Rationale and objectives: A new postprocessing algorithm named adaptive statistical iterative reconstruction (ASIR)-V has been recently introduced. The aim of this article was to analyze the impact of ASIR-V algorithm on signal, noise, and image quality of coronary computed tomography angiography. Materials and Methods: Fifty consecutive patients underwent clinically indicated coronary computed tomography angiography (Revolution CT; GE Healthcare, Milwaukee, WI). Images were reconstructed using filtered back projection and ASIR-V 0%, and a combination of filtered back projection and ASIR-V 20%–80% and ASIR-V 100%. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for left main coronary artery (LM), left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) and were compared between the different postprocessing algorithms used. Similarly a four-point Likert image quality score of coronary segments was graded for each dataset and compared. A cutoff value of P <.05 was considered statistically significant. Results: Compared to ASIR-V 0%, ASIR-V 100% demonstrated a significant reduction of image noise in all coronaries (P <.01). Compared to ASIR-V 0%, SNR was significantly higher with ASIR-V 60% in LM (P <.01), LAD (P <.05), LCX (P <.05), and RCA (P <.01). Compared to ASIR-V 0%, CNR for ASIR-V ≥60% was significantly improved in LM (P <.01), LAD (P <.05), and RCA (P <.01), whereas LCX demonstrated a significant improvement with ASIR-V ≥80%. ASIR-V 60% had significantly better Likert image quality scores compared to ASIR-V 0% in segment-, vessel-, and patient-based analyses (P <.01). Conclusions: Reconstruction with ASIR-V 60% provides the optimal balance between image noise, SNR, CNR, and image quality.
Impact of a New Adaptive Statistical Iterative Reconstruction (ASIR)-V Algorithm on Image Quality in Coronary Computed Tomography Angiography
Gatti M.;
2018-01-01
Abstract
Rationale and objectives: A new postprocessing algorithm named adaptive statistical iterative reconstruction (ASIR)-V has been recently introduced. The aim of this article was to analyze the impact of ASIR-V algorithm on signal, noise, and image quality of coronary computed tomography angiography. Materials and Methods: Fifty consecutive patients underwent clinically indicated coronary computed tomography angiography (Revolution CT; GE Healthcare, Milwaukee, WI). Images were reconstructed using filtered back projection and ASIR-V 0%, and a combination of filtered back projection and ASIR-V 20%–80% and ASIR-V 100%. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for left main coronary artery (LM), left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) and were compared between the different postprocessing algorithms used. Similarly a four-point Likert image quality score of coronary segments was graded for each dataset and compared. A cutoff value of P <.05 was considered statistically significant. Results: Compared to ASIR-V 0%, ASIR-V 100% demonstrated a significant reduction of image noise in all coronaries (P <.01). Compared to ASIR-V 0%, SNR was significantly higher with ASIR-V 60% in LM (P <.01), LAD (P <.05), LCX (P <.05), and RCA (P <.01). Compared to ASIR-V 0%, CNR for ASIR-V ≥60% was significantly improved in LM (P <.01), LAD (P <.05), and RCA (P <.01), whereas LCX demonstrated a significant improvement with ASIR-V ≥80%. ASIR-V 60% had significantly better Likert image quality scores compared to ASIR-V 0% in segment-, vessel-, and patient-based analyses (P <.01). Conclusions: Reconstruction with ASIR-V 60% provides the optimal balance between image noise, SNR, CNR, and image quality.
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