Current Advances and Future Directions in Medical Imaging: Emerging Trends

Dear Colleagues,

Medical imaging is a technically and technologically evolving field. The discovery of X-ray provided the foundation for medical imaging and created opportunities for the non-invasive imaging of body tissues and disease processes, rapidly gaining relevance as a clinical diagnostic tool.  The use of X-rays for diagnostic purposes has evolved from two-dimensional to three-dimensional imaging modalities. Since then, non-X-ray based medical imaging modalities have been developed and clinically translated. Technologies such as ultrasound, computed tomography (CT) magnetic resonance imaging (MRI), positron emission (PET), single photon emission tomography (SPECT) and hybrid systems such as PET-CT and PET-MRI significantly advanced the field of medical imaging and modern health care. The introduction of photon-counting CT and ultra-high-field MRI systems have significantly improved image resolution and tissue contrast. PET, SPECT and MRI have also provided opportunities for the molecular imaging of cancer, cardiovascular diseases and brain health. PET has also enabled diagnostics and therapeutics to be combined to detect and treat cancer.

Over the last three decades, the capabilities of medical imaging modalities have been enhanced by artificial intelligence (AI), from optimising imaging to supporting diagnostic imaging interpretation. The ability of AI tools to extract and model medical imaging data has provided opportunities to reconstruct medical images, correct for artefacts, reduce radiation dose and provide diagnostic decision-support routinely and at the point-of-care. Advance AI technologies continue to expand the clinical utility and applicability of AI in modern health care. Generative AI has enabled image-to-image translation, noise reduction and improved resolution. Foundation models have enabled image interpretation, volumetric tracking and multiorgan segmentation. Explainable AI has enabled imaging features of disease processes to be mapped to support diagnostic decision-making. Federated learning has decentralised machine learning to allow collaborative model training without data exchange. Hybrid radiomics/deep learning models and multimodal AI have further improved the capabilities and applicability AI in medical imaging. Radiogenomics has enabled imaging features to be combined with genetic and molecular data (genomics) for the non-invasive assessment of cancer behaviour and therapeutic responses and the personalisation of treatment pathways.

The rapid innovations in hardware and software technologies, applicability, capabilities and clinical translation come with challenges from image optimisation, data and processing demands and safety and cost to generalisability, regulatory hurdles and universal benchmarking, which need to be addressed to maximise the benefits accrued. Workflows that bridge the gap between raw clinical imaging data and deployment-ready models also need to be optimised. These challenges suggest even more innovation in the field. Therefore, this Special Issue invites submissions addressing innovations, applicability, capabilities, challenges and solutions and the clinical translation of imaging and AI technologies in optimisation, disease diagnosis and treatment.

Dr. Ernest Usang Ekpo
Guest Editor

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