Improvement of radiation treatment on cancer patients

Successful radiation therapy requires the quality assurance of equipment and software used for treatment. I have developed automatic quality assurance tools to ensure the treatment accuracy and improve the stability and consistency of treatment strategies. I have also conducted thoughtful evaluation and qualification strategies of state-of-the-art RT related equipment and software, which include the first commercially available on-line retrospective amplitude-binning algorithm for 4DCT acquisition techniques, the first commercial orthopedic metal artifact reduction function for generating high quality CT images for radiation therapy and investigating its clinical applications, and the first rapid Kilovoltage X-Ray Image Guidance System Designed for a Ring Shape Radiation Therapy Unit, to name a few.

1. Using the flat panel detector on linac for the kV X-ray generator test

On-board imaging (OBI) system have been introduced in the last two decades to assist in patient setup and increase the accuracy of radiation treatment deliveries. The kV OBI system usually consists of an X-ray tube and a flat panel detector. As image-guided radiation therapy (IGRT) has been widely applied and rapidly integrated in the routine clinical workflow of radiation oncology, majority of newly manufactured linacs are equipped with OBI systems. Outstanding performance of this system is required to ensure high accuracy in tumor localization and tracking. Therefore, a comprehensive quality assurance (QA) program for this system is critical. In this study, we propose and evaluate a novel approach utilizing the flat panel detector available on the machine to perform the X-ray generator test via XML-controlled image acquisition and advanced imaging analysis.

  • Bin Cai, Steven Dolly, Gregory Kamal, Sridhar Yaddanapudi, Baozhou Sun, S Murty Goddu, Sasa Mutic, Hua Li*, “A Feasibility Study of Using the Flat Panel Detector on LINAC for the kV X-ray Generator Test”, Medical Physics, 2018, In Press.

2. iDose4 iterative CT reconstruction for radiation therapy

This study is to evaluate the commercial released Philips iDose4 iterative reconstruction technique from the radiotherapy endpoints, compare it to the traditional filter back-projection (FBP) reconstruction technique, and ultimately provide clinical practice suggestions on its usage in radiation therapy.

3. Contrast-enhanced CT simulations in radiation therapy

Intravenous (IV) iodine contrast-enhanced CT simulations are commonly used in thoracic radiation therapy, greatly facilitating both tumor and normal tissue segmentation, particularly in the mediastinum 1-3. However, IV contrast agents increase the CT-imaging linear attenuation coefficients and the subsequent electron densities in the contrast-enhanced regions are erroneously assigned. This raises the concern that the erroneous assigned electron density variations may cause the dose distribution calculation to have clinically relevant errors. In this study, we investigated the severity of CT Hounsfield number (HU) variations on the contrast-affected regions (such as heart), and evaluated the effects of using IV contrast-enhanced CT simulation scans on radiation dosimetry through complete PTV and OAR dose difference analysis and γ passing rate comparisons. The magnitude of potential dose errors and their clinical significance for lung cancer radiation therapy treatments were determined as well.

4. Clinical evaluation of a commercial O-MAR tool in radiation therapy

Severe artifacts in kilovoltage-CT simulation images caused by large metallic implants can significantly degrade the conspicuity and apparent CT Hounsfield number of targets and anatomic structures, jeopardize the confidence of anatomical segmentation, and introduce inaccuracies into the radiation therapy treatment planning process. This study evaluated the performance of the first commercial orthopedic metal artifact reduction function (O-MAR) for radiation therapy, and investigates its clinical applications in treatment planning.

5. Clinical evaluation of a novel amplitude-based binning algorithm for 4D CT reconstruction

Phase binning algorithms are commonly utilized in 4DCT image reconstruction for characterization of tumor or organ shape and respiration motion but breathing irregularities occurring during 4DCT acquisition can cause considerable image distortions. Recently, amplitude-binning algorithms have been evaluated as a potential improvement to phase binning algorithms for 4DCT image reconstruction. The purpose of this study was to evaluate the performance of the first commercially available on-line retrospective amplitude binning algorithm for comparison to the traditional phase binning algorithm.

Related publications

  1. Bin Cai, Eric Langeman, Thomas Mazur, Chun Joo (Justin) C Park, Lauren E Henke, Hyun Kin, Geoffrey D. Hugo, Sasa Mutic, Hua Li, Characterization of a Prototype Rapid Kilovoltage X-Ray Image Guidance System Designed for a Ring Shape Radiation Therapy Unit” Medical Physics, 2019, 46(3):1355-1370, doi: 10.1002/mp.13396. (Editors’ Choice)
  2. Bin Cai, Steven Dolly, Gregory Kamal, Sridhar Yaddanapudi, Baozhou Sun, S Murty Goddu, Sasa Mutic,Hua Li*, A Feasibility Study of Using the Flat Panel Detector on LINAC for the kV X-ray Generator TestMedical Physics, 2018, 28(2):755-766.
  3. B. Fischer-Valuck, L. Henke, O. Green, R. Kashani, S. Acharya,Hua Li,et al.Two-and-a-half year clinical experience with the world’s first magnetic resonance image-guided radiation therapy systemAdvances in Radiation Oncology, 2017, doi: 10.1016/j.adro.2017.05.006.
  4. Sridhar Yaddanapudi, Bin Cai, Taylor Harry, Steven Dolly, Baozhou Sun,Hua Li, Keith Stinson, Camille Noel, Lakshmi Santanam, Todd Pawlicki, Sasa Mutic, S. Goddu, “Rapid Acceptance Testing of Modern Linac using Onboard MV and kV Imaging Systems“,Medical Physics, 2017, 44(7):3393-3406. doi: 10.1002/mp.12294.
  5. Kristy K. Brock, Sasa Mutic, Todd R. McNutt,Hua Li, Marc L. Kessler, “Use of Image Registration and Fusion Algorithms and Techniques in Radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132“,Medical Physics,2017, 44(7):e43-e76. doi: 10.1002/mp.12256.
  6. Hua Li*, Steven Dolly, Hsin-Chen Chen, Mark A. Anastasio, Daniel A. Low, Harold H. Li, Jeff M. Michalski, Wade L. Thorstad, Hiram Gay, Sasa Mutic, “A Comparative Study Based on Image Quality and Clinical Task Performance for CT Reconstruction Algorithms in Radiotherapy“,Journal of Applied Clinical Medical Physics, 2016, Vol.17, No.4, pp: 377-390.
  7. Steven Dolly, Hsin-Chen Chen, Mark A. Anastasio, Sasa Mutic,Hua Li*, “Practical considerations for noise power spectra estimation for simulation CT in radiation therapy“,Journal of Applied Clinical Medical Physics, 2016, Vol.17, No.3, pp: 392-407.
  8. Yanle Hu, Leith Rankine, Olga L. Green, Rojano Kashani, H. Harold Li,Hua Li, Roger Nana, Vivian Rodriguez, Lakshmi Santanam, Shmaryu Shvartsman, James Victoria, H. Omar Wooten, James F. Dempsey, Sasa Mutic, “Characterization of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy system“,Medical Physics, 2015, Vol. 42, No. 10, pp: 5828-5837.
  9. Baozhou Sun, S. Murty Goddu, Sridhar Yaddanapudi, Camille Noel,Hua Li, Bin Cai, James Kavanaugh, Sasa Mutic, “Daily QA of linear accelerators using only EPID and OBI“,Medical Physics, 2015, Vol. 42, No. 10, pp:5584-5594.
  10. Kevin Grantham,Hua Li, Tianyu Zhao, Eric Klein, “The Impact of CT scan energy on range calculation in proton therapy planning“,Journal of Applied Clinical Medical Physics, Vol.16, No.6, 2015, pp:100-109.
  11. Michael Altman, James Kavanaugh, Omar Wooten, Green Olga, Todd DeWees, Hiram Gay, Wade Thorstad,Hua Li, Sasa Mutic, “A framework for automated contour quality assurance in radiation therapy including adaptive techniques“,Physics in Medicine and Biology, 2015, Vol. 60, pp:5199–5209.
  12. Hua Li*, Beth Bottani, Todd DeWees, Daniel Low, Jeff M. Michalski, Sasa Mutic, Jeffrey Bradley, Clifford G. Robinson, “Prospective Study Evaluating the Use of IV contrast on IMRT Treatment Planning for Lung Cancer“,Medical Physics, 2014, Vol. 41, No. 3, 031913.
  13. Hua Li*, Lifeng Yu, Mark A. Anastasio, Hsin-Chen Chen, Jun Tan, Hiram Gay, Jeff M. Michalski, Daniel A. Low, Sasa Mutic, “Automatic CT Simulation Optimization for Radiation Therapy: A General Strategy“,Medical Physics, 2014, Vol. 41, No.3, 031708.
  14. Hua Li*, Camille Noel, Haijian Chen, Harold Li, Daniel Low, Jeff Michalski, Hiram Gay, Wade Thorstad, Sasa Mutic, “Clinical Evaluation of a Commercial Orthopedic Metal Artifact Reduction Tool for CT Simulations in Radiation Therapy“,Medical Physics, 2012, Vol. 39, No.12, pp:7507-7517.
  15. Jun Tan, H. Harold Li, Eric Klein,Hua Li, Parag Parikh, Deshan Yang, “Physical Phantom Studies of Helical Cone-Beam CT with Exact Reconstruction“,Medical Physics, 2012, Vol.39, No. 8, pp:4695-4704.
  16. Hua Li*, Camille Noel, Jose Garcia-Ramirez, Daniel Low, Jeffrey Bradley, Clifford Robinson, Sasa Mutic, Parag Parikh, “The Clinical Evaluation of a Novel Amplitude-based Binning Algorithm for 4D CT Reconstruction“, Medical Physics, 2012, Vol.39, No.2, pp: 922-932.