Computer Vision News - July 2020

3 Summary AI in Cardiac CT Angiogr phy 1 Methods – Different approaches are addressing the above challenges. One uses an FCNN trained on 3D CCTA data patches to predict routine dose images from low dose CCTA data patches. Simulated and real low dose scans were associated with a 90% dose reduction. An additional 2D CNN was utilized for improving denoising. Other methods use a GAN to denoise low-dose CCTA images and reconstruct low-dose images into routine-dose CCTA images, or use an SSDA-based CNN to reconstruct and denoise low- dose CCTA images. Results – Several Convolutional Neural Networks are effective at predicting and reconstructing low dose CCTA images to emulate the image quality of routine dose CCTA images. These results have been validated, showing that using fast and high-quality DL algorithmic denoisingmay contribute to radiation dose reduction of 90%, thereby improving patient safety while enhancing image quality. RSIP Vision is a first mover in deep learning for computed tomography, at the heart of AI for cardiac care. Click here for more information about our computer vision solutions for cardiology.