Hallway Conversations in Physics

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Dual-Energy Computed Tomography to Photon Counting Computed Tomography: Emerging Technological Innovations

Computed tomography (CT) has seen remarkable developments in the past several decades, radically transforming the role of imaging in day-to-day clinical practice. Dual-energy CT (DECT), an exciting innovation introduced in the early part of this century, has widened the scope of CT, opening new opportunities due to its ability to provide superior tissue characterization. The introduction of photon-counting CT (PCCT) heralds a paradigm shift in CT scanner technology representing another significant milestone in CT innovation. PCCT offers several advantages over DECT, such as improved spectral resolution, enhanced tissue characterization, reduced image artifacts, and improved image quality.