S Armato¹*, K Farahani²*, E Sidky³*, X Pan⁴*, E Sidky⁵*, X Hu⁶*, E Abadi⁷*, X Wang⁸*, D Alvarez⁹*, M Oumano¹⁰*, A Ehtesham¹¹*, (1) The University of Chicago, Chicago, IL, (2) National Cancer Institute, Bethesda, MD, (3) University of Chicago, Chicago, IL, (4) University of Chicago, Chicago, IL, (5) University of Chicago, Chicago, IL, (6) University of Texas Southwestern Medical Center, Dallas, TX, (7) Duke University, Durham, NC, (8) Oak Ridge National Laboratory, Oak Ridge, TN, (9) Miami Cancer Institute, Miami, FL, (10) Rhode Island Hospital / Brown University, Warwick, RI, (11) Washington University in St. Louis, St. Louis, MO

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  S Armato
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  K Farahani
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  E Sidky
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  X Pan
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  X Hu
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  E Abadi
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  X Wang
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  D Alvarez
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  M Oumano
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  A Ehtesham

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MO-J-BRC-0 (Monday, 7/11/2022) 4:30 PM - 6:00 PM [Eastern Time (GMT-4)]

Ballroom C

Advancement of imaging research requires years of effort from dedicated research groups around the world. Such groups, working independently, typically suffer from limited local resources in terms of patient data and access to the “ground truth” required to properly train and test their algorithms. When these research groups report their methods in the literature, it is difficult for the research community to compare the relative merits of different approaches, since performance can depend on factors such as database composition, image quality, “truth” definition, and performance evaluation metric. Grand challenges allow for a direct comparison of different algorithms designed for a specific task, with all algorithms following the same set of rules, operating on a common set of data, and being evaluated with a uniform performance assessment approach. Comparisons among the methods of participating groups can help identify approaches that are the most promising for a specific task. The AAPM Working Group on Grand Challenges (WGGC) was created to promote the conduct of grand challenges in medical imaging by (1) developing recommendations for hosting computational challenges through AAPM activities designed to assess or improve the use of medical imaging in diagnostic and/or therapeutic applications, (2) vetting proposals from groups that wish to host a challenge in conjunction with the Annual Meeting, (3) facilitating the execution of these challenges, and (4) generating ideas for challenges to advance the field. Three challenges sponsored by the WGGC were conducted in the months leading up to the 2022 AAPM Annual Meeting and provided a unique opportunity for participants to compare their algorithms and imaging protocols with those of other teams in a structured, direct way. These challenges were: (1) the Deep Learning for Inverse Problems: Spectral Computed Tomography Image Reconstruction (DL-Spectral CT) Challenge, (2) the Truth-Based CT Reconstruction (TrueCT) Challenge, and (3) the Standardizing Imaging and Reconstruction Protocols for Quantitative SPECT/CT Post Yttrium-90 Microspheres Delivery (SIRPRISE) Challenge. This session will include an overview of each challenge along with presentations from the top-performing teams.

Learning objectives:
1. To understand the role of grand challenges and public image datasets in medical imaging research.
2. To learn about the DL-Spectral CT Challenge and top-performing methods.
3. To learn about the TrueCT Challenge and top-performing methods.
4. To learn about the SIRPRISE Challenge and top-performing methods.

Handouts

• 175-64730-16361659-188277-298455935.pdf (Emil Sidky)

Keywords

Not Applicable / None Entered.

Taxonomy

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