Deep-Learning Image Reconstruction in CCTA

Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial

Cardiac CT allows the assessment of the heart and of the coronary arteries by use of ionising radiation. Although radiation exposure was significantly reduced in recent years, further decrease in radiation exposure is limited by increased image noise and deterioration in image quality. Recent evidence suggests that further technological refinements with artificial intelligence allows improved post-processing of images with reduction of image noise.

The present study aims at assessing the potential of a deep-learning image reconstruction algorithm in a clinical setting. Specifically, after a standard clinical scan, patients are scanned with lower radiation exposure and reconstructed with the DLIR algorithm. This interventional scan is then compared to the standard clinical scan.

Inclusion Criteria:

• Patients referred for cardiac CT angiography
• Age ≥ 18 years
• Written informed consent

Exclusion Criteria:

• Pregnancy or breast-feeding
• Enrollment of the investigator, his/her family members, employees and other dependent persons
• Renal insufficiency (GFR below 35 mL/min/1.73 m²)