Tensor-based Dictionary Learning for Imaging Biomarkers

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 DESCRIPTION (provided by applicant): As a central concept in systems biomedicine, biomarkers are multi-scale, diverse, and inter-connected indicators of physiological and pathological states and activities. Over the past decade, the research in this area has been active and exciting, including imaging informatics based on imaging biomarkers. In this context, the genome-wide association studies are being performed to establish fundamental links between genotypic and phenotypic biomarkers but a prime challenge is that progress along this direction has been far from what was widely expected. A critical observation is that while data are exploding from genome sequencing and epigenetic analysis, in most cases medical image features are still subjective or only defined in classic fashions, which seems an unreasonable imbalance between genotypic and phenotypic worlds. Lung cancer screening is an emerging CT application and an opportunity to identify imaging biomarkers. Like other cancers, lung cancer is not one but many diseases. It is different in each patient and even in each tumor site with overwhelming nonlinearity and dynamics. It is crystal-clear that comprehensive, adaptive and individualized therapies are needed to win the battle against lung cancer. Being consistent to this big picture, research on sophisticated, instead of simplistic, biomarkers is not only helpful but also necessary in cancer research, and imaging informatics must perform exclusive and intelligent mining through rich in vivo imaging data for biomarkers so that correlative and predictive models could be established. The general hypothesis behind this R21 project is that new phenotypic information can be unlocked in tomographic data to improve sensitivity and specificity significantly in lung cancer CT screening. The overall goal of this project is to develp a tensor-based dictionary learning approach for extraction of CT imaging biomarkers, and optimize a tensor-based locally linear embedding to use these biomarkers for differentiation between CT lung screening results. The major innovation of this project is to synergistically integrate tensor decomposition, dictionary learning, compressive sensing, low-dose reconstruction, machine learning, locally linear embedding, super-computing and big data mining into a brand-new imaging informatics approach, which can be viewed as "phenome sequencing" in analog of genome sequencing. Upon the successful completion of this project, the identified imaging biomarkers will have been demonstrated instrumental in reducing the false positive rate significantly for lung CT scans while the false negative rate is kept constant.It will also help accurately stage lung cancers and non-invasively monitor cancer progression and therapeutic response. Equally important is the technical significance of this project. If it is established, a lasting impact will be generated on the field of imaging informatics at large.