Deep Learning Synthesizes Contrast-Enhanced CT and MRI Scans

Contrast-enhanced imaging remains the cornerstone for the precise characterization of malignancies, ranging from hepatocellular carcinoma to head and neck squamous cell carcinoma [1, 2]. However, the administration of iodinated and gadolinium-based agents carries inherent risks of adverse reactions and systemic complications, particularly nephrotoxicity in patients with impaired renal function. While artificial intelligence has already demonstrated high sensitivity in detecting lesions across various modalities, most current models still rely on traditional contrast-enhanced sequences to achieve diagnostic precision [3, 4, 5]. As clinicians seek to minimize procedural risks while maintaining high diagnostic standards, the development of virtual enhancement techniques has become a priority. A recently published scoping review now evaluates the current state of deep learning models designed to synthesize these critical diagnostic images without the need for chemical contrast injection.