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Ying Zhang, Ph.D.

Ying Zhang, Ph.D.

Assistant Professor

School
Medical School
Department
Radiation Oncology
  • Biography

    Ying Zhang, Ph.D., DABR, is an Assistant Professor of Radiation Oncology at UT Southwestern Medical Center. She is part of the Department of Radiation Oncology’s Division of Medical Physics & Engineering.

    Before joining UT Southwestern, Dr. Zhang worked as an Assistant Professor and therapeutic medical physicist at Medical College of Wisconsin’s Department of Radiation Oncology. She is board certified in therapeutic medical physics by the American Board of Radiology.

    Dr. Zhang earned her Ph.D. in biomedical engineering from Tianjin University, China. During her Ph.D. studies, she attended Yale University’s Therapeutic Radiology Department as a Ph.D. exchange student. Following her doctoral studies, Dr. Zhang completed her medical physics postdoctoral fellow and residency training at Medical College of Wisconsin.

    Dr. Zhang’s research focuses on using artificial intelligence techniques to accelerate the process of MRI-guided adaptive radiotherapy. Her work includes developing automatic contour quality management methods, including a self-guided interactive contouring tool, automatic contour QA and correction methods, and deep learning-based fast daily adaptive deliverable plan prediction. 

    Her research efforts have resulted in the publication of numerous peer-reviewed papers and have been presented at several national conferences, where she was recognized with multiple prestigious awards.

  • Research Interest
    • Artificial intelligence in radiation therapy
    • Real-time and online adaptive radiotherapy
    • Using AI techniques to accelerate the process of MRI-guided adaptive radiotherapy (MRgART)
  • Publications
    Deep learning based automatic contour refinement for inaccurate auto-segmentation in MR-guided adaptive radiotherapy
    Ding J, Zhang Y, Amjad A, Sarosiek C, Dang NP, Zarenia M, Li XA Physics in medicine and biology 2023 Mar 68
    Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study
    Buchanan L, Hamdan S, Zhang Y, Chen X, Li XA Frontiers in Oncology 2023 Jan 13
    Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier
    Parchur AK, Lim S, Nasief HG, Omari EA, Zhang Y, Paulson ES, Hall WA, Erickson B, Li XA Medical physics 2023 Jan 50 440-448
    Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs
    Amjad A, Xu J, Thill D, Zhang Y, Ding J, Paulson E, Hall W, Erickson BA, Li XA Frontiers in Oncology 2023 13
    Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy
    Nasief HG, Parchur AK, Omari E, Zhang Y, Chen X, Paulson E, Hall WA, Erickson B, Li XA Radiotherapy and Oncology 2022 Nov 176 165-171
    A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging
    Zhang Y, Liang Y, Ding J, Amjad A, Paulson E, Ahunbay E, Hall WA, Erickson B, Li XA International Journal of Radiation Oncology Biology Physics 2022 Oct 114 349-359
    Automatic Contour Refinement for Deep Learning Auto-segmentation of Complex Organs in MRI-guided Adaptive Radiation Therapy
    Ding J, Zhang Y, Amjad A, Xu J, Thill D, Li XA Advances in Radiation Oncology 2022 Sep 7
    Multi-parametric magnetic resonance imaging for radiation treatment planning
    Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA Medical physics 2022 Apr 49 2836-2845
    A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging–Guided Online Adaptive Radiation Therapy: A Feasibility Study
    Zhang Y, Paulson E, Lim S, Hall WA, Ahunbay E, Mickevicius NJ, Straza MW, Erickson B, Li XA Advances in Radiation Oncology 2020 Nov 5 1350-1358
    Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks
    Liang Y, Schott D, Zhang Y, Wang Z, Nasief H, Paulson E, Hall W, Knechtges P, Erickson B, Li XA Radiotherapy and Oncology 2020 Apr 145 193-200
  • Honors & Awards
    • Basic Translational Science Award
      Physics, Senior Author, ASTRO (2024)
    • Best of Physics Abstract
      Co-author, ASTRO (2023)
    • Basic Translational Science Award, Clinical
      First author, ASTRO (2021)
    • Basic Translational Science Award, Physics
      Co-author, ASTRO (2021)
    • Best of Physics Abstract
      First author, ASTRO (2019)
    • Travel Award, Physics Category
      First author, ASTRO (2018)
  • Professional Associations/Affiliations
    • American Association of Physicists in Medicine (AAPM)
    • American Society for Radiation Oncology (ASTRO)