Honors & Awards

  • Bio-X Bowes Fellow, Stanford Bio-X (2016-present)
  • Trainee, Stanford Training in Biomedical Imaging Instrumentation (TBI2) (2015-2016)

Education & Certifications

  • Master of Science, Stanford University, Electrical Engineering (2016)
  • Bachelor of Science, University of Notre Dame, Electrical Engineering (2014)

Stanford Advisors

Service, Volunteer and Community Work

  • President, Stanford Women in Electrical Engineering (WEE) (2016 - 2018)


    Stanford, CA 94305

All Publications

  • Image-domain insertion of spatially correlated, locally varying noise in CT images SPIE Medical Imaging 2019: Physics of Medical Imaging Divel, S. E., Pelc, N. J. 2019

    View details for DOI 10.1117/12.2512453

  • Accurate image domain noise insertion in CT images. IEEE transactions on medical imaging Divel, S. E., Pelc, N. J. 2019


    Tools to simulate lower dose, noisy computed tomography (CT) images from existing data enable protocol optimization by quantifying the trade-off between patient dose and image quality. Many studies have developed and validated noise insertion techniques; however, most of these tools operate on proprietary projection data which can be difficult to access and can be time consuming when a large number of realizations is needed. In response, this work aims to develop and validate an image domain approach to accurately insert CT noise and simulate low dose scans. In this framework, information from the image is utilized to estimate the variance map and local noise power spectra (NPS). Normally distributed noise is filtered within small patches in the image domain using the inverse Fourier transform of the square root of the estimated local NPS to generate noise with the appropriate spatial correlation. The patches are overlapped and element-wise multiplied by the standard deviation map to produce locally varying, spatially correlated noise. The resulting noise image is scaled based on the relationship between the initial and desired dose and added to the original image. The results demonstrate excellent agreement between traditional projection domain methods and the proposed method, both for simulated and real data sets. This new framework is not intended to replace projection domain methods; rather, it fills a gap in CT noise simulation tools and is an accurate alternative when projection domain methods are not practical, for example, in large scale repeatability or detectability studies.

    View details for DOI 10.1109/TMI.2019.2961837

    View details for PubMedID 31870981

  • Virtual clinical trial for task-based evaluation of a deep learning synthetic mammography algorithm Badal, A., Cha, K. H., Divel, S. E., Graff, C. G., Zeng, R., Badano, A., Schmidt, T. G., Chen, G. H., Bosmans, H. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2513062

    View details for Web of Science ID 000483585700022

  • Can image-domain filtering of FBP CT reconstructions match low-contrast performance of iterative reconstructions? SPIE Medical Imaging 2018: Physics of Medical Imaging Divel, S. E., Hsieh, S. S., Wang, J., Pelc, N. J. 2018

    View details for DOI 10.1117/12.2292599

  • Method for decreasing CT simulation time of complex phantoms and systems through separation of material specific projection data SPIE Medical Imaging 2017: Physics of Medical Imaging Divel, S. E., Christensen, S., Wintermark, M., Lansberg, M. G., Pelc, N. J. 2017

    View details for DOI 10.1117/12.2254076

  • Development of a realistic, dynamic digital brain phantom for CT Perfusion validation SPIE Medical Imaging 2016: Physics of Medical Imaging Divel, S. E., Segars, W. P., Christensen, S., Wintermark, M., Lansberg, M. G., Pelc, N. J. 2016

    View details for DOI 10.1117/12.2214997

  • Use of Synthetic CT to reduce simulation time of complex phantoms and systems 4th International Conference on Image Formation in X-Ray Computed Tomography Divel, S. E., Segars, W. P., Christensen, S., Wintermark, M., Pelc, N. J. 2016