Role of Iterative Reconstruction Algorithm for the Assessment of Myocardial Infarction with Dual Energy Computed Tomography

Abstract

Rationale and Objectives: Low monochromatic energy levels (40 keV) derived from delayed enhancement dual energy cardiac computed tomography (DE-DECT) allow the evaluation of myocardial infarcts (MI) among stable patients, although at the expense of high image noise. We explored whether the application of adaptive statistical iterative reconstruction (ASIR) to 40-keV DE-DECT (unavailable with previous software versions) might improve image quality and detection of MI in stable patients. Materials and Methods: We prospectively enrolled patients with a history of previous MI, and performed delayed-enhancement cardiac magnetic resonance (DE-CMR) and DE-DECT within the same week. DE-DECT images were reconstructed with 0% and 60% ASIR. Results: MI was identified in 18 (80%) patients with both DE-CMR and DE-DECT. On a per segment basis, we did not identify significant differences regarding the diagnostic performance of DE-DECT with and without ASIR [area under receiver operating characteristic curve 0.86 vs. 0.83, p = 0.10]. The application of ASIR improved the signal-to-noise ratio of DE-DECT with 0% ASIR compared to DE-DECT with 60% ASIR (6.07 ± 2.1 vs. 11.1 ± 4.5, p < 0.0001). However, qualitative assessment of MI image quality (3.35 ± 1.2, vs. 3.55 ± 1.1, p = 0.10) and diagnostic confidence (4.40 ± 0.9 vs. 4.60 ± 0.8, p = 0.10) were not significantly improved. Using DE-DECT with 60% ASIR, a threshold over 199 HU showed a sensitivity of 67% and a specificity of 92% for the detection of segments with MI. Conclusion: In this study, DE-DECT allowed accurate detection of MI among stable patients compared with DE-CMR, and the application of ASIR improved signal-to-noise ratio of DE-DECT, although the diagnostic performance showed only non-significant improvements.