Welcome to Quantitative BioImaging Laboratory

We envision that biomedical imaging will be a leading force in a new era of personalized medicine where quantitative methods of engineering and systems sciences have a pivotal role in disease detection, diagnostics, and treatment. The mission of my lab is to shape and advance the science and technology of biomedical imaging through innovative research and inspiring education, with the goal of becoming a destination laboratory internationally recognized as a first choice of both researchers and students. In the lab, our faculty members, researchers and students play leading roles from basic science discovery to the creation, clinical evolution, and commercialization of new technologies, devices and therapies. We are committed to excellence in scholarship and to the training of the next generation of imaging scientists. We serve the community through advanced innovation, translational research and clinical application of imaging sciences.

There are urgent needs to develop Quantitative Imaging methods and clinical decision software tools. Advances in molecular medicine offer the potential to move beyond traditional cytotoxic anticancer treatments and to develop safer and more effective targeted therapies based on the molecular characteristics of a patient’s tumor. Significant translational research efforts are needed to realize these emerging opportunities. Quantitative imaging will play a critical role for improving the detection, diagnosis, and therapy of diseases. The development of anatomical, functional, and molecular imaging methods requires proper recognition and addressing the complexities associated with the expression of suspected biomarkers. A full understanding of the response patterns for the potential surrogate biomarkers, e.g. those used to monitor angiogenesis, hypoxia, and necrosis, may often require the use of modeling and/or multi-parametric analysis of the image data in order to examine quantitative correlations with other clinical metadata and clinical outcomes. These requirements generally hold for the measurements of responses to drugs or radiation therapy and for image-guided interventions.

Dr. Baowei Fei

Director, Center for Imaging and Surgical Innovation
Director, Quantitative Bioimaging Laboratory (QBIL)
Cecil & Ida Green Chair in Systems Biology Science
Professor of Bioengineering, UT Dallas
Professor of Radiology, UT Southwestern

Research

Our research concentrates on the development and application of Quantitative Imaging technologies. Specifically, we are interested in synthesizing the information obtained from multiple imaging modalities and sources in order to study disease mechanisms and/or to aid in making clinical decisions.

2022 - Dr. Baowei Fei is recognized by the Jonsson School of Engineering and Computer Science and received the 2022 Faculty Award for exceptional research contributions.
The Faculty Research Awards recognize three Jonsson School faculty members (one assistant professor, one associate professor, and one full professor)...

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2022 - Dr. Baowei Fei served as the Chair for the NIH Study Section – P41 NIBIB Review F-SEP
Dr. Fei chaired the NIH Study Section Panel (ZEB1 OSR-F M2) for the National Institute of Biomedical Imaging and Bioengineering (NIBIB). The NIH...

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2020 - Eight undergraduate students published their first papers
Congratulate eight undergraduate students who published their first papers on their undergraduate research works in the lab in 2020. These papers are...

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All News

  • Ma L, Rathgeb A, Mubarak H, Tran M, Fei B. Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging. Journal of biomedical optics. 2022 May 1;27(5):056502-.

    Ma_2022_JBO_SuperRes_Reconstruction_WSI [PubMed] [PDF] [DOI]
  • Young J, Shahedi M, Dormer JD, Johnson B, Gahan J, Fei B. A low-cost PVC-based dual-modality kidney phantom. In Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling 2022 Apr 4 (Vol. 12034, pp. 639-647). SPIE.

    Fei_2022_SPIE_Young_Kidney_Phantom [PubMed] [PDF] [DOI]
  • Zhou X, Ma L, Mubarak HK, Little JV, Chen AY, Myers LL, Sumer BD, Fei B. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 91-100). SPIE.

    Fei_2022_SPIE_Ximing_PHSI_Histology [PubMed] [PDF] [DOI]

All Publications