Image reconstruction algorithms play a critical role in defining the quality and integrity of medical imaging using computed tomography. Since the advent of CT, image reconstruction has largely been performed by filtered back projection (FBP). This reconstruction technique has served CT well particularly at a time when there were significant limitations in computer processing capabilities. Iterative image reconstruction algorithms were, in fact available and were used to generate images with the very first commercial clinical computed tomographic (CT) scanner. This technique did not see significant adoption in clinical CT use owing to the ease of implementation and the faster image reconstruction of filtered back projection. Over the past decade, the need for finer resolution, greater volume coverage, faster scan times and the desire to lower radiation dose at the same time have pushed the performance of FBP reconstruction to its limits. Recently, there has been a re-introduction of iterative reconstruction for CT imaging with recently published studies in other organ systems showing that iterative reconstructions can produce higher-resolution images with greater robustness for the reduction of various imaging artifacts. There has been subsequent early adoption and experience with iterative reconstruction in coronary CT angiography (CCTA). We herein review the various iterative reconstruction platforms released for use for CCTA and the initial experiences implementing and integrating these reconstruction algorithms in clinical practice.