Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
566-04-012 ISMRM Abstract

Synthetic fractional anisotropy maps from conventional MRI for discriminating autism and global developmental delay

Primary: Pediatrics - Neuro
Secondary: Analysis Methods - Image Synthesis and Translation
566-04-012 · Predicting Neurologic Outcomes · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row G
Keywords: Autism spectrum disorder Children Generative adversarial network Neurodevelopmental disorder Fractional anisotropy
Accepted
Miaoyan Wang 1,2,3, Dandan Xu2, Hongxuan Qiu4,5, Xinmeng Yu2, Zihang Cheng2, Cailei Zhao6, Wenbin Ji1, Bo Peng4, Yakang Dai4, Haoxiang Jiang2
1Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, China
2Department of Radiology, Affiliated Children's Hospital of Jiangnan University, Wuxi, China
3Enze Hospital,Taizhou Enze Medical Center (Group), Taizhou, China
4Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
5Changchun University of Technology, Changchun, China
6Shenzhen Children’s Hospital, Shenzhen, China
Presenting Author: Miaoyan Wang

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References

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2. 2. Fu L, Wang Y, Fang H, et al. Longitudinal Study of Brain Asymmetries in Autism and Developmental Delays Aged 2-5 Years. Neuroscience 2020;432:137-149. DOI: 10.1016/j.neuroscience.2020.02.028. [doi]
3. 3. Hong EK, Roh B, Park B, et al. Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study. Radiology 2025;314(3):e241646. doi: 10.1148/radiol.241646. [doi]
4. 4. Bi Z, Cao B, Zuo W, Hu Q. Learning a Prototype Discriminator With RBF for Multimodal Image Synthesis. IEEE Trans Image Process 2022;31:6664-6678. doi: 10.1109/TIP.2022.3214336. [doi]
5. 5. Chan K, Maralani PJ, Moody AR, Khademi A. Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks. Front Neuroinform 2023;17:1197330. Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks.
6. 6. Huang H, Mo J, Ding Z, et al. Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer. Radiology 2025;314(1):e240238. DOI: 10.1148/radiol.240238. [doi]
7. 7. Jeong JW, Sundaram S, Behen ME, Chugani HT. Relationship between genotype and arcuate fasciculus morphology in six young children with global developmental delay: Preliminary DTI stuy. J Magn Reson Imaging 2016;44(6):1504-1512. DOI: 10.1002/jmri.25306. [doi]
8. 8. Brugnara G, Jayachandran Preetha C, Deike K, et al. Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis. Radiol Artif Intell 2024;6(6):e230514. DOI: 10.1148/ryai.230514. [doi]
9. 9. Wang B, Pan Y, Xu S, et al. Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain Tumors. Radiology 2023;308(2):e222471. DOI: 10.1148/radiol.222471 [doi]

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http://echo.ismrm.org/p/ISMRM2026/566-04-012