3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Abstract

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’s 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.

Development of a 3D Anthropomorphic Phantom Generator for Microwave Imaging Applications of the Head and Neck Region

“…A single-pole Cole-Cole model was used to evaluate the behavior of the dielectric properties of biological tissues over a frequency range from 0.1 to 10 GHz. More recently, Fhager et al [11], have added more realism to head phantoms in order to create a more realistic simulation environment. They based their research on an existing anatomical tissue model of a healthy subject from BrainWeb [12].…”

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

“…Timely diagnosis of ischemic and hemorrhagic stroke, while challenging for physicians, is invaluable for the patient. ML has been explored by researchers for stroke screening [ 42 ], detection of stroke and large vessel occlusion using CTA imaging [ 43 , 44 ], detection and subtyping of hemorrhagic stroke on CT scans [ 45 , 46 , 47 , 48 ], and to predict post-stroke mortality [ 49 , 50 ]. Researchers have also used ML to aid in the acute diagnosis of TIAs and differentiate them from their mimics [ 51 ].…”

Machine Learning Approaches for Automated Stroke Detection, Segmentation, and Classification in Microwave Brain Imaging Systems

“…Then, the hemorrhage stroke location is extracted from the reflected signals. As this point is identified, all of the values are set as [20][21][22][23]. All these focusing algorithms face performance degradation when being applied in brain situations.…”

“…Signal to Clutter Ratio (SCR), Signal to Mean Ratio (SMR), and localization error were selected as the comparison metric. DMAS was the only algorithm that significantly improved the image quality in terms of both SMR and SCR while keeping localization error within prescribed limits [23]. In 2018, these six algorithms were compared using actual clinical data [22].…”