Abstract #2237

Code-Aware Transformation from NAC PET to AC PET, MRI, or CT Imaging

Yuxi Jin¹, Qingneng Li², Chao Zhou³, Zhihua Li², Zixiang Chen², Zhenxing Huang², Na Zhang², Xu Zhang³, Wei Fan³, Jianmin Yuan⁴, Qiang He⁴, Weiguang Zhang³, Hairong Zheng^2,5, Dong Liang^2,5, and Zhanli Hu^2,5

¹Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese, Shenzhen, China, ²Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, Shenzhen, China, ³Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China, ⁴Central Research Institute, United Imaging Healthcare Group, Shanghai, China, ⁵Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences., Beijing, China

Synopsis

Keywords: Other AI/ML, Multimodal, modality transformation, dynamicly transformation, NAC PET, AC PET, MRI, CT

Motivation: In radiation therapy with PET images, CT and MR images are used for precise targeting, but acquiring them is expensive, time-consuming and increases radiation risk.

Goal(s): Developing a deep learning model capable of dynamically switching to a specified mode enhances flexibility beyond traditional one-to-one cross-modal conversion methods.

Approach: We developed a deep learning model with dynamic modality translation capabilities by the incorporation of switch layers within the decoder module.

Results: The evaluations showed that our model excels at converting non-attenuation corrected PET images to attenuation corrected PET, MR, or CT images, making it easier to obtain additional modality images for radiation therapy.

Impact: Dynamic conversion from NAC PET to desired modalities like AC PET, CT, or MRI on demand is more efficient, saving on data storage and processing, and offers customized imaging for specific clinical needs, enhancing workflow efficiency.

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