Prof. Bidaut’s primary goals lie in the development, implementation and application of advanced quantitative imaging and visualization techniques. The related techniques and approaches apply to either or both clinical and research paradigms, whether for the diagnosis, assessment and characterization of disease, or to manage the many sides and ancillaries of therapy. Accordingly, Luc Bidaut's main objectives are to develop, implement and exploit sophisticated processing and imaging concepts beyond standard radiological review, and to provide a functional, image-based multidisciplinary gateway between research, translational and clinical applications.

Related efforts are principally directed towards furthering the understanding, application and exploitation of biomedical imaging in its broadest sense. Of primary importance is understanding what each imaging modality can bring to this compound concept, as well as determining the best way to extract and analyze all relevant information from the data at hand in order to present it accessibly to the end-users. The scope of this research includes the human, animal and even microscopic sides of biomedical imaging along with its clinical and research exploitation.

Central to this broad scope is the following non-exhaustive list of topics:

• Advanced biomedical imaging
• Multidimensional imaging and visualization
• Image segmentation
• Multimodality and hybrid imaging
• Image registration and fusion
• Image-guided therapy (e.g., protocols and devices)
• Interventional planning and monitoring (e.g., radiology, surgery, MI, RT)
• Quantitative/parametric imaging
• Functional and Molecular imaging (e.g., positron emission tomography and others)
• Modeling and simulation
• Therapy follow-up and response assessment
• Scientific visualization
• High-Performance Visualization and Computing

Corollary to these core interests is the multidisciplinary strategizing and collaborations that are associated with implementing and furthering such concepts through the mixing of disciplines that do not traditionally interact with each other, and by fostering and pulling together resources that require complementary support from diverse entities. Other than for the sheer necessity of bundling up various types of expertise and technology to address efficiently the corresponding challenges, the multidisciplinarity of QAI is further boosted and justified by the multiple applications of this complex field, also well outside the purely biomedical realm. Such a versatility makes this field particularly well suited to both inter- and intra-entities collaborations and also to the promotion of core models suitable to address the diverse needs of a diverse community.

As overarching theme of Prof. Bidaut’s clinical interests, advanced quantitative imaging (QAI) is a concept that naturally lends itself to most aspects of disease characterization and therapy management - from diagnosis and therapy planning to delivery and response assessment -, and this especially for complex diseases such as cancer. In turn, QAI contributes to the overall advancement of biomedical imaging as a valid and fundamental component of new biomarkers. Clinical areas and disciplines of interest are ones that can either exploit or contribute to - either directly or indirectly - advanced imaging approaches and paradigms. Such areas include imaging modalities and protocols as well as the diagnosis and characterization of disease. They also include therapy and interventional planning along with guidance and response assessment for routine or trial treatment. While medical disciplines like radiology, surgery, oncology and radiation therapy are obvious consumers of the products of such approaches, other disciplines - including engineering, basic sciences and even art - are also crucial to furthering these concepts and broadening the scope of their applications.