{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:08:43Z","timestamp":1757617723853,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819638628"},{"type":"electronic","value":"9789819638635"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-3863-5_25","type":"book-chapter","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:43:20Z","timestamp":1743824600000},"page":"268-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthesising Whole-Body Diffusion-Weighted Maximum Intensity Projection Images Using Diffusion Model"],"prefix":"10.1007","author":[{"given":"Changhyun","family":"Kim","sequence":"first","affiliation":[]},{"given":"Antonio","family":"Candito","sequence":"additional","affiliation":[]},{"given":"Arrigo","family":"Cattabriga","sequence":"additional","affiliation":[]},{"given":"Dow-Mu","family":"Koh","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Matthew D.","family":"Blackledge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"Abdusalomov, A.B., Nasimov, R., Nasimova, N., Muminov, B., Whangbo, T.K.: Evaluating synthetic medical images using artificial intelligence with the GAN algorithm. Sensors 23(7), Article 3440 (2023). https:\/\/doi.org\/10.3390\/s23073440. https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3440","DOI":"10.3390\/s23073440"},{"key":"25_CR2","doi-asserted-by":"publisher","unstructured":"Buda, M., et al.: Breast Cancer Screening \u2013 Digital Breast Tomosynthesis (BCS-DBT) (Version 5). The Cancer Imaging Archive (2020). https:\/\/doi.org\/10.7937\/E4WT-CD02","DOI":"10.7937\/E4WT-CD02"},{"key":"25_CR3","doi-asserted-by":"publisher","unstructured":"Candito, A., et al.: Deep learning for delineation of the spinal canal in whole-body diffusion-weighted imaging: normalising inter- and intra-patient intensity signal in multi-centre datasets. Bioengineering 11(2), Article 130 (2024). https:\/\/doi.org\/10.3390\/bioengineering11020130","DOI":"10.3390\/bioengineering11020130"},{"issue":"Supplement 1","key":"25_CR4","doi-asserted-by":"publisher","first-page":"27S","DOI":"10.2967\/jnumed.115.157867","volume":"57","author":"GJR Cook","year":"2016","unstructured":"Cook, G.J.R., Azad, G.K., Goh, V.: Imaging bone metastases in breast cancer: staging and response assessment. J. Nucl. Med. 57(Supplement 1), 27S-33S (2016)","journal-title":"J. Nucl. Med."},{"key":"25_CR5","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"8","key":"25_CR6","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1007\/s00259-016-3350-4","volume":"43","author":"L Evangelista","year":"2016","unstructured":"Evangelista, L., et al.: Diagnostic imaging to detect and evaluate response to therapy in bone metastases from prostate cancer: current modalities and new horizons. Eur. J. Nucl. Med. Mol. Imag. 43(8), 1546\u20131562 (2016)","journal-title":"Eur. J. Nucl. Med. Mol. Imag."},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Proc. Syst. 27 (2014). https:\/\/doi.org\/10.48550\/arXiv.1406.2661","DOI":"10.48550\/arXiv.1406.2661"},{"key":"25_CR8","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Jadon, A., Kumar, S.: Leveraging generative AI models for synthetic data generation in healthcare: Balancing research and privacy. In: Proceedings of the 2023 International Conference on Smart Applications, Communications and Networking (SmartNets) (IEEE, 2023), pp. 1\u20134 (2023)","DOI":"10.1109\/SmartNets58706.2023.10215825"},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"10568","DOI":"10.1038\/s41598-023-36712-1","volume":"13","author":"R Kalantar","year":"2023","unstructured":"Kalantar, R., Hindocha, S., Hunter, B., et al.: Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19. Sci. Rep. 13, 10568 (2023). https:\/\/doi.org\/10.1038\/s41598-023-36712-1","journal-title":"Sci. Rep."},{"key":"25_CR11","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. CoRR, abs\/2103.14030 (2021). https:\/\/arxiv.org\/abs\/2103.14030"},{"key":"25_CR12","unstructured":"Luo, C.: Understanding diffusion models: A unified perspective, arXiv (2022). https:\/\/arxiv.org\/abs\/2208.11970"},{"key":"25_CR13","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: Proceedings of the International Conference on Machine Learning (PMLR, 2021), pp. 8162\u20138171 (2021)"},{"key":"25_CR14","doi-asserted-by":"publisher","unstructured":"National Lung Screening Trial Research Team. \u201cData from the National Lung Screening Trial (NLST). The Cancer Imaging Archive (2013). https:\/\/doi.org\/10.7937\/TCIA.HMQ8-J677","DOI":"10.7937\/TCIA.HMQ8-J677"},{"issue":"10","key":"25_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acca5c","volume":"68","author":"S Pan","year":"2023","unstructured":"Pan, S., et al.: 2D medical image synthesis using transformer-based denoising diffusion probabilistic model. Phys. Med. Biol. 68(10), 105004 (2023)","journal-title":"Phys. Med. Biol."},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Peng, J., Chen, G., Saruta, K., Terata, Y.: 2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model. Med. Imaging Process Technol. 6(1) (2023)","DOI":"10.24294\/mipt.v6i1.2518"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Brain imaging generation with latent diffusion models. In: Proceedings of the MICCAI Workshop on Deep Generative Models (Springer, 2022), pp. 117\u2013126 (2022)","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Reddy, S.: Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implem. Sci. 19(27) (2024). https:\/\/doi.org\/10.1186\/s13012-024-01357-9","DOI":"10.1186\/s13012-024-01357-9"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 (Springer International Publishing, Cham, 2015), pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"25_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Yang, D., et al.: Automatic liver segmentation using an adversarial image-to-image network. In: Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11\u201313, 2017, Proceedings, Part III, pp. 507\u2013515. Springer International Publishing (2017)","DOI":"10.1007\/978-3-319-66179-7_58"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3863-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T08:59:18Z","timestamp":1757149158000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3863-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819638628","9789819638635"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3863-5_25","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micad.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}