Impact of Motion Correction Algorithms on Image Quality in Children Undergoing Coronary Computed Tomography Angiography: A Comparison With Regular Monophasic and Multiphasic Acquisitions

Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT

The main purpose of pre-transcatheter aortic valve implantation (TAVI) cardiac computed tomography (CT) for patients with severe aortic stenosis is aortic annulus measurements. However, motion artifacts present a technical challenge because they can reduce the measurement accuracy of the aortic annulus. Therefore, we applied the recently developed second-generation whole-heart motion correction algorithm (SnapShot Freeze 2.0, SSF2) to pre-TAVI cardiac CT and investigated its clinical utility by stratified analysis of the patient's heart rate during scanning. We found that SSF2 reconstruction significantly reduced aortic annulus motion artifacts and improved the image quality and measurement accuracy compared to standard reconstruction, especially in patients with high heart rate or a 40% R-R interval (systolic phase). SSF2 may contribute to improving the measurement accuracy of the aortic annulus.

Motion artefact reduction in coronary CT angiography images with a deep learning method

Background

The aim of this study was to investigate the ability of a pixel-to-pixel generative adversarial network (GAN) to remove motion artefacts in coronary CT angiography (CCTA) images.

Methods

Ninety-seven patients who underwent single-cardiac-cycle multiphase CCTA were retrospectively included in the study, and raw CCTA images and SnapShot Freeze (SSF) CCTA images were acquired. The right coronary artery (RCA) was investigated because its motion artefacts are the most prominent among the artefacts of all coronary arteries. The acquired data were divided into a training dataset of 40 patients, a verification dataset of 30 patients and a test dataset of 27 patients. A pixel-to-pixel GAN was trained to generate improved CCTA images from the raw CCTA imaging data using SSF CCTA images as targets. The GAN’s ability to remove motion artefacts was evaluated by the structural similarity (SSIM), Dice similarity coefficient (DSC) and circularity index. Furthermore, the image quality was visually assessed by two radiologists.

Results

The circularity was significantly higher for the GAN-generated images than for the raw images of the RCA (0.82 ± 0.07 vs. 0.74 ± 0.11, p < 0.001), and there was no significant difference between the GAN-generated images and SSF images (0.82 ± 0.07 vs. 0.82 ± 0.06, p = 0.96). Furthermore, the GAN-generated images achieved the SSIM of 0.87 ± 0.06, significantly better than those of the raw images 0.83 ± 0.08 (p < 0.001). The results for the DSC showed that the overlap between the GAN-generated and SSF images was significantly higher than the overlap between the GAN-generated and raw images (0.84 ± 0.08 vs. 0.78 ± 0.11, p < 0.001). The motion artefact scores of the GAN-generated CCTA images of the pRCA and mRCA were significantly higher than those of the raw CCTA images (3 [4–3] vs 4 [5–4], p = 0.022; 3 [3–2] vs 5[5–4], p < 0.001).

Conclusions

A GAN can significantly reduce the motion artefacts in CCTA images of the middle segment of the RCA and has the potential to act as a new method to remove motion artefacts in coronary CCTA images.

A Simple Low-Cost Electrocardiogram Synchronizer

Electrocardiogram (ECG) synchronization is useful to avoid the effects of cardiac motion in medical measurements, and is widely used in standard medical imaging. A number of medical equipment include embedded commercial synchronizers. However, the use of independent synchronization modules is sometimes needed when several non-integrated instruments are used, or in the development of new medical instruments and procedures. We present a simple low-cost ECG synchronizer module based on an Arduino controller board that converts the ECG signal into a transistor-transistor-logic (TTL) one, allowing real-time medical measurements triggered at specific phases of the cardiac cycle. The device and conversion algorithm developed is optimized in vitro using synthetic and human ECG signals, and tested in vivo on three swine specimens. Error rates during the in vivo testing stage remain below the 2% of the cycles in all animals and critical false positives are less than 1%, which is sufficient for most applications. Possible algorithm updates are discussed if its performance needs to be improved.