Synthetic Time of Flight Magnetic Resonance Angiography Generation Model Based on Cycle‐Consistent Generative Adversarial Network Using PETRA‐MRA in the Patients With Treated Intracranial Aneurysm

Background Pointwise encoding time reduction with radial acquisition (PETRA) magnetic resonance angiography (MRA) is useful for evaluating intracranial aneurysm recurrence, but the problem of severe background noise and low peripheral signal‐to‐noise ratio (SNR) remain. Deep learning could reduce noise using high‐ and low‐quality images. Purpose To develop a cycle‐consistent generative adversarial network (cycleGAN)‐based deep learning model to generate synthetic TOF (synTOF) using PETRA. Study type Retrospective. Population A total of 377 patients (mean age: 60 ± 11; 293 females) with treated intracranial aneurysms who underwent both PETRA and TOF from October 2017 to January 2021. Data were randomly divided into training (49.9%, 188/377) and validation (50.1%, 189/377) groups. Field Strength/Sequence Ultra‐short echo time and TOF‐MRA on a 3‐T MR system. Assessment For the cycleGAN model, the peak SNR (PSNR) and structural similarity (SSIM) were evaluated. Image quality was compared qualitatively (5‐point Likert scale) and quantitatively (SNR). A multireader diagnostic optimality evaluation was performed with 17 radiologists (experience of 1–18 years). Statistical Tests Generalized estimating equation analysis, Friedman's test, McNemar test, and Spearman's rank correlation. P