# Algorithms in Tomography and Related Inverse Problems--A Review
## 8 Conclusions  
An extensive reference on tomographic methods, techniques, effective algorithm implementation, and applications in various physical problems (medical, geophysics, solid state physics, etc.) is provided in the introduction of the paper.  
Furthermore, this comprehensive review provides an insightful analysis of six key themes in the domain of tomographic reconstruction. By critically assessing and synthesizing findings from various research papers, we have gained a comprehensive perspective on the evolution and potential future directions of tomographic imaging algorithms. Accordingly, optimization of breast tomosynthesis image reconstruction, highlighting the importance of refining these methods for more accurate diagnostics and improved patient care, is exposed. The emergence of multi-slice fusion as an innovative approach promises real-time insights into dynamic physiological processes, pushing the boundaries of medical diagnosis. Shifting our attention to computational efficiency, we witnessed a significant transformation in the acceleration of tomographic reconstruction algorithms using commodity PC graphics hardware. This advancement offers enhanced accessibility to high-speed reconstruction, making it more affordable and accessible for researchers and practitioners. In the realm of geophysics, the 3 DInvNet introduced a revolutionary deep learning-based approach to GPR data inversion. This integration of artificial intelligence with traditional sensing methods opens new possibilities for understanding geological and environmental sciences. Exploring Earth sciences, advanced inverse problem solutions in global seismic tomography are found, providing valuable insights into the Earth's interior and expanding our perspectives beyond conventional techniques. Optical coherence tomography was presented with extensive reference to four different papers to show off the fine detail captured from biological tissues. Recent topics such as \"large-scale image reconstruction\" can be elaborated in references [174; 175]. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data in order to reduce the imaging dose [176; 177].  
In summary, this review paper weaves together a tapestry of advancements in tomographic imaging techniques. These six interconnected themes, spanning medical imaging, computational acceleration, and deep learning in geophysics, underscore the versatility and potential of tomography. The multidimensional examination offers a holistic view of how tomographic reconstruction can shape the future of various scientific disciplines and medical diagnostics.  
Conceptualization, S.T., G.K. and V.A.; Methodology, G.K. and V.A.; Resources, S.T.; Writing--original draft preparation, S.T.; Writing--review and editing, G.K. and V.A. All authors have read and agreed to the published version of the manuscript.  
[PERSON] was financially supported by the Andreas Mentzelopoulos Foundation.  
Data Availability StatementData are available upon request.  
The authors declare no conflicts of interest.