1.開發的EIT技術含 signal processing system & machine learning system，可於7分鐘內判斷是缺血性中風或出血性中風，有利於後續處置，具創新性。
2.發展以非侵入式頭戴神經影像設備，即時偵測腦中風之狀況，可應用於救護車、醫院或診所，具臨床價值。
3.倍頻顯微術的關鍵技術，已取得美、中、台專利。

Typically, acute stroke patients need to be imaged in medical centers using MRA (magnetic resonance angiography) or CTA (computed tomography angiography) to be diagnosed accurately. MRA and CTA are usually not available in ambulances due to the scanner size and weight. On the other hand, it is possible to use portable devices in ambulances to image the stroke brains and provide diagnoses before acute ischemic stroke patients arrive at the medical centers. A portable device with electrical impedance tomography capability can inject small current levels (e.g., 140µA at 1KHz sinusoidal signal) into stroke patients' brains and simultaneously measure potential distribution based on an electroencephalogram (EEG) cap. Those measurements can be transmitted and processed in an online or cloud computing center. The online computing center or the cloud computing center can use the following method to process the measurements to create an image of the stroke patient's brain conductivity distribution. This would allow the size and location of the ischemic stroke target to be identified. This would allow the emergency room or stroke center physicians more time to consider injecting tissue plasminogen (tPA) (within 4.5 hours of acute ischemic stroke onset) and extracting blood clots within 6 to 24 hours of stroke onset with mechanical thrombectomy. The method introduced in this method used the finite element analysis, genetic algorithm and divide-and-conquer method to study and analyze the electrical conductivity profile of simulated stroke patients based on a 45 dB signal-to-noise ratio (SNR) synthesized measurement. Clinical measurement data obtained from stroke patients with electrical impedance tomography was reported to be approximately 45 dB SNR. Weighting factors in conjunction with the divide-and-conquer method and adaptive genetic algorithm were used. The adaptive genetic algorithm was applied with the weighting factors to identify the conductivity distribution inside the brain by minimizing the difference between the simulation and synthesized measured voltages under stroke conditions. The result shows a pretty good reconstruction of the electrical conductivity profile of the stroke head based on 45 dB SNR simulated measurement of stroke data. This algorithm can be added to the original algorithm to improve further the overall result of the stroke patient's images.