{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:14:28Z","timestamp":1788952468907,"version":"build-2803163510"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Deep learning for Alzheimer\u2019s disease (AD) detection from structural magnetic resonance imaging (MRI) needs large, labelled datasets, yet many cohorts hold only a few hundred participants, for which conventional augmentation adds little anatomical diversity. In a two-stage pipeline, a variational autoencoder compressed 256 \u00d7 256 coronal slices to a 32 \u00d7 32 \u00d7 8 latent space, and a class-conditional latent diffusion model under classifier-free guidance generated AD and cognitively normal (CN) images using 295 participants from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). The pipeline reached a Kernel Inception Distance (KID) of 0.030 \u00b1 0.002 and a bias-corrected Fr\u00e9chet Inception Distance (FID\u221e) of 43.82. A controlled ablation varying preprocessing alone improved KID by 0.0147 and precision by 0.069, both with 95% intervals excluding zero. FID did not separate the configurations. A ResNet-18 trained only on synthetic slices and tested on 44 held-out real participants (18 AD, 26 CN), each scored as the mean probability over twenty slices, reached an area under the curve of 0.779 \u00b1 0.031 against 0.869 \u00b1 0.027 for real data; the difference was not distinguishable at this sample size. No instance memorisation was found among 880 samples, and a size-matched control exposed a 27.7-percentage-point inflation in the standard memorisation metric. Preprocessing, therefore, measurably affects synthesis quality at the small-cohort scale, though not on every measure.<\/jats:p>","DOI":"10.3390\/jimaging12090426","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:19:16Z","timestamp":1788949156000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer\u2019s Disease: A Preprocessing-Focused Pipeline"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4514-3445","authenticated-orcid":false,"given":"Soheil","family":"Fallah","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University London, London E1 7HT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-615X","authenticated-orcid":false,"given":"Nitsa J.","family":"Herzog","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University London, London E1 7HT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s41572-021-00269-y","article-title":"Alzheimer Disease","volume":"7","author":"Knopman","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The Neuropathological Diagnosis of Alzheimer\u2019s Disease","volume":"14","author":"DeTure","year":"2019","journal-title":"Mol. Neurodegener."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","article-title":"Machine Learning Methods for Predicting Progression from Mild Cognitive Impairment to Alzheimer\u2019s Disease Dementia: A Systematic Review","volume":"13","author":"Grueso","year":"2021","journal-title":"Alzheimers Res. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Herzog, N.J., and Magoulas, G.D. (2021). Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia. Sensors, 21.","DOI":"10.3390\/s21030778"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16791","DOI":"10.1038\/s41598-023-43715-5","article-title":"Effects of MRI Scanner Manufacturers in Classification Tasks with Deep Learning Models","volume":"13","author":"Kushol","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion Models in Medical Imaging: A Comprehensive Survey","volume":"88","author":"Kazerouni","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_7","first-page":"12104","article-title":"Training Generative Adversarial Networks with Limited Data","volume":"Volume 33","author":"Karras","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_8","unstructured":"Ho, J., Jain, A., and Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., Tudosiu, P.-D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., and Cardoso, M.J. (2022). Brain Imaging Generation with Latent Diffusion Models. Proceedings of the Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, 22 September 2022, Springer.","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12098","DOI":"10.1038\/s41598-023-39278-0","article-title":"A Multimodal Comparison of Latent Denoising Diffusion Probabilistic Models and Generative Adversarial Networks for Medical Image Synthesis","volume":"13","author":"Niehues","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7303","DOI":"10.1038\/s41598-023-34341-2","article-title":"Denoising Diffusion Probabilistic Models for 3D Medical Image Generation","volume":"13","author":"Khader","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dhinagar, N.J., Thomopoulos, S.I., Laltoo, E., and Thompson, P.M. (2024). Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer\u2019s Disease Effect Detection. Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC53108.2024.10782737"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103527","DOI":"10.1016\/j.compbiomed.2019.103527","article-title":"Single-Slice Alzheimer\u2019s Disease Classification and Disease Regional Analysis with Supervised Switching Autoencoders","volume":"116","author":"Puentes","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, L., Qiao, H., and Zhu, F. (2022). Alzheimer\u2019s Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.871706"},{"key":"ref_16","unstructured":"Larsen, A.B.L., S\u00f8nderby, S.K., Larochelle, H., and Winther, O. (2016). Autoencoding beyond Pixels Using a Learned Similarity Metric. Proceedings of the 33rd International Conference on Machine Learning, PMLR."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Scardace, A., Puglisi, L., Guarnera, F., Battiato, S., and Rav\u00ec, D. (2026, January 6\u201310). A Novel Metric for Detecting Memorization in Generative Models for Brain MRI Synthesis. Proceedings of the 2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA.","DOI":"10.1109\/WACV61042.2026.00377"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chong, M.J., and Forsyth, D. (2020). Effectively Unbiased FID and Inception Score and Where to Find Them. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00611"},{"key":"ref_20","unstructured":"Bi\u0144kowski, M., Sutherland, D.J., Arbel, M., and Gretton, A. (May, January 30). Demystifying MMD GANs. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22544","DOI":"10.1038\/s41598-021-01681-w","article-title":"Effect of Data Leakage in Brain MRI Classification Using 2D Convolutional Neural Networks","volume":"11","author":"Yagis","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","unstructured":"(2026, July 16). Alzheimer\u2019s Disease Neuroimaging Initiative. Available online: https:\/\/adni.loni.usc.edu\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4952","DOI":"10.1002\/hbm.24750","article-title":"Automated Brain Extraction of Multisequence MRI Using Artificial Neural Networks","volume":"40","author":"Isensee","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_24","unstructured":"Larsen, R., Nielsen, M., and Sporring, J. (2006). Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2006, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00308809","article-title":"Neuropathological Stageing of Alzheimer-Related Changes","volume":"82","author":"Braak","year":"1991","journal-title":"Acta Neuropathol."},{"key":"ref_26","first-page":"14","article-title":"Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model","volume":"Volume 14227","author":"Greenspan","year":"2023","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2023"},{"key":"ref_27","unstructured":"Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., and Yang, D. (2022). MONAI: An Open-Source Framework for Deep Learning in Healthcare. arXiv."},{"key":"ref_28","unstructured":"Kouzelis, T., Kakogeorgiou, I., Gidaris, S., and Komodakis, N. (2025). EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling. Proceedings of the 42nd International Conference on Machine Learning, PMLR."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1038\/s41551-025-01468-8","article-title":"Unconditional Latent Diffusion Models Memorize Patient Imaging Data","volume":"10","author":"Dar","year":"2026","journal-title":"Nat. Biomed. Eng."},{"key":"ref_30","unstructured":"Ho, J., and Salimans, T. (2022). Classifier-Free Diffusion Guidance. arXiv."},{"key":"ref_31","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., Alch\u00e9-Buc, F.d\u2019, Fox, E., and Garnett, R. (2019). Improved Precision and Recall Metric for Assessing Generative Models. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1111\/j.1467-9868.2007.00593.x","article-title":"Bootstrapping Clustered Data","volume":"69","author":"Field","year":"2007","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/LSP.2014.2337313","article-title":"Fast Implementation of DeLong\u2019s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves","volume":"21","author":"Sun","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_36","unstructured":"Deo, Y., Jia, Y., Lassila, T., Smith, W.A.P., Lawton, T., Kang, S., Frangi, A.F., and Habli, I. (2025). Metrics That Matter: Evaluating Image Quality Metrics for Medical Image Generation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bonnaire, T., Urfin, R., Biroli, G., and M\u00e9zard, M. (2025). Why Diffusion Models Don\u2019t Memorize: The Role of Implicit Dynamical Regularization in Training. arXiv.","DOI":"10.52202\/085713-4720"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e210315","DOI":"10.1148\/ryai.210315","article-title":"RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning","volume":"4","author":"Mei","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_39","unstructured":"Lipman, Y., Chen, R.T.Q., Ben-Hamu, H., Nickel, M., and Le, M. (2023). Flow Matching for Generative Modeling. arXiv."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:37:52Z","timestamp":1788950272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090426"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090426","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}