Image quality improvements using adaptive statistical iterative reconstruction for evaluating chronic myocardial infarction using iodine density images with spectral CT

Single-source dual-energy CT (ssDECT) allows the reconstruction of iodine density images (IDIs) from projection based computing. We hypothesized that adding adaptive statistical iterative reconstruction (ASiR) could improve image quality. The aim of our study was to evaluate the effect and determine the optimal blend percentages of ASiR for IDI of myocardial late iodine enhancement (LIE) in the evaluation of chronic myocardial infarction using ssDECT. A total of 28 patients underwent cardiac LIE using a ssDECT scanner. IDIs between 0 and 100% of ASiR contributions in 10% increments were reconstructed. The signal-to-noise ratio (SNR) of remote myocardia and the contrast-to-noise ratio (CNR) of infarcted myocardia were measured. Transmural extent of infarction was graded using a 5-point scale. The SNR, CNR, and transmural extent were assessed for each ASiR contribution ratio. The transmural extents were compared with MRI as a reference standard. Compared to 0% ASiR, the use of 20–100% ASiR resulted in a reduction of image noise (p < 0.01) without significant differences in the signal. Compared with 0% ASiR images, reconstruction with 100% ASiR image showed the highest improvement in SNR (229%; p < 0.001) and CNR (199%; p < 0.001). ASiR above 80% showed the highest ratio (73.7%) of accurate transmural extent classification. In conclusion, ASiR intensity of 80–100% in IDIs can improve image quality without changes in signal and maximizes the accuracy of transmural extent in infarcted myocardium.