Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)




Cancer and other acute and chronic diseases are results of perturbations of common molecular determinants in key biological and signaling processes. Imaging is critical for characterizing dynamic changes in tumors and metastases, the tumor microenvironment (TME), tumor-stroma interactions, and drug targets, at multiscale levels. Magnetic resonance imaging (MRI) has emerged to be a primary imaging modality for both clinical and preclinical applications due to its advantages over other modalities, including sensitivity to soft tissues, lack of depth limitations, and the use of nonionizing radiation. However, extending the application of MRI to achieve both qualitative and quantitative precise molecular imaging with the capability to quantify molecular biomarkers for early detection, staging, and monitoring therapeutic treatment, requires the capacity to overcome several major challenges including the tradeoff between metal binding affinity and relaxivity. The application of MRI to lung diseases such as lung fibrosis (IPF and COPD) and lung cancer is even more challenging due to lack of proton signal and short T2*. This dissertation will summarize from rationale, design strategy, and approaches, to the development and optimization of our pioneering new class of Protein-MRI contrast agent (ProCA), especially ProCA32.collagen, with desired high dual relaxivity largely derived from the secondary sphere relaxivity and nanosecond correlation time, with high metal stability, and with molecular biomarker collagen targeting capability, for precision MRI. I will further report the detection of fibrosis in the lung airway of an electronic cigarette-induced COPD mouse model, using hProCA32.collagen enabled precision MRI (pMRI). In addition, we achieved simultaneous non-invasive targeted detection of lung metastasis and adrenal gland metastasis from Lung carcinoma with LKB1 mutation using hProCA32.collagen with 3D-UTE and T1W pulse sequences. We demonstrated a two-fold higher CNR than HRCT for lung tumor detection in the mouse model. The hProCA32.collagen enhanced MRI signals were further compared with ex vivo MRI, BLI and histological analysis to explore its translational potential for early detection and staging lung cancer as well as monitoring disease progression and regression without radiation.


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