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**KTH Royal Institute of Technology**

*SE-100 44 Stockholm Sweden +46 8 790 60 00*

[1]

T. Buddenkotte *et al.*, "Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation," *Computers in Biology and Medicine*, vol. 163, 2023.

[2]

L. E. Sanchez *et al.*, "Integrating Artificial Intelligence Tools in the Clinical Research Setting : The Ovarian Cancer Use Case," *Diagnostics*, vol. 13, no. 17, 2023.

[3]

C. Esteve-Yague *et al.*, "Spectral decomposition of atomic structures in heterogeneous cryo-EM," *Inverse Problems*, vol. 39, no. 3, pp. 034003, 2023.

[4]

J. Rudzusika, T. Koehler and O. Öktem, "Deep Learning-Based Dictionary Learning and Tomographic Image Reconstruction," *SIAM Journal on Imaging Sciences*, vol. 15, no. 4, pp. 1729-1764, 2022.

[5]

H. Andrade-Loarca *et al.*, "Deep microlocal reconstruction for limited-angle tomography," *Applied and Computational Harmonic Analysis*, vol. 59, pp. 155-197, 2022.

[6]

G. Zickert, O. Öktem and C. E. Yarman, "Joint Gaussian dictionary learning and tomographic reconstruction," *Inverse Problems*, vol. 38, no. 10, 2022.

[7]

E. Ström *et al.*, "Photon-Counting CT Reconstruction With a Learned Forward Operator," *IEEE Transactions on Computational Imaging*, vol. 8, pp. 536-550, 2022.

[8]

J. Adler *et al.*, "Task adapted reconstruction for inverse problems," *Inverse Problems*, vol. 38, no. 7, 2022.

[9]

C. Chen *et al.*, "An efficient algorithm to compute the X-ray transform," *International Journal of Computer Mathematics*, 2021.

[10]

D. Kimanius *et al.*, "Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination," *IUCrJ*, vol. 8, pp. 60-75, 2021.

[11]

A. Aspri *et al.*, "A Data-Driven Iteratively Regularized Landweber Iteration," *Numerical Functional Analysis and Optimization*, 2020.

[12]

S. Banert *et al.*, "Data-driven nonsmooth optimization," *SIAM Journal on Optimization*, vol. 30, no. 1, pp. 102-131, 2020.

[13]

B. Gris, C. Chen and O. Öktem, "Image reconstruction through metamorphosis," *Inverse Problems*, vol. 36, no. 2, 2020.

[14]

A. Hauptmann *et al.*, "Multi-Scale Learned Iterative Reconstruction," *IEEE Transactions on Computational Imaging*, vol. 6, pp. 843-856, 2020.

[15]

H. Andrade-Loarca, G. Kutyniok and O. Öktem, "Shearlets as feature extractor for semantic edge detection : the model-based and data-driven realm," *Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences*, vol. 476, no. 2243, 2020.

[16]

C. Chen, B. Gris and O. Öktem, "A new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging," *SIAM Journal on Imaging Sciences*, vol. 12, no. 4, pp. 1686-1719, 2019.

[17]

H. Andrade-Loarca *et al.*, "Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks," *SIAM Journal on Imaging Sciences*, vol. 12, no. 4, pp. 1936-1966, 2019.

[18]

M. Siadat *et al.*, "Joint Image Deconvolution and Separation Using Mixed Dictionaries," *IEEE Transactions on Image Processing*, vol. 28, no. 8, pp. 3936-3945, 2019.

[19]

S. Arridge *et al.*, "Solving inverse problems using data-driven models," *Acta Numerica*, vol. 28, pp. 1-174, 2019.

[20]

J. Bergstrand *et al.*, "Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells," *Nanoscale*, vol. 11, no. 20, pp. 10023-10033, 2019.

[21]

L. F. Lang *et al.*, "Template-Based Image Reconstruction from Sparse Tomographic Data," *Applied mathematics and optimization*, 2019.

[22]

C. Chen and O. Öktem, "Indirect image registration with large diffeomorphic deformations," *SIAM Journal on Imaging Sciences*, vol. 11, no. 1, pp. 575-617, 2018.

[23]

J. Adler and O. Öktem, "Learned Primal-Dual Reconstruction," *IEEE Transactions on Medical Imaging*, vol. 37, no. 6, pp. 1322-1332, 2018.

[24]

M. Siadat, N. Aghazadeh and O. Öktem, "Reordering for improving global Arnoldi-Tikhonov method in image restoration problems," *Signal, Image and Video Processing*, vol. 12, no. 3, pp. 497-504, 2018.

[25]

A. H. Tavabi *et al.*, "Tunable Ampere phase plate for low dose imaging of biomolecular complexes," *Scientific Reports*, vol. 8, 2018.

[26]

M. Reuss *et al.*, "Measuring true localization accuracy in super resolution microscopy with DNA-origami nanostructures," *New Journal of Physics*, vol. 19, no. 2, 2017.

[27]

O. Öktem *et al.*, "Shape-based image reconstruction using linearized deformations," *Inverse Problems*, vol. 33, no. 3, 2017.

[28]

J. Adler and O. Öktem, "Solving ill-posed inverse problems using iterative deep neural networks," *Inverse Problems*, vol. 33, no. 12, 2017.

[29]

S. Hahn *et al.*, "Spectral transfer from phase to intensity in Fresnel diffraction," *PHYSICAL REVIEW A*, vol. 93, no. 5, 2016.

[30]

M. Vulovic *et al.*, "Image formation modeling in cryo-electron microscopy," *Journal of Structural Biology*, vol. 183, no. 1, pp. 19-32, 2013.

[31]

A. Gopinath *et al.*, "Shape-based regularization of electron tomographic reconstruction," *IEEE Transactions on Medical Imaging*, vol. 31, no. 12, pp. 2241-2252, 2012.

[32]

H. Rullgard *et al.*, "Simulation of transmission electron microscope images of biological specimens," *Journal of Microscopy*, vol. 243, no. 3, pp. 234-256, 2011.

[33]

O. Öktem, E. T. Quinto and U. Skoglund, "Electron Lambda-tomography," *Proceedings of the National Academy of Sciences of the United States of America*, vol. 106, no. 51, pp. 21842-21847, 2009.

[34]

L. Norlén, O. Öktem and U. Skoglund, "Molecular cryo-electron tomography of vitreous tissue sections : current challenges," *Journal of Microscopy*, vol. 235, no. 3, pp. 293-307, 2009.

[35]

O. Öktem and D. Fanelli, "Electron tomography : A short overview with an emphasis on the absorption potential model for the forward problem," *Inverse Problems*, vol. 24, no. 1, pp. 013001, 2008.

[36]

O. Öktem and E. T. Quinto, "Local tomography in electron microscopy," *SIAM Journal on Applied Mathematics*, vol. 68, no. 5, pp. 1282-1303, 2008.

[37]

O. Öktem, H. Rullgård and U. Skoglund, "A component-wise iterated relative entropy regularization method with updated prior and regularization parameter," *Inverse Problems*, vol. 23, no. 5, pp. 2121-2139, 2007.

[38]

O. Öktem and E. T. Quinto, "Inversion of the X-ray transform from limited angle parallel beam region of interest data with applications to electron tomography," *Proceedings in Applied Mathematics and Mechanics : PAMM*, vol. 7, no. 1, pp. 1050301-1050302, 2007.

[39]

A. Eguizabal, O. Öktem and M. Persson, "A deep learning one-step solution to material image reconstruction in photon counting spectral CT," in *Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging*, 2022.

[40]

S. Mukherjee, O. Öktem and C. -. Schönlieb, "Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems," in *8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021*, 2021, pp. 540-552.

[41]

S. Mukherjee *et al.*, "End-to-end reconstruction meets data-driven regularization for inverse problems," in *Advances in Neural Information Processing Systems*, 2021, pp. 21413-21425.

[42]

O. Öktem, C. Pouchol and O. Verdier, "Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation," in *2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019*, 2019, pp. 151-162.

[43]

S. Lunz, O. Öktem and C. -. Schönlieb, "Adversarial regularizers in inverse problems," in *Advances in Neural Information Processing Systems*, 2018, pp. 8507-8516.

[44]

G. Dong *et al.*, "Infinite dimensional optimization models and PDEs for dejittering," in *5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015*, 2015, pp. 678-689.

[45]

A. Ringh *et al.*, "High-level algorithm prototyping : An example extending the TVR-DART algorithm," in *Discrete Geometry for Computer Imagery : 20th IAPR International Conference, DGCI 2017, Vienna, Austria, September 19 – 21, 2017, Proceedings, * : Springer, 2017, pp. 109-121.

[46]

O. Öktem, "Reconstruction methods in electron tomography," in *Mathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation Therapy (IMRT), *Y. Censor, Jiang M., and Louis A. K. Ed., : Springer Berlin/Heidelberg, 2008, pp. 289-320.

[47]

S. Mukherjee *et al.*, "Learned Reconstruction Methods With Convergence Guarantees : A survey of concepts and applications," *IEEE signal processing magazine (Print)*, vol. 40, no. 1, pp. 164-182, 2023.

[48]

E. T. Quinto, Ö. Ozan and U. Skoglund, "Reply to Wang and Yu : Both electron lambda tomography and interior tomography have their uses," *Proceedings of the National Academy of Sciences of the United States of America*, vol. 107, no. 22, pp. E94-E95, 2010.

[49]

A. Eguizabal, M. Persson and O. Öktem, "Learned Material Decomposition for Photon Counting CT," in *Proceedings of the 16th Virtual International Meeting onFully 3D Image Reconstruction inRadiology and Nuclear Medicine*, 2021, pp. 15-19.

[50]

O. Öktem, "Mathematics of electron tomography," in *Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition, * : Springer, 2015, pp. 937-1031.

[51]

L. Norlén, J. Anwar and O. Öktem, "Accessing the molecular organization of the stratum corneum using high-resolution electron microscopy and computer simulation," in *Computational Biophysics of the Skin, * : Pan Stanford Publishing, 2014, pp. 289-330.

[52]

[53]

[54]

[55]

J. Bergstrand *et al.*, "Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells," (Manuscript).

[56]

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2023-11-28 01:05:29

**KTH Royal Institute of Technology**

*SE-100 44 Stockholm Sweden +46 8 790 60 00*