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Tractography Methods in Preoperative Neurosurgical Planning

Koryciński Mateusz, Ciecierski Konrad A.

Journal of Telecommunications and Information Technology

2021

nr 3

78--85

Knowledge of the location of nerve tracts during the surgical preoperative planning stage and during the surgery itself may help neurosurgeons limit the risk of causing neurological deficits affecting the patient’s essential abilities. Development of MRI techniques has helped profoundly with in vivo visualization of the brain’s anatomy, enabling to obtain images within minutes. Different methodologies are relied upon to identify anatomical or functional details and to determine the movement of water molecules, thus allowing to track nerve fibers. However, precise determination of their location continues to be a labor-intensive task that requires the participation of highly-trained medical experts. With the development of computational methods, machine learning and artificial intelligence, many approaches have been proposed to automate and streamline that process, consequently facilitating image-based diagnostics. This paper reviews these methods focusing on their potential use in neurosurgery for better planning and intraoperative navigation.

Mathematical methods of signal analysis applied in medical diagnostic

Ciecierski Konrad A.

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 3

449--462

Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.