Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system.
Sai Gokul HariharanChristian KaethnerNorbert StrobelMarkus KowarschikJulie DiNittoShadi AlbarqouniRebecca FahrigNassir NavabPublished in: International journal of computer assisted radiology and surgery (2019)
Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.
Keyphrases
- convolutional neural network
- deep learning
- image quality
- optical coherence tomography
- low dose
- magnetic resonance
- end stage renal disease
- newly diagnosed
- computed tomography
- ejection fraction
- chronic kidney disease
- palliative care
- magnetic resonance imaging
- contrast enhanced
- prognostic factors
- machine learning
- network analysis