{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:17:56Z","timestamp":1783106276734,"version":"3.54.6"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute of Information & Communications Technology Planning & Evaluation","doi-asserted-by":"publisher","award":["RS-2025-02214780"],"award-info":[{"award-number":["RS-2025-02214780"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002635","name":"Inha University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002635","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110713","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:48:42Z","timestamp":1781225322000},"page":"110713","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["p-LDM: Partitioned latent diffusion model for memory-efficient 3D medical image translation"],"prefix":"10.1016","volume":"125","author":[{"given":"Jion","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-4846","authenticated-orcid":false,"given":"Byeong-Seok","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110713_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107326","article-title":"BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI","volume":"165","author":"Jiang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110713_b2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2021.11.017","article-title":"Multimodal medical image segmentation using multi-scale context-aware network","volume":"486","author":"Wang","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.110713_b3","doi-asserted-by":"crossref","unstructured":"A. Rahman, J.M.J. Valanarasu, I. Hacihaliloglu, V.M. Patel, Ambiguous Medical Image Segmentation Using Diffusion Models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11536\u201311546.","DOI":"10.1109\/CVPR52729.2023.01110"},{"key":"10.1016\/j.bspc.2026.110713_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117421","article-title":"Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation","volume":"203","author":"Amini Amirkolaee","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"10.1016\/j.bspc.2026.110713_b5","doi-asserted-by":"crossref","first-page":"6003","DOI":"10.1364\/BOE.467683","article-title":"TCGAN: A transformer-enhanced GAN for PET synthetic CT","volume":"13","author":"Li","year":"2022","journal-title":"Biomed. Opt. Express"},{"issue":"3","key":"10.1016\/j.bspc.2026.110713_b6","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10334-021-00974-5","article-title":"GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment","volume":"35","author":"Ranjan","year":"2022","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110713_b7","doi-asserted-by":"crossref","unstructured":"Z. Zhang, M. Li, J. Yu, On the convergence and mode collapse of GAN, in: SIGGRAPH Asia 2018 Technical Briefs, 2018, pp. 1\u20134.","DOI":"10.1145\/3283254.3283282"},{"key":"10.1016\/j.bspc.2026.110713_b8","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110713_b9","doi-asserted-by":"crossref","unstructured":"R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.bspc.2026.110713_b10","series-title":"Sculpting efficiency: Pruning medical imaging models for on-device inference","author":"Sreeram","year":"2023"},{"issue":"1","key":"10.1016\/j.bspc.2026.110713_b11","doi-asserted-by":"crossref","first-page":"16450","DOI":"10.1038\/s41598-023-42000-9","article-title":"Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices","volume":"13","author":"Song","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110713_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2021.07.076","article-title":"An optimization scheme for segmented-memory neural network","volume":"462","author":"Shan","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.110713_b13","article-title":"Aligning few-step diffusion models with dense reward difference learning","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110713_b14","doi-asserted-by":"crossref","unstructured":"X. Zhang, P. Zhou, C. Lin, R. Ji, EfficientViT: Memory Efficient Vision Transformers with Cascaded Group Attention, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14420\u201314430.","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"10.1016\/j.bspc.2026.110713_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108201","article-title":"DR-CapsNet: Deep residual capsule network with dynamic routing for automated identification of hepatocellular carcinoma and cirrhosis in CT images","author":"Qu","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110713_b16","doi-asserted-by":"crossref","unstructured":"M.M. Rahman, R. Marculescu, G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV, 2024, pp. 7728\u20137737.","DOI":"10.1109\/WACV57701.2024.00755"},{"key":"10.1016\/j.bspc.2026.110713_b17","series-title":"HER-Seg: Holistically efficient segmentation for high-resolution medical images","author":"Xie","year":"2025"},{"key":"10.1016\/j.bspc.2026.110713_b18","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"14","article-title":"Generating realistic brain MRIs via a conditional diffusion probabilistic model","author":"Peng","year":"2023"},{"key":"10.1016\/j.bspc.2026.110713_b19","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"592","article-title":"Make-a-volume: Leveraging latent diffusion models for cross-modality 3D brain MRI synthesis","author":"Zhu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110713_b20","series-title":"Generative enhancement for 3D medical images","author":"Zhu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b21","doi-asserted-by":"crossref","unstructured":"S. Yu, K. Sohn, S. Kim, J. Shin, Video probabilistic diffusion models in projected latent space, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 18456\u201318466.","DOI":"10.1109\/CVPR52729.2023.01770"},{"key":"10.1016\/j.bspc.2026.110713_b22","series-title":"Leveraging multimodal CycleGAN for the generation of anatomically accurate synthetic CT scans from MRIs","author":"Crespi","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106676","article-title":"BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer\u2019s disease diagnosis","volume":"217","author":"Zhang","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"issue":"21","key":"10.1016\/j.bspc.2026.110713_b24","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.3390\/electronics11213470","article-title":"Generating synthetic images for healthcare with novel deep Pix2Pix GAN","volume":"11","author":"Aljohani","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.bspc.2026.110713_b25","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging","first-page":"1784","article-title":"MRI to CT synthesis of the lumbar spine from a pseudo-3D cycle GAN","author":"Oulbacha","year":"2020"},{"key":"10.1016\/j.bspc.2026.110713_b26","series-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014"},{"issue":"4","key":"10.1016\/j.bspc.2026.110713_b27","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TRPMS.2022.3223275","article-title":"Synthetic PET via domain translation of 3-D MRI","volume":"7","author":"Rajagopal","year":"2022","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10.1016\/j.bspc.2026.110713_b28","article-title":"Multi-scale tokens-aware transformer network for multi-region and multi-sequence MR-to-CT synthesis in a single model","author":"Zhong","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110713_b29","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110713_b30","series-title":"KNN-diffusion: Image generation via large-scale retrieval","author":"Sheynin","year":"2022"},{"key":"10.1016\/j.bspc.2026.110713_b31","series-title":"European Conference on Computer Vision","first-page":"37","article-title":"DiffiT: Diffusion vision transformers for image generation","author":"Hatamizadeh","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b32","series-title":"Semantic image synthesis via diffusion models","author":"Wang","year":"2022"},{"issue":"4","key":"10.1016\/j.bspc.2026.110713_b33","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1002\/mp.16847","article-title":"Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model","volume":"51","author":"Pan","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110713_b34","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin Transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.bspc.2026.110713_b35","series-title":"SOICT \u201923: Proceedings of the 12th International Symposium on Information and Communication Technology","first-page":"456","article-title":"LSegDiff: A latent diffusion model for medical image segmentation","author":"Vu Quoc","year":"2023"},{"key":"10.1016\/j.bspc.2026.110713_b36","series-title":"AMIA Annual Symposium Proceedings, vol. 2023","first-page":"624","article-title":"DiffusionCT: latent diffusion model for CT image standardization","author":"Selim","year":"2023"},{"key":"10.1016\/j.bspc.2026.110713_b37","series-title":"Latent diffusion model for medical image standardization and enhancement","author":"Selim","year":"2023"},{"key":"10.1016\/j.bspc.2026.110713_b38","doi-asserted-by":"crossref","unstructured":"J. Kim, H. Park, Adaptive latent diffusion model for 3D medical image to image translation: Multi-modal magnetic resonance imaging study, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 7604\u20137613.","DOI":"10.1109\/WACV57701.2024.00743"},{"key":"10.1016\/j.bspc.2026.110713_b39","series-title":"Similarity-aware syncretic latent diffusion model for medical image translation with representation learning","author":"Lin","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b40","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"657","article-title":"Slice-consistent 3D volumetric brain CT-to-MRI translation with 2D Brownian bridge diffusion model","author":"Choo","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b41","first-page":"22732","article-title":"TCAM-Diff: Triplane-aware cross-attention medical diffusion model","volume":"vol. 39","author":"Zhang","year":"2025"},{"key":"10.1016\/j.bspc.2026.110713_b42","series-title":"Blaze3DM: Marry triplane representation with diffusion for 3D medical inverse problem solving","author":"He","year":"2024"},{"key":"10.1016\/j.bspc.2026.110713_b43","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110713_b44","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TASLP.2019.2950099","article-title":"A vector quantized variational autoencoder (VQ-VAE) autoregressive neural F0 model for statistical parametric speech synthesis","volume":"28","author":"Wang","year":"2019","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"10.1016\/j.bspc.2026.110713_b45","doi-asserted-by":"crossref","unstructured":"P. Esser, R. Rombach, B. Ommer, Taming transformers for high-resolution image synthesis, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 12873\u201312883.","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"10.1016\/j.bspc.2026.110713_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110757","article-title":"AutoInfo GAN: Toward a better image synthesis GAN framework for high-fidelity few-shot datasets via NAS and contrastive learning","volume":"276","author":"Shi","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110713_b47","series-title":"DiTCtrl: Exploring attention control in multi-modal diffusion transformer for tuning-free multi-prompt longer video generation","author":"Cai","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110713_b48","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41597-023-02038-w","article-title":"The Queensland twin adolescent brain project, a longitudinal study of adolescent brain development","volume":"10","author":"Strike","year":"2023","journal-title":"Sci. Data"},{"issue":"7","key":"10.1016\/j.bspc.2026.110713_b49","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1002\/mp.16529","article-title":"SynthRAD2023 grand challenge dataset: Generating synthetic CT for radiotherapy","volume":"50","author":"Thummerer","year":"2023","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110713_b50","series-title":"The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification","author":"Baid","year":"2021"},{"issue":"10","key":"10.1016\/j.bspc.2026.110713_b51","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110713_b52","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110713_b53","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"issue":"3","key":"10.1016\/j.bspc.2026.110713_b54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4236\/jcc.2019.73002","article-title":"Image quality assessment through FSIM, SSIM, MSE and PSNR\u2014A comparative study","volume":"7","author":"Sara","year":"2019","journal-title":"J. Comput. Commun."},{"key":"10.1016\/j.bspc.2026.110713_b55","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110713_b56","doi-asserted-by":"crossref","unstructured":"R. Zhang, P. Isola, A.A. Efros, E. Shechtman, O. Wang, The unreasonable effectiveness of deep features as a perceptual metric, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 586\u2013595.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"10.1016\/j.bspc.2026.110713_b57","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2024.3385504","article-title":"Conditional diffusion models for semantic 3D brain MRI synthesis","author":"Dorjsembe","year":"2024","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.bspc.2026.110713_b58","doi-asserted-by":"crossref","unstructured":"A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H.R. Roth, D. Xu, Unetr: Transformers for 3d medical image segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"issue":"3","key":"10.1016\/j.bspc.2026.110713_b59","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942601267X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942601267X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:38:33Z","timestamp":1783103913000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S174680942601267X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":59,"alternative-id":["S174680942601267X"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110713","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"p-LDM: Partitioned latent diffusion model for memory-efficient 3D medical image translation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110713","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110713"}}