Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader’s confidence

• Published in: Acta radiologica open 2019-06, Vol.8 (6), p.2058460119856266-2058460119856266
• Main Authors: Grosser, Oliver S, Ruf, Juri, Kupitz, Dennis, Czuczwara, Damian, Loewenthal, David, Thormann, Markus, Furth, Christian, Ricke, Jens, Denecke, Timm, Pech, Maciej, Kreissl, Michael C, Amthauer, Holger
• Format: Article
• Language: English
• Subjects: Abdomen; Adaptive algorithms; Adaptive filters; Algorithms; Background noise; Computed tomography; Confidence; Diagnostic systems; Hybrid systems; Image processing; Image quality; Image reconstruction; Iterative methods; Medical imaging; Noise; Photon emission; Quality; Single photon emission computed tomography; Statistical analysis; Subjective assessment; Substructures; Tomography
• ISSN: 2058-4601; 2058-4601
• DOI: 10.1177/2058460119856266

Background Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT). Purpose To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment. Material and Methods The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures. Results The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003). Conclusion An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.