During the magnetic resonance imaging (MRI) procedures, contrast agents like the rare metal gadolinium can go ahead and pose potential health issues. Researchers at the Hong Kong Polytechnic University – PolyU have gone on to spend years developing contrast-free scanning technology and have even successfully developed AI-powered virtual MRI images for precision tumor detection by offering a safe and smarter diagnostic pathway.

Nanopharyngeal carcinoma – NPC happens to be a challenging malignancy because of its location within the nose-pharynx, which is an intricate area surrounded by crucial structures like the skull base as well as cranial nerves. This cancer is especially prevalent in Southern China, where it happens at a rate that is 20 times higher than in non-endemic regions of the world, thereby posing a prominent health scare and burden.

The fact is that the infiltrative nature of NPC goes on to make accurate imaging critical when it comes to effective treatment planning, especially for radiation therapy, which happens to remain the primary treatment modality. In the past, contrast-enhanced MRI, which uses gadolinium-based contrast agents (GBCAs), has been the highest standard for delineating tumor boundaries. But the usage of GBCAs does carry risks, thereby underscoring the requirement for safer imaging choices.

It is well to be noted that gadolinium is indeed capable of elevating the visibility of internal structures. This is especially very useful in the case of NPC, where the infiltrative nature of the tumor needs accurate imaging so as to distinguish it from the surrounding healthy tissues. But it also happens to pose a significant health risk, which includes nephrogenic systemic fibrosis. It is a serious condition that is associated with gadolinium exposure, which leads to fibrosis of the skin, internal organs, and joints, thereby causing severe pain as well as disability. Moreover, there are recent studies that have shown that gadolinium can accumulate within the brain, thereby raising concerns about the long-term effects that it has.

The head and professor of the PolyU Department of Health Technology and Informatics, Prof. Jing Cai, has been exploring certain methods so as to eradicate the usage of GBCAs with a focus on applying deep learning in terms of virtual contrast enhancement (VCE) with MRI. In a published paper in the international journal of radiation oncology, biology, and physics in 2022, Prof. Cai as well as his research team went on to report the development of the multimodality-guided synergistic neural network—MMgSN-Net. In 2024, he further went on to develop the Pixelwise gradient model with generative adverse serial network – GAN for virtual contrast enhancement – PGMGVCE, which is reported in Cancers.

It is well to be noted that MMgSN-Net goes on to represent a prominent jump forward when it comes to synthesizing virtual contrast-enhanced T1-weighted MRI images, right from contrast-free scans, making utmost use of complementary information from T1-weighted and T2-weighted images in order to produce high-quality synthetic images. Its architecture goes on to include a multi-modality learning module, a self-attention module, a synergistic guidance system, and a multilevel module, as well as a discriminator, all working in tandem so as to optimize feature extraction along with image synthesis. It is designed in order to unravel the tumor-related imaging characteristics from each input modality by overcoming the barriers in terms of single modality synthesis.