RESUMO
A modified distorted Born iterative method (DBIM), which includes clustering of reconstructed electrical properties (EPs) after certain iterations, is presented for brain imaging aiming at stroke detection and classification. For this approach to work, a rough estimation of number of different materials (or bio-tissues) in the imaged domain and their corresponding rough dielectric properties (permittivity and conductivity) are needed as a prior information. The proposed adaptive clustering DBIM (AC-DBIM) is compared with three conventional methods (DBIM, multiplicative regularized contrast source inversion (MR-CSI), and CSI for shape and location reconstruction (SL-CSI)) in two-dimensional scenario on a head phantom and numerical head model with different strokes. Three-dimensional simulations are also conducted to indicate the suitability of AC-DBIM in real-life brain imaging. Lastly, the proposed algorithm is assessed using a clinical electromagnetic head scanner developed on phantoms. The simulation and experimental results show superiority of AC-DBIM compared to conventional methods. AC-DBIM achieves significant improvement in the size and shape reconstruction and reduction in errors and standard deviation of the reconstructed εr and σ at clinical scenarios compared with conventional DBIM.
Assuntos
Micro-Ondas , Acidente Vascular Cerebral , Encéfalo/diagnóstico por imagem , Análise por Conglomerados , Humanos , Acidente Vascular Cerebral/diagnóstico por imagem , Tomografia/métodosRESUMO
Introduction: Electromagnetic imaging is an emerging technology which promises to provide a mobile, and rapid neuroimaging modality for pre-hospital and bedside evaluation of stroke patients based on the dielectric properties of the tissue. It is now possible due to technological advancements in materials, antennae design and manufacture, rapid portable computing power and network analyses and development of processing algorithms for image reconstruction. The purpose of this report is to introduce images from a novel, portable electromagnetic scanner being trialed for bedside and mobile imaging of ischaemic and haemorrhagic stroke. Methods: A prospective convenience study enrolled patients (January 2020 to August 2020) with known stroke to have brain electromagnetic imaging, in addition to usual imaging and medical care. The images are obtained by processing signals from encircling transceiver antennae which emit and detect low energy signals in the microwave frequency spectrum between 0.5 and 2.0 GHz. The purpose of the study was to refine the imaging algorithms. Results: Examples are presented of haemorrhagic and ischaemic stroke and comparison is made with CT, perfusion and MRI T2 FAIR sequence images. Conclusion: Due to speed of imaging, size and mobility of the device and negligible environmental risks, development of electromagnetic scanning scanner provides a promising additional modality for mobile and bedside neuroimaging.
RESUMO
A wideband wearable electromagnetic (EM) head imaging system for brain stroke detection is presented. The proposed system aims at overcoming the challenges of size, rigidity, and complex structures of existing systems. The proposed system is built into a light-weight and compact imaging platform, which integrates a 16-element antenna array into a highly flexible custom-made wearable cap made of a cost-effective and robust room-temperature-vulcanizing (RTV) silicone. The system mitigates the mismatch between the skin and antenna array by introducing a flexible high-permittivity matching layer. The utilized compact antenna demonstrates wideband operational frequency over 0.6-2.5 GHz with a low signal distortion, safe values of SAR, and unidirectional radiations. The system is experimentally validated on realistic head phantoms. The polar sensitivity encoding (PSE) image processing algorithm is utilized to generate 2D images of different testing scenarios. The obtained images of a stroke-like target inside the head phantoms demonstrate the merits and feasibility of the system for preclinical trials.