Effect of a New Model-Based Reconstruction Algorithm for Evaluating Early Peripheral Lung Cancer With Submillisievert Chest Computed Tomography

Objective The aim of this study was to compare a new model-based iterative reconstruction algorithm with either spatial and density resolution balance (MBIRSTND) or spatial resolution preference (MBIRRP20) with the adaptive statistical iterative reconstruction (ASIR) in evaluating early small peripheral lung cancer (SPLC) with submillisievert chest computed tomography (CT). Methods Low-contrast and spatial resolutions were assessed in a phantom and with 30 pathologically confirmed SPLC patients. Images were reconstructed using 40% ASIR, MBIRSTND, and MBIRRP20. Computed tomography value and image noise were measured by placing the regions of interest on back muscle and subcutaneous fat at 3 levels. Two radiologists used a 4-point scale (1, worst, and 4, best) to rate subjective image quality in 3 aspects: image noise, nodule imaging signs, and nodule internal clarity. Results The phantom study revealed an improved detectability of low-contrast targets and small objects for MBIRSTND and MBIRRP20 compared with ASIR. The effective dose for patient scans was 0.88 ± 0.83 mSv. There was no significant difference in CT value between the 3 reconstructions (P> 0.05), but MBIRSTND and MBIRRP20 significantly reduced image noise compared with ASIR (P
Source: Journal of Computer Assisted Tomography - Category: Radiology Tags: Cardiothoracic and Breast Imaging Source Type: research