Ying Cui

Office: 216 Egan Research Center
Phone: 617-373-3975
E-mail: cui.yi@neu.edu

Advisors

Prof. Jennifer G. Dy , and Prof. Steve B. Jiang .

Brief Biography

Ying Cui received her B.S. degree from the Department of Electrical Engineering, Xi'an Jiaotong University , and M.S. degree from the Department of Computer Engineering, North Carolina State University in 2003. She is currently a Ph.D. student in the Department of Electrical and Computer Engineering, Northeastern University.

Research Interests

• Data mining and machine learning
• Biomedical research, in particular tumor respiratory motion modeling and analysis and prediction in real-time imaged guided radiation therapy
• Pattern Recognition

Publications

• Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian Alexander, Steve B. Jiang, "Multiple Template Based Fluoroscopic Tracking of Lung Tumor Mass without Implanted Fiducial Markers", to appear in 2007.
• Ying Cui, Xiaoli Z. Fern, Jennifer G. Dy, "Non-Redundant Multi-View Clustering Via Orthogonalization", to be appeared in the proceeding of IEEE International Conference on Data Mining (ICDM), 2007.
• Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian Alexander, Steve B. Jiang, "Robust fluoroscopic respiratory gating for lung cancer radiotherapy without implanted fiducial markers", Physics in Medicine and Biology (PMB), 52, 741-755, 2007.
• Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian Alexander, Steve B. Jiang, "Correlation score based respiratory gating for lung cancer radiotherapy without implanted fiducial markers", 48th Annual Meeting of the American Association of Physicists in Medicine (AAPM), 2006.
• Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian Alexander, Steve B. Jiang, "Fluoroscopic tracking of lung tumor mass without implanted fiducial markers", 48th Annual Meeting of the American Association of Physicists in Medicine (AAPM), 2006.

Projects

• Lung tumor motion analysis and prediction for image-guided radiotherapy (IGRT). Develop tools for gated treatment of lung cancer without implanted fiducial markers.
  • Introduction:
    • Treatment errors related to respiratory organ motion may greatly degrade the effectiveness of conformal radiotherapy for the management of thoracic and abdominal lesions. This has become a pressing issue in the emerging era of image-guided radiation therapy (IGRT). Respiratory gated radiotherapy holds promise to precisely deliver a lethal dose to the tumour, while minimizing the incidence and severity of normal tissue complications, for mobile tumours in the thorax and the abdomen. For gated lung cancer radiotherapy, it is difficult to generate accurate gating signals due to the large uncertainties when using external surrogates and the risk of pneumothorax when using implanted fiducial internal markers. Thus, we investigate algorithms for gated treatment without markers and utilizing flouroscopy images instead.
    • Acquire fluoro images

      A dual-head on-board x-ray imaging system, called Integrated Radiotherapy Imaging System (IRIS), developed at MGH. The system consists of two x-ray tubes, two flat panels, and two x-ray generators.

    • Preprocessing the image

      A frame of setup fluoroscopic images. Left: original image. Right: motion-enhanced image. The selected ROI is shown in both images as a rectangular region containing tumor positions of all phases.

• Score-based gating
  • Proposed clinical procedure
  • Gating by template matching:
    • Single template. All the images in the gating window is averaged to be a template.
    • Multiple templates. Each image in the gating window is a template.
    • Find the best representative template by clustering. Template Building: Gaussian mixture model for clustering and Bayesian Information Criteria (BIC) to automatically find the number of clusters. Using cluster means as representative templates.
    • Result, smooth correlation scores and good gating signals.

 

• Location-based gating.
  • Precise target localization in real time is particularly important for gated radiotherapy due to the reduced clinical tumor volume to planning target volume (CTV-to-PTV) margin and/or escalated dose. Implanted fiducial markers can help localize the tumor however for many lung cancer patients the implantation of markers is not a preferred procedure due to risk of pneumothorax. Therefore, direct tracking of lung tumor mass without implanted markers is necessary.
  • Build multiple templates
  • Multiple-template matching
  • Allow small shifts for each template matching and take the weighted average of locations derived by those templates with high scores
  • Tracking result in absolute distance with the clinician marked tumor location.

 

For more detailed information, refer to our publications.