Spectral Precise Image Study for Coronary Artery

Image Quality Evaluation of Coronary CT Angiography Using Deep Learning-Based Spectral Precise Image Reconstruction

This is a single-center, comparative, observational, retrospective data study. Datasets from patients who underwent spectral coronary CT angiography (CCTA) for suspected coronary artery disease (CAD) or other diagnosis needs as part of their routine clinical evaluation will be included.

Eligible dataset will be selected from the existing CT scan datasets generated at the study site, based on the CT scanning parameters, enrolled study participant datasets will be divided into Group A (120kVp, DRI=30) and Group B (100kVp, DRI=24). One hundred cases are planned to be enrolled in each group, with a total target of 200 datasets to be collected.

Inclusion Criteria:

1. Datasets from Patients who underwent spectral coronary CT angiography (CCTA).
2. Datasets from study participants with age ≥ 18 years old.
3. Scan parameters that meet the criteria defined in the 5.4.3.

Exclusion Criteria:

1. The clinical data information is considered incomplete after evaluation by the investigator.
2. The investigator determined that poor image quality (e.g. obvious artifacts, missing critical scan layers) would not satisfy post-processing analysis.
3. Data of patients deemed inappropriate for inclusion after evaluation by the investigator.