Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation.
Krishna Pandu WicaksonoKoji FujimotoYasutaka FushimiAkihiko SakataSachi OkuchiTakuya HinodaSatoshi NakajimaYukihiro YamaoKazumichi YoshidaKanae Kawai MiyakeHitomi NumamotoTsuneo SagaYuji NakamotoPublished in: European radiology (2022)
• GAN could significantly improve the image quality and vessel visualization of low-resolution brain MR angiography (MRA). • With optimally adjusted training parameters, the GAN model did not degrade diagnostic performance by generating substantial false positives or false negatives. • GAN could be a promising approach for obtaining higher resolution TOF-MRA from images scanned in a fraction of time.
Keyphrases
- image quality
- computed tomography
- contrast enhanced
- optical coherence tomography
- dual energy
- magnetic resonance imaging
- light emitting
- resting state
- white matter
- magnetic resonance
- single molecule
- functional connectivity
- mass spectrometry
- ms ms
- deep learning
- cerebral ischemia
- multiple sclerosis
- machine learning
- convolutional neural network
- virtual reality
- blood brain barrier
- network analysis