Improving image quality of sparse-view lung tumor CT images with U-Net.

Annika RiesTina DorostiJohannes ThalhammerDaniel SasseAndreas SauterFelix MeurerAshley BenneTobias LasserFranz PfeifferFlorian SchaffDaniela Pfeiffer

Published in: European radiology experimental (2024)

• Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

Keyphrases

image quality
computed tomography
dual energy
neural network
deep learning
optical coherence tomography
high resolution
magnetic resonance imaging
magnetic resonance
artificial intelligence
contrast enhanced