{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:27:39Z","timestamp":1775251659862,"version":"3.50.1"},"reference-count":177,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Andreas Mentzelopoulos Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the ever-evolving landscape of tomographic imaging algorithms, this literature review explores a diverse array of themes shaping the field\u2019s progress. It encompasses foundational principles, special innovative approaches, tomographic implementation algorithms, and applications of tomography in medicine, natural sciences, remote sensing, and seismology. This choice is to show off the diversity of tomographic applications and simultaneously the new trends in tomography in recent years. Accordingly, the evaluation of backprojection methods for breast tomographic reconstruction is highlighted. After that, multi-slice fusion takes center stage, promising real-time insights into dynamic processes and advanced diagnosis. Computational efficiency, especially in methods for accelerating tomographic reconstruction algorithms on commodity PC graphics hardware, is also presented. In geophysics, a deep learning-based approach to ground-penetrating radar (GPR) data inversion propels us into the future of geological and environmental sciences. We venture into Earth sciences with global seismic tomography: the inverse problem and beyond, understanding the Earth\u2019s subsurface through advanced inverse problem solutions and pushing boundaries. Lastly, optical coherence tomography is reviewed in basic applications for revealing tiny biological tissue structures. This review presents the main categories of applications of tomography, providing a deep insight into the methods and algorithms that have been developed so far so that the reader who wants to deal with the subject is fully informed.<\/jats:p>","DOI":"10.3390\/a17020071","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T05:36:43Z","timestamp":1707197803000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Algorithms in Tomography and Related Inverse Problems\u2014A Review"],"prefix":"10.3390","volume":"17","author":[{"given":"Styliani","family":"Tassiopoulou","sequence":"first","affiliation":[{"name":"Electronics Laboratory, Physics Department, University of Patras, Rio, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9314-8359","authenticated-orcid":false,"given":"Georgia","family":"Koukiou","sequence":"additional","affiliation":[{"name":"Electronics Laboratory, Physics Department, University of Patras, Rio, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7307-580X","authenticated-orcid":false,"given":"Vassilis","family":"Anastassopoulos","sequence":"additional","affiliation":[{"name":"Electronics Laboratory, Physics Department, University of Patras, Rio, 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0074-7696(08)60925-0","article-title":"Three-Dimensional Reconstruction from Projections: A Review of Algorithms","volume":"38","author":"Gordon","year":"1974","journal-title":"Int. Rev. Cytol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/S0146-664X(77)80014-2","article-title":"Iterative three-dimensional image reconstruction from tomographic projections","volume":"6","author":"Colsher","year":"1977","journal-title":"Comput. Graph. Image Process"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MSP.2010.936743","article-title":"Tomographic Reconstruction in the 21st Century","volume":"27","author":"Clackdoyle","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_4","unstructured":"Hornegger, J., Maier, A., and Kowarschik, M. (2023, October 15). CT Image Reconstruction Basics. 2016 [Source: Radiology Key]. Available online: https:\/\/radiologykey.com\/ct-image-reconstruction-basics\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/s10916-018-1042-2","article-title":"A Methodological Review of 3D Reconstruction Techniques in Tomographic Imaging","volume":"42","author":"Khan","year":"2018","journal-title":"J. Med. Syst. J. Med Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/42.192686","article-title":"Matching of tomographic slices for interpolation","volume":"11","author":"Goshtasby","year":"1992","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_7","first-page":"1","article-title":"Statistical Image Reconstruction Methods for Transmission Tomography","volume":"Volume 1","author":"Fessler","year":"2000","journal-title":"Handbook of Medical Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/42.993134","article-title":"Edge-preserving tomographic reconstruction with nonlocal regularization","volume":"21","author":"Yu","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4431","DOI":"10.1109\/TIP.2012.2206033","article-title":"Recovering Missing Slices of the Discrete Fourier Transform Using Ghosts","volume":"21","author":"Chandra","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s10278-014-9736-6","article-title":"Evaluation of Back Projection Methods for Breast Tomosynthesis Image Reconstruction","volume":"28","author":"Zhou","year":"2014","journal-title":"J. Digit. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chetih, N., and Messali, Z. (2015, January 25). Tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART). Proceedings of the 3rd International CEIT 2015, Tlemcen, Algeria.","DOI":"10.1109\/CEIT.2015.7233031"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1177\/030089169608200512","article-title":"How Thick Should CT\/MR Slices be to Plan Conformal Radiotherapy? A Study on the Accuracy of Three-Dimensional Volume Reconstruction","volume":"82","author":"Somigliana","year":"1996","journal-title":"Tumori J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1088\/0266-5611\/18\/5\/315","article-title":"The inverse problem of emission tomography","volume":"18","author":"Gourion","year":"2002","journal-title":"IOP Publ. Inverse Probl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.ejrad.2008.08.013","article-title":"Technical principles of dual source CT","volume":"68","author":"Petersilka","year":"2008","journal-title":"Eur. J. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/21681163.2014.994819","article-title":"CT reconstruction from simultaneous projections: A step towards capturing CT in One Go","volume":"5","author":"Saha","year":"2014","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TIP.2017.2766785","article-title":"A Backprojection Slice Theorem for Tomographic Reconstruction","volume":"27","author":"Miqueles","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s00330-018-5810-7","article-title":"The evolution of image reconstruction for CT\u2014From filtered back projection to artificial intelligence","volume":"29","author":"Willemink","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s42256-020-00273-z","article-title":"Deep learning for tomographic image reconstruction","volume":"2","author":"Wang","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14316\/pmp.2021.32.1.1","article-title":"Basic Physical Principles and Clinical Applications of Computed Tomography","volume":"32","author":"Jung","year":"2021","journal-title":"Prog. Med. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s43586-021-00015-4","article-title":"X-ray computed tomography","volume":"1","author":"Withers","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_21","first-page":"764","article-title":"Image Processing and Data Analysis in Computed Tomography","volume":"72","author":"Seletci","year":"2007","journal-title":"Rom. J. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"052103","DOI":"10.1103\/PhysRevB.72.052103","article-title":"Equally sloped tomography with oversampling reconstruction","volume":"72","author":"Miao","year":"2005","journal-title":"Phys. Rev. B"},{"key":"ref_23","first-page":"032503","article-title":"Direct PET: Full Size Neural Network PET Reconstruction from Sinogram Data","volume":"7","author":"Whiteley","year":"2019","journal-title":"J. Med Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1002\/mp.13258","article-title":"High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains","volume":"46","author":"Lee","year":"2018","journal-title":"J. Med. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/TMI.2021.3066318","article-title":"Limited View Tomographic Reconstruction using a Cascaded Residual Dense Spatial-Channel Attention Network with Projection Data Fidelity Layer","volume":"40","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","unstructured":"Luther, K., and Seung, S. (2023). Stretched sinograms for limited-angle tomographic reconstruction with neural networks. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1118\/1.598470","article-title":"Multi-slice helical CT: Scan and reconstruction","volume":"26","author":"Hu","year":"1999","journal-title":"J. Med. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1053\/crad.2000.0651","article-title":"Multi-slice Technology in Computed Tomography","volume":"56","author":"Dawson","year":"2001","journal-title":"Clin. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TCI.2021.3074881","article-title":"Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction","volume":"7","author":"Majee","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1148\/radiol.10092212","article-title":"Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques","volume":"257","author":"Singh","year":"2010","journal-title":"Radiology"},{"key":"ref_31","first-page":"1852","article-title":"MRI Reconstruction Using Discrete Fourier Transform: A tutorial","volume":"2","author":"Aibinu","year":"2008","journal-title":"WASET"},{"key":"ref_32","first-page":"123","article-title":"Super-resolution reconstruction using cross-scale self-similarity in multi-slice MRI","volume":"16","author":"Plenge","year":"2013","journal-title":"MICCAI"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shinomiya, Y., and Yoshida, S. (2021). 3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution. Sensors, 21.","DOI":"10.3390\/s21092978"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/23.106686","article-title":"3-D phantom to simulate cerebral blood flow and metabolic images for PET","volume":"37","author":"Hoffman","year":"1990","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/42.712135","article-title":"Design and construction of a realistic digital brain phantom","volume":"17","author":"Collins","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","first-page":"870","article-title":"Advances in digital and physical anthropomorphic breast phantoms for X-ray imaging","volume":"45","author":"Glick","year":"2018","journal-title":"J. Med. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/S0720-048X(99)00086-8","article-title":"Subsecond multi-slice computed tomography: Basics and applications","volume":"31","author":"Schaller","year":"1999","journal-title":"Eur. J. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"041915","DOI":"10.1118\/1.4795758","article-title":"Generation of voxelized breast phantoms from surgical mastectomy specimens","volume":"40","author":"Das","year":"2013","journal-title":"J. Med. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"R65","DOI":"10.1088\/0031-9155\/48\/19\/R01","article-title":"Digital X-ray tomosynthesis: Current state of the art and clinical potential","volume":"48","author":"Dobbins","year":"2003","journal-title":"Phys. Med. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"291","DOI":"10.3934\/ipi.2020013","article-title":"Robust and stable region-of-interest tomographic reconstruction using a robust width prior","volume":"14","author":"Goossens","year":"2020","journal-title":"Inverse Probl. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.1002\/mp.14779","article-title":"DIR-DBTnet: Deep iterative reconstruction network for three-dimensional digital breast tomosynthesis imaging","volume":"48","author":"Su","year":"2021","journal-title":"Med. Phys."},{"key":"ref_42","unstructured":"Quillent, A., Bismuth, V.J., Bloch, I., Kervazo, C., and Ladjal, S. (2023). A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction. MIDL Poster, 1\u201313. Available online: https:\/\/openreview.net\/pdf?id=xcMTcyk2v69."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lyu, T., Wu, Z., Ma, G., Jiang, C., Zhong, X., Xi, Y., Chen, Y., and Zhu, W. (2023). PDS-MAR: A fine-grained Projection-Domain Segmentation-based Metal Artifact Reduction method for intraoperative CBCT images with guide wires. arXiv.","DOI":"10.1088\/1361-6560\/ad00fc"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1067\/moe.2001.117812","article-title":"Effect of angular disparity of basis images and projection geometry on caries detection using tuned-aperture computed tomography","volume":"92","author":"Abreu","year":"2001","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pekel, E., Lavilla, M.L., Pfeiffer, F., and Lasser, T. (2023). Runtime Optimization of Acquisition Trajectories for X-ray Computed Tomography with a Robotic Sample Holder. arXiv.","DOI":"10.1088\/2631-8695\/ad08fd"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","article-title":"Deep Convolutional Neural Network for Inverse Problems in Imaging","volume":"26","author":"Jin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","first-page":"296","article-title":"Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion","volume":"20","author":"Hou","year":"2017","journal-title":"MICCAI"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/21681163.2023.2219765","article-title":"Deep learning-based automated COVID-19 classification from computed tomography images","volume":"11","author":"Morani","year":"2021","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/TNS.2005.851398","article-title":"Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware","volume":"52","author":"Fang","year":"2005","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"018001","DOI":"10.1088\/1674-1137\/39\/1\/018001","article-title":"A User-Friendly Nano-CT Image Alignment and 3D Reconstruction Platform Based on LabVIEW","volume":"39","author":"Wang","year":"2015","journal-title":"Chin. Phys. C"},{"key":"ref_51","unstructured":"Pham, M., Yuan, Y., Rana, A., Miao, J., and Osher, S. (2020). RESIRE: Real space iterative reconstruction engine for Tomography. arXiv."},{"key":"ref_52","unstructured":"Lyons, C., Raj, R.G., and Cheney, M. (2023). A Compound Gaussian Network for Solving Linear Inverse Problems. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2528\/PIER09052003","article-title":"A trust region subproblem for 3D electrical impedance tomography inverse problem using experimental data","volume":"94","author":"Goharian","year":"2009","journal-title":"Prog. Electromagn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5121\/ijist.2012.2502","article-title":"Implementation of Radon Transformation for Electrical Impedance Tomography (EIT)","volume":"2","author":"Hossain","year":"2012","journal-title":"IJIST"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ihrke, I., and Magnor, M. (2004, January 27\u201329). Image-based tomographic reconstruction of flames. Proceedings of the Eurographics Symposium on Computer, Grenoble, France.","DOI":"10.1145\/1028523.1028572"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"R41","DOI":"10.1088\/0266-5611\/15\/2\/022","article-title":"Optical tomography in medical imaging","volume":"15","author":"Arridge","year":"1999","journal-title":"Inverse Probl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4445","DOI":"10.1109\/TMI.2020.3020720","article-title":"Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction with Neural Network","volume":"39","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/36.868873","article-title":"Firstdemonstration of airborne SAR tomography using multibaseline L-band data","volume":"38","author":"Reigber","year":"2000","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3497","DOI":"10.1109\/TGRS.2006.881748","article-title":"Imaging of Single and Double Scatterers in Urban Areas via SAR Tomography","volume":"44","author":"Fornaro","year":"2006","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_60","unstructured":"Oriot, H., and Cantalloube, H. (2008, January 2\u20135). Circular SAR imagery for urban remote sensing. Proceedings of the 7th EUSAR, Friedrichshafen, Germany."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TGRS.2010.2050487","article-title":"Very High Resolution Spaceborne SAR Tomography in Urban Environment","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1109\/TGRS.2011.2132727","article-title":"Extraction and Three-Dimensional Reconstruction of Isolated Buildings in Urban Scenes From High-Resolution Optical and SAR Spaceborne Images","volume":"49","author":"Sportouche","year":"2011","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1109\/TGRS.2011.2177843","article-title":"Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment","volume":"50","author":"Zhu","year":"2012","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/JSTSP.2015.2469646","article-title":"JointSparsity in SAR Tomography for Urban Mapping","volume":"9","author":"Zhu","year":"2015","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isprsjprs.2018.10.003","article-title":"A framework for SAR-optical stereogrammetry over urban areas","volume":"146","author":"Bagheri","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Budillon, A., Johnsy, A., and Schirinzi, G. (2019). Urban Tomographic Imaging Using Polarimetric SAR Data. J. Remote Sens., 11.","DOI":"10.3390\/rs11020132"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhang, X., Hu, Y., and Zhan, X. (2022, January 17\u201322). AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884512"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.1984.350573","article-title":"Geophysical Diffraction Tomography","volume":"GE-22","author":"Devaney","year":"1984","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1088\/0266-5611\/14\/3\/002","article-title":"Global seismic tomography: The inverse problem and beyond","volume":"14","author":"Trampert","year":"1998","journal-title":"Inverse Probl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1190\/1.1443553","article-title":"Characterization of resolution and uniqueness in crosswell direct-arrival traveltime tomography using the Fourier projection slice theorem","volume":"59","author":"Rector","year":"1994","journal-title":"J. Geophys."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1144\/GSL.SP.2003.215.01.03","article-title":"Computed tomography in petroleum engineering research","volume":"215","author":"Akin","year":"2003","journal-title":"Geol. Soc. Spec. Publ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1109\/TGRS.2021.3051566","article-title":"Clutter Distributions for Tomographic Image Standardization in Ground-Penetrating Radar","volume":"59","author":"Worthmann","year":"2021","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1046\/j.1365-2478.1997.430277.x","article-title":"Introduction to ground surface self-potential tomography","volume":"45","author":"Patella","year":"1997","journal-title":"Geophys. Prospect."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Dai, Q., Lee, Y.H., Sun, H.-H., Ow, G., Mohd Yusof, M.L., and Yucel, A.C. (2023). 3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion. arXiv.","DOI":"10.1109\/TGRS.2023.3275306"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1088\/0031-9155\/59\/8\/1979","article-title":"Inverse problems of ultrasound tomography in models with attenuation","volume":"59","author":"Goncharsky","year":"2014","journal-title":"Phys. Med. Biol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/TUFFC.2020.2972327","article-title":"3D Wave-Equation-Based Finite-Frequency Tomography for Ultrasound Computed Tomography","volume":"67","author":"Martiartu","year":"2019","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1021\/acsphotonics.2c01431","article-title":"Tomographic Reconstruction of Quasistatic Surface Polariton Fields","volume":"10","author":"Hauer","year":"2022","journal-title":"ACS Photonics"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"12872","DOI":"10.1364\/OE.379200","article-title":"Diffraction tomography with a deep image prior","volume":"28","author":"Zhou","year":"2020","journal-title":"Opt. Express"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1080\/17415977.2015.1104307","article-title":"X-ray Compton scattering tomography","volume":"24","author":"Webber","year":"2015","journal-title":"Inverse Probl. Sci. Eng."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yang, D.-C., Zhang, S., Hu, Y., and Hao, Q. (2023). Refractive Index Tomography with a Physics Based Optical Neural Network. arXiv.","DOI":"10.1364\/BOE.504242"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1259\/0007-1285-46-552-1016","article-title":"Computerized transverse axial scanning (tomography): Part 1. Description of system","volume":"46","author":"Hounsfield","year":"1973","journal-title":"BJR"},{"key":"ref_82","first-page":"97","article-title":"NMR in cancer: XVI. FONAR image of the live human body","volume":"9","author":"Damadian","year":"1977","journal-title":"Physiol. Chem. Phys."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1126\/science.115.2983.226","article-title":"Application of Echo-Ranging Techniques to the Determination of Structure of Biological Tissues","volume":"115","author":"Wild","year":"1952","journal-title":"Science"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4971","DOI":"10.1073\/pnas.85.14.4971","article-title":"Comparison of time-resolved and -unresolved measurements of deoxyhemoglobin in brain","volume":"85","author":"Chance","year":"1988","journal-title":"Proc. Nati. Acad. Sci. USA"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1148\/radiology.205.2.9356620","article-title":"Digital tomosynthesis in breast imaging","volume":"205","author":"Niklason","year":"1997","journal-title":"Radiology"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1148\/rg.27si075511","article-title":"Breast tomosynthesis: Present considerations and future applications","volume":"27","author":"Park","year":"2007","journal-title":"Radiographics"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1118\/1.2776256","article-title":"III. Importance of point-by-point back projection (BP) correction for isocentric motion in digital breast tomosynthesis: Relevance to morphology of microcalcifications","volume":"34","author":"Chen","year":"2007","journal-title":"Med. Phys."},{"key":"ref_88","first-page":"131","article-title":"Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device","volume":"6142","author":"Mertelemeier","year":"2006","journal-title":"Proc. SPIE"},{"key":"ref_89","unstructured":"Chen, Y., Lo, J.Y., and Dobbins, J.T. (December, January 28). Matrix Inversion Tomosynthesis (MITS) of the Breast: Preliminary Results. Proceedings of the RSNA 90th Scientific Assembly, Chicago, IL, USA."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1118\/1.1543934","article-title":"Tomographic mammography using a limited number of low-dose cone-beam projection images","volume":"30","author":"Wu","year":"2003","journal-title":"Med. Phys."},{"key":"ref_91","first-page":"355","article-title":"Tomosynthesis Reconstruction Using an Accelerated Expectation Maximization Algorithm with Novel Data Structure Based on Sparse Matrix Ray-Tracing Method","volume":"1","author":"Zhou","year":"2008","journal-title":"Int. J. Funct. Inform. Pers. Med."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/42.20361","article-title":"Algebraic reconstruction in CT from limited views","volume":"8","author":"Andersen","year":"1989","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1118\/1.2237543","article-title":"A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis","volume":"33","author":"Zhang","year":"2006","journal-title":"Med. Phys."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"041910","DOI":"10.1118\/1.4868457","article-title":"MR-based motion correction for PET imaging using wired active MR microcoils in simultaneous PET-MR: Phantom study","volume":"41","author":"Huang","year":"2014","journal-title":"Med. Phys."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TCI.2015.2431913","article-title":"TIMBIR: A method for time-space reconstruction from interlaced views","volume":"1","author":"Mohan","year":"2015","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_96","first-page":"181","article-title":"Separable models for cone-beam MBIR reconstruction","volume":"15","author":"Balke","year":"2018","journal-title":"Electron. Imaging"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Majee, S., Balke, T., Kemp, C.A., Buzzard, G.T., and Bouman, C.A. (2019, January 15\u201317). 4D X-ray CT reconstruction using multi-slice fusion. Proceedings of the 2019 IEEE International Conference on Computational Photography (ICCP), Tokyo, Japan.","DOI":"10.1109\/ICCPHOT.2019.8747328"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/TCI.2017.2690143","article-title":"A model-based iterative reconstruction approach to tunable diode laser absorption tomography","volume":"3","author":"Nadir","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"10","DOI":"10.2352\/ISSN.2470-1173.2017.17.COIMG-417","article-title":"A model-based neuron detection approach using sparse location priors","volume":"17","author":"Majee","year":"2017","journal-title":"Electron. Imaging"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Ziabari, A., Ye, D.H., Sauer, K.D., Thibault, J., and Bouman, C.A. (2018, January 28\u201331). 2.5D deep learning for CT image reconstruction using a multi-GPU implementation. Proceedings of the 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2018.8645364"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"11824","DOI":"10.1038\/srep11824","article-title":"The three-dimensional morphology of growing dendrites","volume":"5","author":"Gibbs","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3197517.3201298","article-title":"Space-time tomography for continuously deforming objects","volume":"37","author":"Zang","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_103","unstructured":"Kisner, S.J., Haneda, E., Bouman, C.A., Skatter, S., Kourinny, M., and Bedford, S. (2012, January 24\u201327). Model-based CT reconstruction from sparse views. Proceedings of the Second International Conference on Image Formation in X-ray Computed Tomography, Salt Lake City, UT, USA."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/78.193196","article-title":"A local update strategy for iterative reconstruction from projections","volume":"41","author":"Sauer","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_105","first-page":"574","article-title":"Convolutional regularization methods for 4D, X-ray CT reconstruction","volume":"10948","author":"Clark","year":"2019","journal-title":"Phys. Med. Imaging"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TCI.2016.2599778","article-title":"Plug-and-play priors for bright field electron tomography and sparse interpolation","volume":"2","author":"Sreehari","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Venkatakrishnan, S.V., Bouman, C.A., and Wohlberg, B. (2013, January 3\u20135). Plug-and-play priors for model-based reconstruction. Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA.","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TCI.2019.2893568","article-title":"An online plug-and-play algorithm for regularized image reconstruction","volume":"5","author":"Sun","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/LSP.2017.2763583","article-title":"A plug-and-play priors approach for solving nonlinear imaging inverse problems","volume":"24","author":"Kamilov","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3D transform-domain collaborative filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_111","unstructured":"Maggioni, M., Boracchi, G., Foi, A., and Egiazarian, K. (2011). Image Processing: Algorithms and Systems IX, SPIE."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1137\/17M1122451","article-title":"Plug-and-play unplugged: Optimization-free reconstruction using consensus equilibrium","volume":"11","author":"Buzzard","year":"2018","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_113","unstructured":"Sun, Y., Wohlberg, B., and Kamilov, U.S. (2018). Plug-in stochastic gradient method. arXiv."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Sun, Y., Xu, S., Li, Y., Tian, L., Wohlberg, B., and Kamilov, U.S. (2019, January 12\u201317). Regularized Fourier ptychography using an online plug-and-play algorithm. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683057"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/83.491321","article-title":"A unified approach to statistical tomography using coordinate descent optimization","volume":"5","author":"Bouman","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/42.222671","article-title":"Maximum a posteriori estimation for SPECT using regularization techniques on massively parallel computers","volume":"12","author":"Butler","year":"1993","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_117","unstructured":"Foley, J., van Dam, A., Feiner, S., and Hughes, J. (1990). Computer Graphics: Principles and Practice, Addison-Wesley."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1088\/0031-9155\/37\/3\/015","article-title":"Alternatives to voxels for image representation in iterative reconstruction algorithms","volume":"37","author":"Lewitt","year":"1992","journal-title":"Phys. Med. Biol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"100931","DOI":"10.1016\/j.aei.2019.100931","article-title":"Improving reconstruction of tunnel lining defects from ground-penetrating radar profiles by multi-scale inversion and bi-parametric full-waveform inversion","volume":"41","author":"Feng","year":"2019","journal-title":"Adv. Eng. Inform."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1093\/gji\/ggt528","article-title":"Two-dimensional permittivity and conductivity imaging by full waveform inversion of multioffset GPR data: A frequency-domain quasi-Newton approach","volume":"197","author":"Brossier","year":"2014","journal-title":"Geophys. J. Int."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.autcon.2016.03.011","article-title":"Underground structure defect detection and reconstruction using crosshole GPR and Bayesian waveform inversion","volume":"68","author":"Qin","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Watson, F. (2016, January 2). Towards 3D full-wave inversion for GPR. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485323"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/LGRS.2020.2976146","article-title":"Multiparameter full-waveform inversion of 3-D on-ground GPR with a modified total variation regularization scheme","volume":"18","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"6349","DOI":"10.1109\/TAP.2022.3177556","article-title":"Artificial intelligence: New frontiers in real-time inverse scattering and electromagnetic imaging","volume":"70","author":"Salucci","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2528\/PIER20030705","article-title":"A review of deep learning approaches for inverse scattering problems (invited review)","volume":"167","author":"Chen","year":"2020","journal-title":"Prog. Electromagn. Res."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1109\/LAWP.2019.2916369","article-title":"DNNs as applied to electromagnetics, antennas, and propagation\u2014A review","volume":"18","author":"Massa","year":"2019","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"120371","DOI":"10.1016\/j.conbuildmat.2020.120371","article-title":"Advances of deep learning applications in ground-penetrating radar: A survey","volume":"258","author":"Tong","year":"2020","journal-title":"Constr. Build Mater."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.aci.2018.10.001","article-title":"Artificial neural networks and machine learning techniques applied to ground penetrating radar: A review","volume":"17","author":"Travassos","year":"2021","journal-title":"Appl. Comput. Inform."},{"key":"ref_129","first-page":"385","article-title":"Deep convolutional neural networks for classifying GPR B-scans","volume":"9454","author":"Besaw","year":"2015","journal-title":"Proc. SPIE"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"102839","DOI":"10.1016\/j.autcon.2019.102839","article-title":"Automatic hyperbola detection and fitting in GPR B-scan image","volume":"106","author":"Lei","year":"2019","journal-title":"Automat. Constr."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TGRS.2020.2984951","article-title":"Landmine detection using autoencoders on multipolarization GPR volumetric data","volume":"59","author":"Bestagini","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_132","first-page":"1","article-title":"The orientation estimation of elongated underground objects via multipolarization aggregation and selection neural network","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_133","first-page":"5108716","article-title":"Estimating parameters of the tree root in heterogeneous soil environments via mask-guided multi-polarimetric integration neural network","volume":"20","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Alvarez, J.K., and Kodagoda, S. (June, January 31). Application of deep learning image-to-image transformation networks to GPR radar-grams for sub-surface imaging in infrastructure monitoring. Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China.","DOI":"10.1109\/ICIEA.2018.8397788"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"287","DOI":"10.2113\/JEEG19-074","article-title":"\u00dc-Net: Deep-learning schemes for ground penetrating radar data inversion","volume":"25","author":"Xie","year":"2021","journal-title":"J. Environ. Eng. Geophys."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"8305","DOI":"10.1109\/TGRS.2020.3046454","article-title":"GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/JSEN.2021.3050618","article-title":"Deep neural network-based permittivity inversions for ground penetrating radar data","volume":"21","author":"Ji","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/313541a0","article-title":"Lower mantle heterogeneity, dynamic topography, and the geoid","volume":"313","author":"Hager","year":"1985","journal-title":"Nature"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1098\/rsta.1996.0055","article-title":"Magnetoconvection and thermal coupling of the Earth\u2019s core and mantle","volume":"354","author":"Olsen","year":"1996","journal-title":"Phil. Trans. R. Soc."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/94RG03020","article-title":"Three-dimensional seismic models of the Earth\u2019s mantle","volume":"33","author":"Ritzwoller","year":"1995","journal-title":"Rev. Geophys."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0012-821X(96)00134-3","article-title":"Constraints on the physical properties of the mantle from seismology and mineral physics","volume":"143","author":"Robertson","year":"1996","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/360149a0","article-title":"Deep origin of mid-oceanic ridge seismic velocity anomalies","volume":"360","author":"Su","year":"1992","journal-title":"Nature"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"15877","DOI":"10.1029\/94JB00853","article-title":"Effects of multiple phase transitions in a 3-D spherical model of convection in the Earth\u2019s mantle","volume":"99","author":"Tackley","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_144","first-page":"1","article-title":"New geodynamical constraints from seismic tomography","volume":"143","author":"Woodhouse","year":"1996","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s43017-019-0003-8","article-title":"Seismic wavefield imaging of Earth\u2019s interior across scales","volume":"1","author":"Tromp","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_146","unstructured":"Nocedal, J., and Wright, S. (2006). Numerical Optimization, Springer. [2nd ed.]."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Biegler, L., Ghattas, O., Heinkenschloss, M., and Van Bloemen Waanders, B. (2003). Large-Scale PDE Constrained Optimization, Springer.","DOI":"10.1007\/978-3-642-55508-4"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Igel, H. (2016). Computational Seismology, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780198717409.001.0001"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Lions, J.L., and Magenes, E. (1972). Non-Homogeneous Boundary Value Problems and Applications, Springer.","DOI":"10.1007\/978-3-642-65217-2"},{"key":"ref_150","unstructured":"Goodson, R.E., and Polis, M.P. (1974). Identification of Parameter Distributed Systems, American Society of Mechanical Engineers."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1111\/j.1365-246X.2006.02978.x","article-title":"A review of the adjoint-state method for computing the gradient of a functional with geophysical applications","volume":"167","author":"Plessix","year":"2006","journal-title":"Geophys. J. Int."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1093\/gji\/ggw014","article-title":"Measuring the misfit between seismograms using an optimal transport distance: Application to full waveform inversion","volume":"205","author":"Brossier","year":"2016","journal-title":"Geophys. J. Int."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"R429","DOI":"10.1190\/geo2015-0387.1","article-title":"Adaptive waveform inversion: Theory","volume":"81","author":"Warner","year":"2016","journal-title":"Geophysics"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1190\/tle38030193.1","article-title":"Long-wavelength FWI updates in the presence of cycle skipping","volume":"38","author":"Qiu","year":"2019","journal-title":"Lead. Edge"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1007\/BF01589116","article-title":"On the limited memory BFGS method for large scale optimization","volume":"45","author":"Liu","year":"1989","journal-title":"Math. Program."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1137\/0801023","article-title":"A numerical study of the limited memory BFGS method and the truncated Newton method for large scale optimization","volume":"1","author":"Nash","year":"1991","journal-title":"SIAM J. Optim."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1137\/0803029","article-title":"Numerical experience with limitedmemory quasi-Newton and truncated Newton methods","volume":"3","author":"Zou","year":"1993","journal-title":"SIAM J. Optim."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2017.11.003","article-title":"Optical coherence tomography angiography","volume":"64","author":"Spaide","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"Huang","year":"1991","journal-title":"Science"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1364\/OE.11.002183","article-title":"Sensitivity advantage of swept-source and Fourier-domain optical coherence tomography","volume":"11","author":"Choma","year":"2003","journal-title":"Opt. Express"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0030-4018(95)00119-S","article-title":"Measurement of Intraocular Distances by Backscattering Spectral Interferometry","volume":"117","author":"Fercher","year":"1995","journal-title":"Opt. Commun."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1117\/1.429899","article-title":"Coherence Radar\u201d and \u201cSpectral Radar\u201d\u2014New Tools for Dermatological Diagnosis","volume":"3","author":"Lindner","year":"1998","journal-title":"J. Biomed. Opt."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1117\/1.1482379","article-title":"In vivo human retinal imaging by Fourier domain optical coherence tomography","volume":"7","author":"Wojtkowski","year":"2002","journal-title":"J. Biomed. Opt."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1364\/OL.27.001415","article-title":"Full range complex spectral optical coherence tomography technique in eye imaging","volume":"27","author":"Wojtkowski","year":"2002","journal-title":"Opt. Lett."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1364\/OL.22.000340","article-title":"Optical coherence tomography using a frequency-tunable optical source","volume":"22","author":"Chinn","year":"1997","journal-title":"Opt. Lett."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1364\/OL.22.001704","article-title":"Optical frequency-domain reflectometry using rapid wavelength tuning of a Cr4+:forsterite laser","volume":"22","author":"Golubovic","year":"1997","journal-title":"Opt. Lett."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"6548","DOI":"10.1364\/AO.36.006548","article-title":"Wavelength-tuning interferometry of intraocular distances","volume":"36","author":"Lexer","year":"1997","journal-title":"Appl. Opt."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1117\/1.429889","article-title":"Chirp Optical Coherence Tomography of Layered Scattering Media","volume":"3","author":"Haberland","year":"1998","journal-title":"J. Biomed. Opt."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"8928","DOI":"10.3390\/s130708928","article-title":"Dental Optical Coherence Tomography","volume":"13","author":"Hsieh","year":"2013","journal-title":"Sensors"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1364\/OE.3.000239","article-title":"In vivo OCT imaging of hard and soft tissue of the oral cavity","volume":"3","author":"Feldchtein","year":"1998","journal-title":"Opt. Express"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1364\/AO.38.002092","article-title":"Characterization of dentin and enamel by use of optical coherence tomography","volume":"38","author":"Wang","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"511","DOI":"10.14219\/jada.archive.2000.0210","article-title":"Optical coherence tomography: A new imaging technology for dentistry","volume":"131","author":"Otis","year":"2000","journal-title":"J. Am. Dent. Assoc."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"13365","DOI":"10.1364\/OE.24.013365","article-title":"MEMS-based handheld scanning probe with pre-shaped input signals for distortion-free images in Gabor-Domain Optical Coherence Microscopy","volume":"24","author":"Cogliati","year":"2016","journal-title":"Opt. Express"},{"key":"ref_174","unstructured":"Hong, Y., Zhang, K., Gu, J., Sai Bi, S., Zhou, Y., Liu, D., Liu, F., Sunkavalli, K., Bui, T., and Tan, H. (2023). LRM: Large Reconstruction Model for Single Image to 3D. arXiv."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Strecha, C., Pylv\u00e4n\u00e4inen, T., and Pascal Fua, P. (2010, January 13\u201318). Dynamic and scalable large scale image reconstruction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540184"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"5949","DOI":"10.1088\/0031-9155\/56\/18\/011","article-title":"Low-dose CT reconstruction via edge-preserving total variation regularization","volume":"56","author":"Tian","year":"2011","journal-title":"Phys. Med. Biol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"7657","DOI":"10.1002\/mp.15101","article-title":"Low-dose CT reconstruction with Noise2Noise network and testing-time fine-tuning","volume":"48","author":"Wu","year":"2021","journal-title":"Med Phys."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/2\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:55:19Z","timestamp":1760104519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/2\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,5]]},"references-count":177,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["a17020071"],"URL":"https:\/\/doi.org\/10.3390\/a17020071","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,5]]}}}