Exploring the diagnostic performance of low-dose CT for colorectal cancer assessment: feasibility of artificial intelligence iterative reconstruction

Background: Contrast-enhanced long-range abdominal computed tomography (CT) plays a crucial role in the diagnosis and staging of colorectal cancer (CRC) yet may involve excessive radiation exposure, especially for patients requiring multiple examinations. This raises the need to reduce radiation dose while maintaining image quality. Given the potential of a novel artificial intelligence iterative reconstruction (AIIR) algorithm to improve image quality in low-dose (LD) CT, this study aimed to investigate the feasibility of applying AIIR to LD abdominal CT for CRC diagnosis. Methods: In this prospective study, 203 patients with pathology-confirmed CRC underwent abdominal CT, including a routine-dose (RD) scan (120 kVp, 200 mAs) followed by a LD scan (120 kVp, 20 mAs) at the portal venous phase (PVP). RD images were reconstructed with hybrid iterative reconstruction (HIR, RD-HIR) and AIIR (RD-AIIR), whereas LD images were reconstructed with AIIR (LD-AIIR). Diagnostic performance for assessing visceral peritoneal invasion (VPI) and regional lymph node metastasis (RLNM) was characterized using receiver operating characteristic (ROC) analysis. Qualitative image quality was rated using a five-point scale, and tumor contrast-to-noise ratio (CNR) was measured. Results: . 68.47%, respectively; P