Choi, Kihwan 2024-01-12T03:44:02Z 2024-01-12T03:44:02Z 2022-04-30 2021-11 1557-170X https://pubs.kist.re.kr/handle/201004/77298 We consider the problem of denoising low-dose x-ray projections for cone-beam CT, where x-ray measurements are typically modeled as signal corrupted by Poisson noise. Since each projection view is a 2D image, we regard the low-dose projection views as examples to train a convolutional neural network. For self-supervised training without ground truth, we partially blind noisy projections and train the denoising model to recover the blind spots of projection views. From the projection views denoised by the learned model, we can reconstruct a high-quality 3D volume with a reconstruction algorithm such as the standard filtered backprojection. Through a series of phantom experiments, our self-supervised denoising approach simultaneously reduces noise level and restores structural information in cone-beam CT images. English IEEE Self-supervised Projection Denoising for Low-Dose Cone-Beam CT Conference 10.1109/EMBC46164.2021.9629859 1 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), pp.3459 - 3462 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) 3459 3462 US ELECTR NETWORK 2021-11-01 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) 000760910503094 2-s2.0-85122545627