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The inverse problem is ill-posed for the above modalities, which cause a nonunique solution. In this paper, we propose an effective reconstruction method based on the linearized Bregman iterative algorithm with sparse regularization (LBSR) for reconstruction. Considering the sparsity characteristics of the reconstructed sources, the sparsity can be regarded as a kind of<jats:italic>a priori<\/jats:italic>information and sparse regularization is incorporated, which can accurately locate the position of the source. The linearized Bregman iteration method is exploited to minimize the sparse regularization problem so as to further achieve fast and accurate reconstruction results. Experimental results in a numerical simulation and<jats:italic>in vivo<\/jats:italic>mouse demonstrate the effectiveness and potential of the proposed method.<\/jats:p>","DOI":"10.1155\/2015\/304191","type":"journal-article","created":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T17:39:13Z","timestamp":1441129153000},"page":"1-8","source":"Crossref","is-referenced-by-count":10,"title":["Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization"],"prefix":"10.1155","volume":"2015","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4535-5430","authenticated-orcid":true,"given":"Chengcai","family":"Leng","sequence":"first","affiliation":[{"name":"Key Laboratory of Nondestructive Testing of Ministry of Education, School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang 330063, China"},{"name":"State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy 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