# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 6 Conclusions  
DOT with NIR light, which can image the concentrations of chromophores such as hemoglobin deep inside the body, is expected to be a new imaging modality for cancer diagnosis and functional brain imaging. Unlike X-ray CT, DOT image reconstruction must consider diffusive light propagation, which reduces the spatial resolution and quantification of images. Studies on image reconstruction have attempted to resolve the technical problems of spatial resolution and quantification accuracy in DOT images of highly heterogeneous measured objects. Various image reconstruction methods were reviewed here.  
In the forward process, the use of the RTE, \(P_{N}\), \(SP_{N}\), diffusion approximations, and MC simulations has been reported. Improvements in computational performance in recent years have enabled us to base the high-precision forward simulation of light propagation on predicting measurements for DOT image reconstruction. To reduce the mismatch between the forward simulation and the actual measurement, which degrades the quality of the reconstructed image, RTE and its higher-order approximation are useful. To balance the required precision in clinical applications with computational time and cost, an appropriate forward computation method should be chosen.  
During the optimization process, various methods, including regularization techniques and the use of structural and spectral prior information, have been proposed to overcome the technical problems of low spatial resolution, noise, and artifacts. Sparsity regularization techniques have improved spatial resolution. The use of prior information on the structures inside the body obtained from other imaging modalities, such as X-ray CT and MRI, is useful for treating heterogeneous backgrounds. Spectral prior information improves the quantification of chromophore concentrations in multispectral DOT imaging. For reliable image reconstruction in cancer diagnoses with high heterogeneity, the use of prior information and sparsity regularization must be effective. By considering the purpose of DOT in clinical use and the specificity of the diffuse optical measurement system, an effective optimization method can be developed.  
Although the authors have reviewed important studies, many of them could have been missed owing to the limitations of the authors' knowledge and time. As discussed in this review, various efforts have been made during the past two decades to improve image reconstruction. Moreover, technologies surrounding DOT, such as AI, high-performance computation, and PA imaging, have begun in recent years to change the circumstances. Novel solutions that apply emerging technologies to improve image reconstruction and DOT applications in clinics can be anticipated by leveraging numerous previously reported studies.  
[PERSON] and [PERSON] reviewed the literature and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by JSPS KAKENHI (grant number 21K04085).  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
The authors declare no conflict of interest.