlation spectroscopy and tomography. We have already addressed two important aspects that will facilitate the translation to the clinic: (1) complete automation of image segmentation and model generation; (2) implementation on multi-core and GPU hardware for high-speed reconstruction.
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