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Such an approach is usually associated with limited resolution of the reconstruction, high computational complexity due to slow convergence and noisy results.\nThis paper presents a novel method of PET image reconstruction using the underlying assumption that the originals of interest can be modelled using Gaussian mixture models. Parameters are estimated from one-dimensional projections using an iterative algorithm resembling the expectation-maximization algorithm. This presents a complex computational problem which is resolved by a novel approach that utilizes ${L_{1}}$ minimization.<\/jats:p>","DOI":"10.15388\/22-infor482","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T11:23:28Z","timestamp":1651490608000},"page":"653-669","source":"Crossref","is-referenced-by-count":1,"title":["2D PET Image Reconstruction Using Robust L1 Estimation of the Gaussian Mixture Model"],"prefix":"10.15388","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-7934","authenticated-orcid":false,"given":"Azra","family":"Tafro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5008-5047","authenticated-orcid":false,"given":"Damir","family":"Ser\u0161i\u0107","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8187-5540","authenticated-orcid":false,"given":"Ana","family":"Sovi\u0107 Kr\u017ei\u0107","sequence":"additional","affiliation":[]}],"member":"6097","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"2022092811193636369_j_infor482_ref_001","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1118\/1.2358198","article-title":"PET image reconstruction","volume":"1","year":"2006","journal-title":"Nuclear Medicine"},{"issue":"11","key":"2022092811193636369_j_infor482_ref_002","first-page":"1721","article-title":"The comparison of L11 and L22-norm minimization methods","volume":"5","year":"2010","journal-title":"International Journal of Physical Sciences"},{"issue":"1","key":"2022092811193636369_j_infor482_ref_003","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1214\/aos\/1176324464","article-title":"Optimal rate of convergence for finite mixture models","volume":"23","year":"1995","journal-title":"The Annals of Statistics"},{"issue":"6","key":"2022092811193636369_j_infor482_ref_004","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1109\/TKDE.2005.97","article-title":"Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection","volume":"17","year":"2005","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2022092811193636369_j_infor482_ref_005","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2307\/2984875","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","year":"1977","journal-title":"Journal of the Royal Statistical Society. 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