Advancements in CT Imaging for Neuro-Radiological Diagnosis: A Review of Techniques and Challenges

Abstract

In neuro-radiological diagnosis, the imaging technology known as Computed Tomography (CT) has advanced significantly. Both traditional CT approaches and new techniques are encompassed in the advances. The fundamentals of CT imaging, such as X-ray attenuation, data collection, and image reconstruction (such as iterative reconstruction and Filtered Back Projection, or FBP), are crucial for its use as a tool. There are many types of CT scanners--axial, spiral, Multidetector CT (MDCT), and Cone Beam CT (CBCT)- each has different configurations and applications. The diagnosis of neurological disorders, such as stroke, traumatic brain injury, brain tumors, and congenital or degenerative diseases, makes extensive use of CT imaging. The recent advancements in CT technology (MDCT and now Dual-Energy CT (DECT) or Perfusion CT) have facilitated more accurate diagnoses and have provided a new methodology to determine functional status. The applications of artificial intelligence (AI) and machine learning (ML) in neuroimaging is gaining traction with several studies utilizing AI and ML to synthesize contrast-enhanced CT images, use multimodal neuro-imaging data to classify neurological disorders or optimize data and image reconstruction ability.