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MRI-to-PET cross-modality translation using globally & locally aware GAN (GLA-GAN) for multi-modal diagnosis of Alzheimer’s disease

Sikka, Apoorva, Peri, Skand, Virk, Jitender Singh, Niyaz, Usma, Bathula, Deepti R.·2025·The Journal of Precision Medicine: Health and Disease
DOI 10.1016/j.premed.2025.100004
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  • GLA-GAN synthesizes high-fidelity FDG-PET data from structural MRI, enabling PET-based diagnosis in resource-limited settings.

    “as exemplified by the Globally & Locally Aware GAN (GLA-GAN), which synthesizes high-fidelity FDG-PET data from structural MRI, thus enabling PET-based diagnosis in resource-limited settings”

    From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Sikka et al., 2025

    The Introduction (page unavailable) motivates MRI-to-FDG-PET synthesis by the cost and limited availability of PET, and PET Synthesis and AD Classification (pages unavailable) report improved image quality and Alzheimer’s classification using synthesized scans. The paper tests multimodal classification on research datasets; it does not establish PET-based diagnosis in resource-limited clinical settings. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 49

  1. Lorenzi, M., Simpson, I., Mendelson, A., Vos, S., Cardoso, M.J., Modat, M., Schott, J., Ourselin, S.: Multimodal image analysis in alzheimer's disease via statistical modelling of non-local intensity correlations. Scientific reports 6, 22161 (2016) https://doi.org/10.1038/srep22161 · DOI
  2. Gray, K., Wolz, R., Keihaninejad, S., Heckemann, R., Aljabar, P., Hammers, A., Rueckert, D.: Regional analysis of fdg-pet for use in the classification of alzheimer's disease. In: IEEE International Symposium on Biomedical Imag- ing(ISBI), pp. 1082-1085 (2011). https://doi.org/10.1109/ISBI.2011.5872589 · DOI
  3. Johnson, K., Fox, N., Sperling, R., Klunk, W.: Brain imaging in alzheimer disease. Cold Spring Harbor perspectives in medicine 2, 006213 (2012) https://doi.org/ 10.1101/cshperspect.a006213 · DOI · Paper page
  4. Zhang, X., Yang, Z., Lu, G., Yang, G., Zhang, L.: Pet/mr imaging: New frontier in alzheimer's disease and other dementias. Frontiers in Molecular Neuroscience 10, 343 (2017) https://doi.org/10.3389/fnmol.2017.00343 · DOI
  5. Cheng, D., Liu, M.: Cnns based multi-modality classification for ad diagnosis. In: 10th International Congress on Image and Signal Processing, BioMedical Engi- neering and Informatics (CISP-BMEI), pp. 1-5 (2017). https://doi.org/10.1109/ CISP-BMEI.2017.8302281 · DOI
  6. Huynh, T., Gao, Y., Kang, J., Wang, L., Zhang, P., Lian, J., Shen, D.: Estimating ct image from mri data using structured random forest and auto-context model. IEEE transactions on medical imaging 35, 174-183 (2015) https://doi.org/10. 1109/TMI.2015.2461533 · DOI
  7. Kang, J., Gao, Y., Shi, F., Lin, W., Shen, D.: Prediction of standard-dose brain pet image by using mri and low-dose brain [(18)f]fdg pet images. Medical physics 42, 5301 (2015) https://doi.org/10.1118/1.4928400 · DOI
  8. Jog, A., Carass, A., Pham, D., Prince, J.: Random forest flair reconstruction from t1, t2, and pd-weighted mri. 11th International Symposium on Biomedi- cal Imaging (ISBI) 2014, 1079-1082 (2014) https://doi.org/10.1109/ISBI.2014. 6868061 · DOI
  9. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Advances in Neural Information Processing Systems 3, 2672-2680 (2014) https://doi.org/10.1145/ 3422622 · DOI · Paper page
  10. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Con- ference on Learning Representations (ICLR), vol. abs/1511.06434 (2016). https: //doi.org/10.1109/AIAR.2018.8769811 · DOI
  11. Zhang, H., Xu, T., Li, H.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: International Conference on Com- puter Vision (ICCV), pp. 5908-5916 (2017). https://doi.org/10.1109/ICCV.2017. 629 · DOI · Paper page
  12. Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M., Zhang, Y., Sun, L., Wang, G.: Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Transactions on Medical Imaging 37, 1348-1357 (2017) https://doi.org/10.1109/TMI.2018.2827462 · DOI · Paper page
  13. Wolterink, J., Dinkla, A., Savenije, M., Seevinck, P., Berg, C., Išgum, I.: Deep mr to ct synthesis using unpaired data. In: MICCAI Workshop on Simulation and Synthesis in Medical Imaging, vol. 10557, pp. 14-23 (2017). https://doi.org/10. 1007/978-3-319-68127-6 2 · DOI
  14. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing 321, 321-331 (2018) https://doi. org/10.1016/j.neucom.2018.09.013 · DOI
  15. Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., Furukawa, Y., Mauri, G., Nakayama, H.: Gan-based synthetic brain mr image generation. In: 15th International Symposium on Biomedical Imaging (ISBI), pp. 734-738 (2018). https://doi.org/10.1109/ISBI.2018.8363678 · DOI
  16. Han, Z., Wei, B., Mercado, A., Leung, S., Li, S.: Spine-gan: Semantic segmentation of multiple spinal structures. Medical Image Analysis 50, 23-35 (2018) https: //doi.org/10.1016/j.media.2018.08.005 · DOI
  17. Sevetlidis, V., Giuffrida, V., Tsaftaris, S.: Whole image synthesis using a deep encoder-decoder network. In: MICCAI Workshop on Simulation and Synthe- sis in Medical Imaging, vol. 9968, pp. 127-137 (2016). https://doi.org/10.1007/ 978-3-319-46630-9 13 · DOI
  18. Kingma, D., Welling, M.: Auto-encoding variational bayes. In: 2nd Interna- tional Conference on Learning Representations (ICLR) (2014). https://doi.org/ 10.48550/arXiv.1312.6114 · DOI · Paper page
  19. Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q.: Medical image synthesis with context-aware generative adversarial networks. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 10435, pp. 417- 425 (2017). https://doi.org/10.1007/978-3-319-66179-7 48 · DOI
  20. Jin, C., Kim, H., Liu, M., Jung, W., Joo, S., Park, E., Ahn, Y., Han, I., Lee, J., Cui, X.: Deep ct to mr synthesis using paired and unpaired data. Sensors 19, 2361 (2019) https://doi.org/10.3390/s19102361 · DOI
  21. Ben-Cohen, A., Klang, E., Raskin, S., Soffer, S., Ben-Haim, S., Konen, E., Ami- tai, M., Greenspan, H.: Cross-modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection. Engineering Applications of Artificial Intelligence 78, 186-194 https://doi.org/10.1016/j.engappai.2018.11. 013 · DOI · Paper page
  22. Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O.: Predicting pet-derived demyelination from multimodal mri using sketcher- refiner adversarial training for multiple sclerosis. Medical Image Analysis 58, 101546 (2019) https://doi.org/10.1016/j.media.2019.101546 · DOI
  23. li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Medical Image Computing and Computer Assisted Intervention(MICCAI), vol. 17, pp. 305-12 (2014). https://doi.org/10.1007/978-3-319-10443-0 39 · DOI
  24. Sikka, A., Peri, S.V., Bathula, D.R.: Mri to fdg-pet: Cross-modal synthesis using 3d u-net for multi-modal alzheimer's classification. In: MICCAI Workshop on Simulation and Synthesis in Medical Imaging, vol. 11037, pp. 80-89 (2018). https: //doi.org/10.1007/978-3-030-00536-8 9 · DOI
  25. Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y.: Synthesizing missing pet from mri with cycle-consistent generative adversarial networks for alzheimer's disease diagnosis. In: Medical Image Computing and Computer Assisted Inter- vention (MICCAI), vol. 11072, pp. 455-463 (2018). https://doi.org/10.1007/ 978-3-030-00931-1 52 · DOI
  26. Sun, H., Mehta, R., Zhou, H., Huang, Z., Johnson, S., Prabhakaran, V., Singh, V.: Dual-glow: Conditional flow-based generative model for modality transfer. In: International Conference on Computer Vision (ICCV), vol. 2019, pp. 10610-10619 (2019). https://doi.org/10.1109/ICCV.2019.01071 · DOI
  27. Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1x1 convo- lutions. In: Advances in Neural Information Processing Systems, vol. 31, pp. 10215-10224 (2018). https://doi.org/10.48550/arXiv.1807.03039 · DOI · Paper page
  28. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision (ECCV), vol. 9906, pp. 694-711 (2016). https://doi.org/10.1007/978-3-319-46475-6 43 · DOI · Paper page
  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770- 778 (2016). https://doi.org/10.1109/CVPR.2016.90 · DOI · Paper page
  30. Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning (ICML), vol. 37, pp. 448-456 (2015). https://doi.org/10.5555/3045118. 3045167 · DOI
  31. Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: International Conference on Machine Learning (ICML), vol. 27, pp. 807-814 (2010). https://doi.org/10.5555/3104322.3104425 · DOI
  32. Gordon, B., Blazey, T., Benzinger, T.: Regional variability in alzheimer's disease biomarkers. Future Neurology 9, 131-134 (2014) https://doi.org/10.2217/FNL. 14.9 · DOI · Paper page
  33. Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C., Jagust, W., Trojanowski, J.Q., Toga, A.W., Beckett, L.: The alzheimer's disease neuroimaging initiative. Neuroimaging clinics of North America 15, 869-877 (2005) https:// doi.org/10.1016/j.nic.2005.09.008 · DOI · Paper page
  34. Jack, C., Knopman, D., Jagust, W., Shaw, L., Aisen, P., Weiner, M., Petersen, R., Trojanowski, J.: Hypothetical model of dynamic biomarkers of the alzheimer's pathological cascade. Lancet neurology 9, 119-28 (2010) https://doi.org/10.1016/ S1474-4422(09)70299-6 · DOI · Paper page
  35. Jack Jr, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Har- vey, D., Borowski, B., Britson, P.J., L. Whitwell, J., Ward, C., et al.: The alzheimer's disease neuroimaging initiative (adni): Mri methods. Journal of Mag- netic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 27(4), 685-691 (2008) https://doi.org/10.1002/ jmri.21049 · DOI · Paper page
  36. Tzourio-Mazoyer, N., Landeau, B., DF, P., Crivello, F., Etard, O.N.D., Delcroix, N., Mazoyer, B., Marc, J.: Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. NeuroImage 15, 273-89 (2002) https://doi.org/10.1006/nimg.2001.0978 · DOI · Paper page
  37. Iglesias, J., Liu, C.-Y., Thompson, P., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. IEEE transactions on medical imaging 30, 1617-34 (2011) https://doi.org/10.1109/TMI.2011.2138152 · DOI
  38. Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: Fsl. NeuroImage 62(2), 782-790 (2012) https://doi.org/10.1016/j.neuroimage. 2011.09.015 · DOI · Paper page
  39. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. In: NIPS 2017 Workshop on Autodiff (2017)
  40. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: Interna- tional Conference on Learning Representations (ICLR) (2014). https://doi.org/ 10.48550/arXiv.1412.6980 · DOI · Paper page
  41. Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary clas- sifier gans. In: 34th International Conference on Machine Learning (ICML), vol. 70, pp. 2642-2651 (2017). https://doi.org/10.5555/3305890.3305954 · DOI
  42. Wang, Y., Yin, G., Lang, J., Wang, P., Li, J.: U-net and gans-based pet synthesis from mri for soft-tissue sarcomas. International Journal of Radiation Oncol- ogy*Biology*Physics 108(3, Supplement), 775 (2020) https://doi.org/10.1016/j. ijrobp.2020.07.226 . Proceedings of the American Society for Radiation Oncology · DOI
  43. Sun, H., Mehta, R.R., Zhou, H.H., Huang, Z., Johnson, S.C., Prabhakaran, V., Singh, V.: Dual-glow: Conditional flow-based generative model for modality transfer. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 10610-10619 (2019)
  44. Hu, S., Shen, Y., Wang, S.-Q., Lei, B.: Brain MR to PET Synthesis via Bidirec- tional Generative Adversarial Network, pp. 698-707 (2020). https://doi.org/10. 1007/978-3-030-59713-9 67 · DOI
  45. Hu, S., Yuan, J., Wang, S.: Cross-modality synthesis from mri to pet using adversarial u-net with different normalization. In: 2019 International Confer- ence on Medical Imaging Physics and Engineering (ICMIPE), pp. 1-5 (2019). https://doi.org/10.1109/ICMIPE47306.2019.9098219 · DOI
  46. Zhang, J., He, X., Qing, L., Gao, F., Wang, B.: Bpgan: Brain pet synthesis from mri using generative adversarial network for multi-modal alzheimer's disease diagnosis. Computer Methods and Programs in Biomedicine 217, 106676 (2022) https://doi.org/10.1016/j.cmpb.2022.106676 · DOI
  47. Hernández, M., Reid, S., Mikhael, S., Pernet, C.: Do 2-year changes in superior frontal gyrus and global brain atrophy affect cognition? Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 10, 706-716 (2018) https://doi.org/ 10.1016/j.dadm.2018.07.010 · DOI
  48. Jacobs, H., Hopkins, D., Mayrhofer, H., Bruner, E., Leeuwen, F., Raaijmakers, W., Schmahmann, J.: The cerebellum in alzheimer's disease: Evaluating its role in cognitive decline. Brain : a journal of neurology 141, 37-47 (2017) https: //doi.org/10.1093/brain/awx194 · DOI
  49. Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J.B., Freeman, W.T., Torralba, A.: Gan dissection: Visualizing and understanding generative adversar- ial networks. In: International Conference on Learning Representations (ICLR) (2019). https://doi.org/10.48550/arXiv.1811.10597 · DOI

Cited by

Sorted by citations

Authors
Sikka, A., Peri, S., Virk, J. S., Niyaz, U., & Bathula, D. R.
ORCID
Bathula, Deepti R.
Venue
The Journal of Precision Medicine: Health and Disease
Publisher
Elsevier BV
Published
2025-07
Volume
2
Pages
100004
ISSN
3050-6328
DOI
10.1016/j.premed.2025.100004
Cited by
16
References
2
Language
en
Licence
Funders
Department of Science and Technology, Ministry of Science and Technology, India (SR/FST/ET-II/2022/1075)

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