Contrast-Aware Network With Aggregated-Interacted Transformer and Multi-Granularity Aligned Contrastive Learning for Synthesizing Contrast-Enhanced Abdomen CT Imaging

Abstract

Contrast-enhanced CT imaging (CECTI) is crucial for the diagnosis of patients with liver tumors. Therefore, if CECTI can be synthesized using only non-contrast CT imaging (NCCTI), it will provide significant clinical advantages. We propose a novel contrast-aware network with Aggregated-interacted Transformer and Multi-granularity aligned contrastive learning (AMNet) for CECTI synthesizing, which enables synthesizing CECTI for the first time. AMNet mitigates the challenges associated with high-risk, time-consuming, expensive, and radiation-intensive procedures required for obtaining CECTI. Furthermore, it overcomes the challenges of low contrast and low sensitivity in CT imaging through four key innovations to address these challenges: 1) The Aggregated-Interacted Transformer (AI-Transformer) introduces two mechanisms: multi-scale token aggregation and cross-token interaction. These enable long-range dependencies between multi-scale cross-tokens, facilitating the extraction of discriminative structural and content features of tissues, thereby addressing the low-contrast challenge. 2) The Multi-granularity Aligned Contrastive Learning (MACL) constructs a new regularization term for exploiting intra-domain compact and inter-domain separable features to improve the model's sensitivity to chemical contrast agents (CAs) and overcome the low sensitivity challenge. 3) The Contrast-Aware Adaptive Layer (CAL) imbues the AMNet with contrast-aware abilities that adaptively adjust the contrast information of various regions to achieve perfect synthesis. 4) The dual-stream discriminator (DSD) adopts an ensemble strategy to evaluate the synthetic CECTI from multiple perspectives. AMNet is validated using two corresponding CT imaging modalities (pre-contrast and portal venous-phase), an essential procedure for liver tumor biopsy. Experimental results demonstrate that our AMNet has successfully synthesized CECTI without chemical CA injections for the first time.