Approximately 1.7 million Americans sustain traumatic brain injuries (TBI) each year requiring clinical intervention and monitoring. The pathological response to TBI typically presents as increased fluid volume as the brain swells resulting in subsequent internal pressure changes. Intracranial compliance (ICC) defines the relationship between these volume and pressure changes and above specific compliance thresholds the brain undergoes irreversible damage. The American Association of Neurologic Surgeons recommends that TBI patients rendered comatose undergo continuous intracranial pressure (ICP) monitoring as a surrogate metric of compliance to assess injury progression. ICP provides the intensives with a measure of global pressure, but provides delayed and non-specific information regarding the spatial distribution of deteriorating compliance. Occasional CT scans are obtained to visualize the changing spatial distribution of tissues, however these are acquired intermittently (every few hours to every few days) because these patients are significantly instrumented and ionizing radiation from multiple scans increases the probability of developing cancer. Spatial ICC information collected continuously would enable clinicians to provide more timely and specific care to patients. We propose to image dynamic fluid (blood, cerebral spinal fluid) volume changes in the brain by means of real-time electrical impedance tomography (EIT). These fluids have significantly different electrical properties from other tissues in the cranium (white matter, gray matter) and provide the necessary imaging contrast. Typically, in EIT a number of standard ECG electrodes (~8-64) are adhered to the skin surface of the cranium and small (<1mA) currents are injected between specific electrode-pairs, while surface voltages are recorded at the remaining sites. A computational algorithm utilizes these surface measurements of current and voltage to construct an image of the internal electrical property distribution. The primary limitation to employing this modality for brain imaging has been the high impedance skull which limits current levels able to interrogate internal anatomy. We intend to overcome this by coupling an intracranial electrode (ICE) to an ICP sensor routinely positioned in a TBI patient's ventricle. This ICE will provide a sensing element within the skull and improve image sensitivity of deep brain structures. We hypothesize that pixel-based correlation of EIT-based fluid volume changes and ICP sensor-based pressure changes will for the first time enable real-time imaging of brain compliance. In this feasibility study, we aim to construct an EIT system specifically for this application and integrate an ICP and ICE sensor to the instrument. The system will be evaluated through a series of simulated brain phantom experiments and the clinical potential will be evaluated by monitoring porcine brains following induced TBI using a model we have previously developed. We expect that these animal studies will demonstrate this technology's ability to safely and accurately monitor brain injury progression. We foresee that, at the end of this study, we will be well-poised to translate this technology to a pre-clinical human trial.
Clinical management of patients with traumatic brain injury (TBI) includes continuous real-time monitoring of intracranial pressure (ICP) and intermittent (every few hours or days) CT imaging of the brain in order to assess injury progression and best determine treatment. Because ICP sensing provides only a global estimate of disease progression and since CT images are only obtained intermittently, the therapeutic window of opportunity is often missed.
We aim to construct a continuous real-time brain imaging modality based on the significantly different electrical properties of the various cranial tissues (i.e. skull, white and gray matter, blood) and evaluate the clinical potential of this technology through a series of animal studies.
| Khan, Shadab; Manwaring, Preston; Borsic, Andrea et al. (2015) FPGA-based voltage and current dual drive system for high frame rate electrical impedance tomography. IEEE Trans Med Imaging 34:888-901 |
| Khan, S; Borsic, A; Manwaring, Preston et al. (2013) FPGA Based High Speed Data Acquisition System for Electrical Impedance Tomography. J Phys Conf Ser 434:012081 |
| Manwaring, Preston K; Moodie, Karen L; Hartov, Alexander et al. (2013) Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma. Anesth Analg 117:866-75 |
