Techniques and Applications of Synthetic Images

In medical imaging and radiation oncology, image synthesis refers to generating a target image modality from a given source modality, for example, synthetic CT images from MRI simulations for treatment planning. Synthetic images have the potential to enhance the efficiency of the clinical workflow, through bypassing an imaging procedure when the acquisition is infeasible. Advanced image synthesis techniques, such as image synthesis with deep learning or other machine learning techniques, have led to improved images and enabled a wide range of clinical applications. This activity discusses advances in image synthesis techniques and applications, including quality assurance, based on various imaging modalities.

Topics:

1. Techniques and Applications of Synthetic Images in Radiation Oncology
    Jessica Scholey, PhD, DABR
2. Synthetic Data in Radiological Imaging: Current State and Future Outlook   
    Brandon J. Nelson, PhD
3. QA and Safety Considerations for Synthetic Images
    Jing Wang, PhD, FAAPM
4. Q and A/Panel Discussion
    Heng Li, PhD – Moderator
    Full Panel

This activity is available from March 31, 2026, through 11:59 p.m. Eastern time on March 30, 2028. 

The content was originally presented and recorded at the 2025 ASTRO Annual Meeting.

Target Audience

The activity is designed to meet the interests of radiation oncologists, physicists, radiation dosimetrists and residents.

Learning Objectives

Upon completion of this activity, participants should be able to:

  • Outline the techniques and applications of synthetic images. 
  • Describe the quality assurance for image synthesis. 
Course summary
Available credit: 
  • 1.00 AMA PRA Category 1 Credit™

    The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council for Continuing Medical Education for physicians. ASTRO designates this Enduring material for a maximum of 1.00 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

  • 1.00 Certificate of Attendance
    This activity was designated for 1.00 AMA PRA Category 1 Credit™.
Course opens: 
03/31/2026
Course expires: 
03/30/2028
Cost:
$149.00
Rating: 
0

Moderator:  

  • Heng Li, PhD, is employed by The Johns Hopkins Proton Center. Dr. Li has no relevant financial relationship with ineligible companies to disclose. 

Speakers:

  • Jessica Scholey, PhD, DABR, is employed by University of California San Francisco. Dr. Scholey has no relevant financial relationship with ineligible companies to disclose.
  • Brandon J. Nelson, PhD, is employed by the FDA. Dr. Nelson has no relevant financial relationship with ineligible companies to disclose.
  • Jing Wang, PhD, FAAPM, is employed by University of Texas Southwestern Medical Center. Dr. Wang receives grant/research funding from Varian and has ownership equity/limited liability company interest in Diagnostx LLC. 

Planners: 

  • Malcolm Mattes, MD is employed at Rutgers Cancer Institute. Dr. Mattes received research/grant funding from Gilead Sciences (relationship ended 8/30/2025). Dr. Mattes has no relevant financial relationship with ineligible companies. 
  • Parul Barry, MD, is employed by the University of Pittsburgh Medical Center. Dr. Barry has no relevant financial relationship with ineligible companies. 
  • Michael Haddock, MD, is employed by Mayo Clinic and has no relevant financial relationship with ineligible companies to disclose.
  • Manisha Palta, MD, is employed by Duke Cancer Center and Duke University Hospital. Dr. Palta receives grant/research funding from Merck and Varian Medical Systems. 
  • Salma Jabbour, MD, is employed by Rutgers Cancer Institute. Dr. Jabbour receives grant/research funding from Merck & Co., Inc. and Beigene. Dr. Jabbour receives compensation/payment as a consultant with Radialogica and IMX Medical. 

All relevant financial relationships have been mitigated.

The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Available Credit

  • 1.00 AMA PRA Category 1 Credit™

    The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council for Continuing Medical Education for physicians. ASTRO designates this Enduring material for a maximum of 1.00 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

  • 1.00 Certificate of Attendance
    This activity was designated for 1.00 AMA PRA Category 1 Credit™.

Price

Cost:
$149.00
Please login or register to take this course.

Course Fees:
ASTRO members must log in to the ASTRO website to view and receive the member rate.

  • Nonmember: $149
  • Member: $99
  • Member-in-Training: $49
  • Student/Graduate Student/PGY-1 Member: $49
  • Postdoctoral Fellow Member: $49

If you are an ASTRO member from a low or lower-middle income country, as identified by the World Bank, you can receive a 50% discount off your corresponding registration for this activity. Please email [email protected] to inquire about the discount.

Policies:
No refunds, extensions, or substitutions will be made for those participants who, for any reason, have not completed the activity by the expiration date. 

Participants using ASTRO Academy activities to satisfy the requirement of a Continuing Certification (MOC) program should verify the credit number and type and availability dates of any activity before making a purchase. No refunds, extensions, or substitutions will be made for participants who have purchased activities that do not align with their MOC requirement.

The activity and its materials will only be available on the ASTRO website until March 30, 2028, regardless of purchase date. At the expiration of the activity, participants will no longer have access to the activity or its materials. ASTRO reserves the right to remove an activity before its expiration date. 

Required Hardware/software

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