Deep Learning-Based Reconstruction vs. Iterative Reconstruction for Quality of Low-Dose Head-and-Neck CT Angiography with Different Tube-Voltage Protocols in Emergency-Department Patients.
Marc LenfantPierre-Olivier CombyKévin GuillenFelix GalissotKarim HaiounAnthony ThayOlivier ChevallierFrédéric RicolfiRomaric LoffroyPublished in: Diagnostics (Basel, Switzerland) (2022)
DLR significantly reduced image noise and improved the overall image quality of CTSA with both low and standard tube voltages and at all vascular segments. CT that was acquired with 80 kVp and reconstructed with DLR yielded better overall image quality compared to higher kVp values with H-IR, while reducing the radiation dose by half, but it has limitations for arteries that are close to the aortic arch.
Keyphrases
- image quality
- deep learning
- computed tomography
- emergency department
- dual energy
- low dose
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- artificial intelligence
- peritoneal dialysis
- air pollution
- high dose
- magnetic resonance imaging
- patient reported outcomes
- quality improvement
- convolutional neural network
- blood flow