{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T03:38:05Z","timestamp":1782358685784,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"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>This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 regularization methods involve trade-offs between noise suppression and structural preservation, ElasticNet combines their strengths. Our method further introduces a dynamic weighting scheme that adaptively adjusts the balance between the L1 and L2 terms over iterations while ensuring nonnegativity when using a sufficiently small regularization parameter. We evaluated the proposed algorithm using numerical phantoms (Shepp\u2013Logan and digitized Hoffman) under various noise conditions. Quantitative results based on the peak signal-to-noise ratio and multi-scale structural similarity index measure demonstrated that the proposed dynamic ElasticNet regularized MLEM consistently outperformed not only standard MLEM and L1\/L2 regularized MLEM but also the fixed-weight ElasticNet variant. Clinical single-photon emission computed tomography brain image experiments further confirmed improved noise suppression and clearer depiction of fine structures. These findings suggest that our proposed method offers a robust and accurate solution for tomographic image reconstruction in nuclear medicine imaging.<\/jats:p>","DOI":"10.3390\/jimaging11070213","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T06:18:36Z","timestamp":1751264316000},"page":"213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Iterative Reconstruction with Dynamic ElasticNet Regularization for Nuclear Medicine Imaging"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4699-1642","authenticated-orcid":false,"given":"Ryosuke","family":"Kasai","sequence":"first","affiliation":[{"name":"Department of Medical Imaging\/Nuclear Medicine, Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7165-6099","authenticated-orcid":false,"given":"Hideki","family":"Otsuka","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging\/Nuclear Medicine, Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hussain, S., Mubeen, I., Ullah, N., Shah, S.S.U.D., Khan, B.A., Zahoor, M., Ullah, R., Khan, F.A., and Sultan, M.A. (2022). Modern diagnostic imaging technique applications and risk factors in the medical field: A review. Biomed. Res. Int., 2022.","DOI":"10.1155\/2022\/5164970"},{"key":"ref_2","first-page":"4","article-title":"Introduction to PET instrumentation","volume":"29","author":"Turkington","year":"2001","journal-title":"J. Nucl. Med. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cri\u015fan, G., Moldovean\u2013Cioroianu, N.S., Timaru, D.G., Andrie\u015f, G., C\u0103inap, C., and Chi\u015f, V. (2022). Radiopharmaceuticals for PET and SPECT imaging: A literature review over the last decade. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23095023"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1038\/s41467-023-36377-4","article-title":"Radiochemistry for positron emission tomography","volume":"14","author":"Rong","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1007\/s13534-024-00418-8","article-title":"A comprehensive review on Compton camera image reconstruction: From principles to AI innovations","volume":"14","author":"Kim","year":"2024","journal-title":"Biomed. Eng. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"529","DOI":"10.2217\/iim.10.49","article-title":"Image reconstruction for PET\/CT scanners: Past achievements and future challenges","volume":"2","author":"Tong","year":"2010","journal-title":"Imaging Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/0022-5193(70)90109-8","article-title":"Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography","volume":"29","author":"Gordon","year":"1970","journal-title":"J. Theor. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1088\/0031-9155\/49\/8\/006","article-title":"Experiments with the nonlinear and chaotic behaviour of the multiplicative algebraic reconstruction technique (MART) algorithm for computed tomography","volume":"49","author":"Badea","year":"2004","journal-title":"Phys. Med. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1080\/01630563.2024.2422058","article-title":"Data-driven Morozov regularization of inverse problems","volume":"45","author":"Haltmeier","year":"2024","journal-title":"Numer. Funct. Anal. Optim."},{"key":"ref_10","unstructured":"Kak, A.C., and Slaney, M. (1988). Principles of Computerized Tomographic Imaging, IEEE Press."},{"key":"ref_11","unstructured":"Stark, H. (1987). Image Recovery: Theory and Applications, Academic Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TMI.1982.4307558","article-title":"Maximum likelihood reconstruction for emission tomography","volume":"1","author":"Shepp","year":"1982","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","unstructured":"Macovski, A. (1983). Medical Imaging Systems, Prentice Hall."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1088\/0031-9155\/51\/2\/004","article-title":"Convergence study of an accelerated ML-EM algorithm using bigger step size","volume":"51","author":"Hwang","year":"2006","journal-title":"Phys. Med. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/42.363108","article-title":"Accelerated image reconstruction using ordered subsets of projection data","volume":"13","author":"Hudson","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1109\/83.465106","article-title":"Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms","volume":"4","author":"Fessler","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/83.650854","article-title":"Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods","volume":"7","author":"Byrne","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"556","DOI":"10.21037\/qims-20-19","article-title":"An improved patch-based regularization method for PET image reconstruction","volume":"11","author":"Gao","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Boudjelal, A., Messali, Z., and Elmoataz, A. (2017). A novel kernel-based regularization technique for PET image reconstruction. Technologies, 5.","DOI":"10.3390\/technologies5020037"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tuna, U., Pepe, A., and Ruotsalainen, U. (2011, January 23\u201329). Sequential regularized MLEM reconstruction method for incomplete sinograms. Proceedings of the 2011 IEEE Nuclear Science Symposium Conference Record, Valencia, Spain.","DOI":"10.1109\/NSSMIC.2011.6152689"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1111\/j.1365-246X.2007.03409.x","article-title":"Tomographic inversion using L1-norm regularization of wavelet coefficients","volume":"170","author":"Loris","year":"2007","journal-title":"Geophys. J. Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1049\/iet-ipr.2020.0194","article-title":"Simple algorithm for L1-norm regularisation-based compressed sensing and image restoration","volume":"14","author":"Qin","year":"2020","journal-title":"IET Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1364\/BOE.9.001423","article-title":"L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography","volume":"9","author":"Lu","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jmri.24365","article-title":"Fast image reconstruction with L2-regularization","volume":"40","author":"Bilgic","year":"2014","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yi, H., Chen, D., Li, W., Zhu, S., Wang, X., Liang, J., and Tian, J. (2013). Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: A comparative study. J. Biomed. Opt., 18.","DOI":"10.1117\/1.JBO.18.5.056013"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Series B Stat. Methodol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"157","DOI":"10.15199\/48.2017.12.39","article-title":"Detection of seepages in flood embankments using the ElasticNET method","volume":"1","author":"Rymarczyk","year":"2019","journal-title":"Prz. Elektrotech."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, J., Guo, H., Yu, J., Yi, H., Hou, Y., and He, X. (2021). A robust elastic net-L1L2 reconstruction method for X-ray luminescence computed tomography. Phys. Med. Biol., 66.","DOI":"10.1088\/1361-6560\/ac246f"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Ghanbari","year":"2008","journal-title":"IET Electron. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TBC.2008.2000733","article-title":"The evolution of video quality measurement: From PSNR to hybrid metrics","volume":"54","author":"Winkler","year":"2008","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_31","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mudeng, V., Kim, M., and Choe, S.-W. (2022). Prospects of structural similarity index for medical image analysis. Appl. Sci., 12.","DOI":"10.3390\/app12083754"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/7\/213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:00:09Z","timestamp":1760032809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/7\/213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":33,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["jimaging11070213"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11070213","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.174762800.00857276\/v1","asserted-by":"object"}]},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]}}}