Nikos

Nikolaos Dikaios, Ph.D (Cantab)

Physicist, Computer Scientist interested in tomography, inverse problems, cancer informatics and physics.

open researcher id

About me

I am a Research Scientist (Associate Professor level) at the Mathematics Research Center, Academy of Athens. I completed my DPhil (2012) in medical physics from the University of Cambridge and worked in Magnetic Resonance Imaging (MRI) as a research associate at UCL until 2016. Prior to my appointment at the Academy of Athens in 2020 I was an Assistant Professor (tenure), at the department of Electrical Engineering, University of Surrey where I am currently a Visiting Assistant Professor. Since 2018, I am a Fellow of the Higher Educational Academy from the UK Professional Standards Framework for teaching and learning support in higher education (Recognition reference: PR158316). Further, since 2024, I am a member of the Scientific Council of the Research Centers of the Academy of Athens. My research interests include tomography, inverse problems, cancer informatics and physics, where to date (12/2023) I have published 56 peer-reviewed papers (12 as first author, 3 as single author and 7 as last author), 1 book chapter and >30 refereed proceedings at major international conferences.

In 2019 I have been awarded a Royal Society Fellowship to work with Elekta on the world's first linear accelerator integrated with high field Magnetic Resonance Imaging (MRI). The aim was to perform real- time visualization for truly adaptive radiotherapy, with a potential to monitor tumour response, so that treatment can be adjusted accordingly. I have also been awarded (2017) with the Engineering and Physical Sciences Research Council (EPSRC) first grant for the most promising UK researchers to work on imaging methods to optimize cancer treatment with high energy proton beams. My work in prostate cancer detection using multi-parametric MRI has been awarded twice with the Summa Cum Laude (top 5%) and once with Magna Cum Laude (top 15%) from the flagship conference in MRI (ISMRM) with more than 6000 attendees every year.

News

01/2024: This week we had the opportunity to deliver a lecture on demystifying artificial intelligence at the Arsakeio school.

01/2024: New review paper on the role of attention mechanisms in medical image analysis

12/2023: New paper on large -scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies.

08/2023: New paper proposing an algebraic formula, deep learning and a novel SEIR-type model for the COVID-19 pandemic.

08/2023: New paper proposing an automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography Images.

07/2023: This week we had the opportunity to participate at the 29th school-conference on dynamical systems and complexity at NCSR “DEMOKRITOS”. radio genomics

05/2023: New paper proposing numerical computation of Neumann controls for the heat equation on a finite Interval.

05/2023: New paper discussing the digital transformation of cancer care and the role of big data, AI, and data-driven interventions.

04/2023: Examining a UCL PhD candidate on prescription dose optimization for personalized radiotherapy.

02/2023: New paper proposing a novel particle imaging filter.

01/2023: New paper assessing a range of OCTA microvascular metrics at rest and after post-occlusive reactive hyperaemia in the hands and feet of healthy and diabetic people.

01/2023: New paper on motion compensated PET image reconstruction via separable parabolic surrogates

12/2022: New paper on the decline of common femoral artery flow-mediated dilation and wall shear stress with age in healthy subjects

12/2022: New paper on algebraic activation functions for artificial neural networks based on solutions of a Riccati equation

07/2022: Join us in the 28th Summer School for Dynamical Systems and Complexity

04/2022: New paper on sparse-input neural networks to differentiate 32 primary cancer types on the basis of somatic point mutations

04/2022: New paper on simple formulae, deep learning and elaborate modelling for the covid-19 pandemic

04/2022: New paper on the reconstruction of preclinical PET Images via Chebyshev polynomial approximation of the sinogram

02/2022: Launching a new Special Issue in Diagnostics: Artificial Intelligence for Magnetic Resonance Imaging.

02/2022: New paper on the use of deep learning model for lung cancer lesion segmentation on PET/CT images

01/2022: New paper on the use of discrete shearlets as a sparsifying transform in low-rank plus sparse decomposition for undersampled (k, t)-space MR data

12/2021: New paper on an open-source toolbox for quantitative analysis of optical coherence tomography angiography images

08/2021: Book chapter in piecewise polynomial inversion of the Radon transform in 3D.

07/2021: New paper on identifying nodal disease using pretreatment micro-structural diffusion MRI characteristics in lymph nodes.

06/2021: Launching a new Special Issue in Applied Sciences: Applications of Artificial Intelligence in Medical Imaging.

06/2021: Joined the editorial board of the "Applied Sciences" journal.

05/2021: New paper that mathematically predicts the number of Covid-19 deaths during lockdown and possible scenarios for the post-lockdown period.

05/2021: Released python scripts on tracer kinetic modelling in DCE MRI.

05/2021: New paper on new analytical transformations for radial MR Fingerprinting k-space data.

04/2021: Joined the editorial board of the "Artificial Intelligence in Cancer" journal.

03/2021: New paper using machine learning to track proton paths and reconstruct relative stopping power maps in proton computed tomography.

03/2021: New paper on statistical limitations of ion imaging to produce high quality relative stopping power maps for radiotherapy planning.

02/2021: New paper on an end-to-end assessment of the accuracy of adaptive radiotherapy in an Elekta MR-linac.

01/2021: New paper using augmented synthetic 1H magnetic resonance spectra to train convolutional neural networks on brain tumour classification.

Updates

Current projects

  1. Research new multiparametric magnetic resonance fingerprinting pulse sequences aiming to robustly quantify tissue properties. magnetic resonance

    Figure: Analytical transformations for radial MR Fingerprinting
  2. Cancer mutation network aiming to link types of tumors with driver mutations. cluster analysis

    Figure: This figure is a view of the cancer network in unified clusters.
  3. Radio-genomics to monitor the response of novel cancer therapies. radio genomics

Main scientific contributions

  1. Deep learning to model the complex nonlinear relationship between diseases and their MR spectroscopic metabolic fingerprint (pattern) (doi: 10.1002/nbm.4479).
  2. Proton path tracking using physics models and machine learning algorithms (doi(s): 10.1088/1361-6560/ab9413, 10.1088/1361-6560/abf1fd) as part of my EPSRC grant.
  3. Proposed a direct estimation of parameters related to magnetic resonance properties and tissue vasculature from k-space measurements in dynamic contrast enhanced MRI (doi(s): 10.1016/j.media.2014.05.0, 10.1016/j.media.20).
  4. Machine learning models and radiomics aiming to improve the detection and characterization of prostate cancer and help radiologists classify areas that were scored as indeterminate for cancer. (doi(s):10.1007/s00330-014-3386-4, 10.1007/s00330-016-4579-9, 10.1007/s00330-019-06244-2).
  5. Showcased that the magnetic resonance imaging phenotype between the peripheral and the transition zone in the prostate is different and zone-specific diagnostic models are needed (doi(s): 10.1007/s00330-015-3636-0, 10.1007/s00330-018-5799-y).
  6. A noise estimation algorithm from averaged diffusion weighted magnetic resonance images, which included a new closed form approximation to Rician sum distributions to model the expected noise distribution (doi: 10.1002/mrm.24877).
  7. Dynamic 2D and 3D magnetic resonance imaging (MRI) acquisition protocols suitable for MRI based motion correction in positron emission tomography –proposed a binning algorithm of dynamic 2D MRI into dynamic 3D MRI (doi: 10.1007/s00330-011-2274-4).
  8. Mathematically derived a 4D expectation maximization algorithm suitable for motion compensated image reconstruction in positron emission tomography (doi: 10.1118/1.3611041).
  9. An analytical Compton scatter simulation algorithm of photons in tissue, which is included in STIR (doi: 10.1109/NSSMIC.2006.354339).
2021-2023 Dr. Nicholas E. Protonotarios is a senior research associate at the Mathematics Research Center, Academy of Athens and the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. His main research interests include the mathematics of medical imaging and mathematical image reconstruction in PET, SPECT and MRI. He currently works on radio-genomics aiming to monitor the response of novel cancer therapies.

2021-present Stamatis Choudalakis is Mathematician graduate pursuing his PhD in bioinformatics after completing his Masters in applied mathematics in 2023. He is currently working on novel Network Graphs that can reveal the relationships between different types of tumours and cancer genes.

2020-present Evangelia Tzampazidou is a PhD student at the Mathematics Research Center of the Academy of Athens. Her aim is to explore novel image reconstruction methods for undersampled Magnetic Resonance Imaging data.
funding

Funding

Principal Investigator

Co-Investigator

Honors and awards

teaching

Courses

Supervision of PhD students and postdoctoral fellows

PhD examinations

teaching google scholar

Books

  1. NE Protonotarios, GA Kastis, N Dikaios, AS Fokas. (2021) Piecewise Polynomial Inversion of the Radon Transform in Three Space Dimensions via Plane Integration and Applications in Positron Emission Tomography. Nonlinear Analysis, Differential Equations, and Applications. Springer Optimization and Its Applications, vol 173. Springer, Cham. (https://doi.org/10.1007/978-3-030-72563-1_17)

Peer reviewed papers

Single author

  1. N Dikaios. Sparse-Input Neural Networks to Differentiate 32 Primary Cancer Types on the Basis of Somatic Point Mutations. Onco. 2022; 2(2):56-68. (doi: 10.3390/onco2020005).
  2. N Dikaios. Deep learning Magnetic Resonance Spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data. NMR in Biomedicine. 2021; 34: 4479 (doi: 10.1002/nbm.4479).
  3. N Dikaios. Stochastic Gradient Langevin dynamics for joint parameterization of tracer kinetic models, input functions, and T1 relaxation-times from undersampled k-space DCE-MRI. Med Image Anal. 2020; 62:101690 (doi: 10.1016/j.media.2020.101690).

First author

  1. N Dikaios, NE Protonotarios, AS Fokas, GA Kastis. Quantification of T1, T2 relaxation times from magnetic resonance fingerprinting radially undersampled data using analytical transformations. Magn Reson Imaging, 2021; 80: 81:89 (doi: 10.1016/j.mri.2021.04.013).
  2. N Dikaios, F Giganti, HS Sidhu, …, S Punwani. Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer. Eur. Radiol. 2019; 29(8), 4150-4159 (doi: 10.1007/s00330-018-5799-y)
  3. N Dikaios, D Atkinson, C Tudisca, P Purpura, M Forster, H Ahmed, T Beale, M Emberton, S Punwani. A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis. Comput Med Imaging Graph. 2017; 56:1-10 (doi: 10.1016/j.compmedimag.2017.01.003).
  4. N Dikaios, J Alkalbani, M Abd-Alazeez,…, S Punwani: Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI. Eur. Radiol. 2015; 25(9):2727-37 (doi: 10.1007/s00330-015-3636-0).
  5. N Dikaios, J Alkalbani, HS Sidhu,…, S Punwani: Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI. Eur Radiol. 2015; 25(2):523-32 (doi: 10.1007/s00330-014-3386-4).
  6. N Dikaios, Simon Arridge, V Hamy, S Punwani, D Atkinson: Direct parametric reconstruction from undersampled (k, t)-space data in dynamic contrast enhanced MRI. Med Image Anal. 2014; 18(7): 989-1001 (doi: 10.1016/j.media.2014.05.001).
  7. N Dikaios, S Punwani, V Hamy,…, D Atkinson: Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation? Magn Reson Med. 2013; 71(6):2105-17 (doi: 10.1002/mrm.24877).
  8. N Dikaios, TD Fryer: Registration-weighted motion correction for PET. Med Phys. 2012; 39(3):1253-64 (doi: 10.1118/1.3675922).
  9. N Dikaios, TD Fryer: Improved motion-compensated image reconstruction for PET using sensitivity correction per respiratory gate and an approximate tube-of-response backprojector. Med Phys. 2011; 38(9):4958-70 (doi: 10.1118/1.3611041).
  10. N Dikaios, TD Fryer: Acceleration of motion-compensated PET reconstruction: ordered subsets-gates EM algorithms and a priori reference gate information. Phys Med Biol. 2011; 56(6):1695-715 (doi: 10.1088/0031-9155/56/6/011).
  11. N Dikaios, D Izquierdo-Garcia, MJ Graves, V Mani, ZA Fayad, TD Fryer: MRI-based motion correction of thoracic PET: initial comparison of acquisition protocols and correction strategies suitable for simultaneous PET/MRI systems. Eur Radiol. 2011; 22(2):439-46 (doi: 10.1007/s00330-011-2274-4).
  12. N Dikaios, K Dinelle, T Spinks, K Nikita, K Thielemans: Processing of transmission data from an uncollimated single photon source. Nuclear Instruments and Methods in Physics Research Section A 2006; 569(2):416-420 (doi: 10.1016/j.nima.2006.08.104).

Last author

  1. K Kalimeris, T Özsarl and N Dikaios. Numerical Computation of Neumann controls for the Heat Equation on a Finite Interval. IEEE Transactions on Automatic Control. (doi: 10.1109/TAC.2023.3263753).
  2. NE Protonotarios, GA Kastis, AD Fotopoulos, AG Tzakos, D Vlachos, N Dikaios. Motion-Compensated PET Image Reconstruction via Separable Parabolic Surrogates. Mathematics. 2023; 11(1):55 (doi: 10.3390/math11010055).
  3. NE Protonotarios, AS Fokas, GA Kastis and N Dikaios, Sigmoid and Beyond: Algebraic Activation Functions for Artificial Neural Networks Based on Solutions of a Riccati Equation. IT Professional, 2022; 24(5), 30-36 (doi: 10.1109/MITP.2022.3204904)
  4. NE Protonotarios, E Tzampazidou, GA Kastis, N Dikaios. Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data. Journal of Imaging 2022; 8(2): 29 (doi: 10.3390/jimaging8020029).
  5. D Lazos, CA Collins-Fekete, M Bober, PM Evans, N Dikaios. Machine Learning for proton path tracking in proton Computed Tomography. Phys Med Biol. 2021; 66(10): 105013 (doi: 10.1088/1361-6560/abf1fd).
  6. D Lazos, CA Collins-Fekete, PM Evans, N Dikaios. Molière maximum likelihood proton path estimation approximated by cubic Bézier curve for scatter corrected proton CT reconstruction. Phys Med Biol. 2020; 65(17):175003 (doi: 10.1088/1361-6560/ab9413/meta).
  7. HA Aldaqadossi, H Khairy, Y Kotb, HA Hussein, H Shaker, N Dikaios. Prediction of pediatric PCNL outcomes using contemporary scoring systems. J Urol. 2017; 198(5):1146-1152 (doi: 10.1016/j.juro.2017.04.084).

Co-author

  1. G Papanastasiou, N Dikaios, J Huang, C Wang and G Yang. Is attention all you need in medical image analysis? A review. IEEE Journal of Biomedical and Health Informatics. (doi: 10.1109/JBHI.2023.3348436).
  2. G Papanastasiou, G Yang, DI Fotiadis, N Dikaios, …, D Palumbo. Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies. Commun Med 2023; 189(3). (doi: 10.1038/s43856-023-00412-8). I
  3. AS Fokas,N Dikaios, YC Yortsos. An algebraic formula, deep learning and a novel SEIR-type model for the COVID-19 pandemic. R. Soc. open sci. 2023; 10(8): 08-58. (doi: 10.1098/rsos.230858).
  4. MU Saeed, N Dikaios, A Dastgir, G Ali, M Hamid, F Hajjej. An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images. Diagnostics. 2023; 13(16):2658. (doi: 10.3390/diagnostics13162658).
  5. N Papachristou, G Kotronoulas, N Dikaios, SJ Allison, H Eleftherochorinou, T Rai, H Kunz, P Barnaghi, C Miaskowski, PD Bamidis . Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field. Seminars in Oncology Nursing. 2023, 151433 (doi: 10.1016/j.soncn.2023.151433).
  6. R Fullarton, L Volz, N Dikaios, G Royle, PM Evans, J Seco, CA Collins-Fekete. A likelihood-based particle imaging filter using prior information. Med Phys. 2023; 1- 18. (doi: 10.1002/mp.16258).
  7. GR Untracht, N Dikaios, AK Durrani, M Bapir, MV Sarunic, DD Sampson, C Heiss, DM Sampson. Pilot study of optical coherence tomography angiography-derived microvascular metrics in hands and feet of healthy and diabetic people. Sci Rep 2023; 13, 1122. (doi: 10.1038/s41598-022-26871-y).
  8. M Bapir, GR Untracht, JEA Hunt, JH McVey, J Harris, SS Skene, PCampagnolo, N Dikaios, A Rodriguez-Mateos, DD Sampson, DM Sampson, C Heiss. Age-Dependent Decline in Common Femoral Artery Flow-Mediated Dilation and Wall Shear Stress in Healthy Subjects. Life. 2022; 12(12):2023. (doi: 10.3390/life12122023).
  9. M Bapir, GR Untracht, D Cooke, JH McVey, SS Skene, P Campagnolo, MB Whyte, N Dikaios, A Rodriguez-Mateos, DD Sampson, DM Sampson, C Heiss. Cocoa flavanol consumption improves lower extremity endothelial function in healthy individuals and people with type 2 diabetes. Food Funct. 2022; 13:10439-10448. (doi: 10.1039/D2FO02017C).
  10. AS Fokas, N Dikaios, S Tsiodras, GA Kastis. Simple Formulae, Deep Learning and Elaborate Modelling for the COVID-19 Pandemic. Encyclopedia. 2022; 2(2):679-689. (doi: 10.3390/encyclopedia2020047).
  11. NE Protonotarios, AS Fokas, A Vrachliotis, V Marinakis, N Dikaios, GA Kastis. Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram. Applied Sciences. 2022; 12(7):3335. (doi: 10.3390/app12073335).
  12. NE Protonotarios, I Katsamenis, S Sykiotis, N Dikaios, GA Kastis, SN Chatziioannou, M Metaxas, N Doulamis, A Doulamis. A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging. Biomed Phys Eng Express. 2022; 8(2). (doi: 10.1088/2057-1976/ac53bd).
  13. GR Untracht, RS Matos, N Dikaios, M Bapir, AK Durrani, T Butsabong, P Campagnolo, DD Sampson, C Heiss, DM Sampson. OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images. PLoS One. 2021; 16(12):e0261052. (doi: 10.1371/journal.pone.0261052).
  14. MV Papoutsaki, HS Sidhu, N Dikaios, S Singh, D Atkinson, B Kanber, T Beale, S Morley, M Forster, D Carnell, R Mendes, S Punwani. Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers. NMR in Biomedicine. 2021 (doi: 10.1002/nbm.4587).
  15. CA Fekete, N Dikaios, G Royle, E Bar, P Evans. Statistical limitations in ion imaging Phys Med Biol. 2021; 65(8):085011 (doi: 10.1088/1361-6560/abee57).
  16. AS Fokas, N Dikaios, GA Kastis. Covid-19: Predictive Mathematical Formulas for the Number of Deaths During Lockdown and Possible Scenarios for the Post-Lockdown Period. Royal Society Proceedings A. 2021; 477(2249): 20200745 (doi: 10.1098/rspa.2020.0745).
  17. A Axford, N Dikaios, DA Roberts, CH Clark, PM Evans. An end-to-end assessment on the accuracy of adaptive radiotherapy in an MR-linac. Phys Med Biol. 2021; 66(10): 105009 (doi: 10.1088/1361-6560/abe053).
  18. AS Fokas, N Dikaios, GA Kastis. Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2 Journal of the Royal Society Interface. 2020: 17 (169), 0494 (doi: 10.1098/rsif.2020.0494).
  19. CA Fekete, N Dikaios, G Royle, P Evans. Statistical limitations in proton imaging Phys Med Biol. 2020; 65(8):085011 (doi: 10.1088/1361-6560/ab7972).
  20. M Antonelli, EW Johnston, N Dikaios, ..., S Punwani. Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists Eur. Radiol. 2019; 29(8), 4150-4159 (doi: 10.1007/s00330-019-06244-2).
  21. AM Brizmohun, HS Sidhu, N Dikaios, EW Johnston, LA Simmons, A Freeman, AP Kirkham, HU Ahmed, S Punwani. Characterizing indeterminate (Likert-score 3/5) peripheral zone prostate lesions with PSA density, PI-RADS scoring and qualitative descriptors on multiparametric MRI. Br J Radiol. 2018; 91 (1083): 20170645 (doi: 10.1259/bjr.20170645).
  22. A Latifoltojar, M Hall-Craggs, N Rabin, R Popat, A Bainbridge, N Dikaios, … , K Yong. Magnetic Resonance Imaging in newly diagnosed multiple myeloma: Early changes in lesional signal fat fraction predict disease response. British Journal of Haematology 2017; 176(2):222-233 (doi: 10.1111/bjh.14401).
  23. A Latifoltojar, A Hall-Craggs, A Bainbridge, K Yong, N Dikaios, … , S Punwani. Whole-body MRI quantitative biomarkers predict response in patients with newly diagnosed symptomatic multiple myeloma following Bortezomib induction. Eur. Radiol. 2017; 27(12): 5325-5336 (doi: 10.1007/s00330-017-4907-8).
  24. HS Sidhu, S Benigno, B Ganeshan, N Dikaios, … , S Punwani. Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur. Radiol. 2017; 27(6): 2348-2358 (doi: 10.1007/s00330-016-4579-9).
  25. G Bhatnagar, N Dikaios, D Prezzi ,…, SA Taylor. Changes in Dynamic Contrast Enhanced pharmacokinetic and Diffusion Weighted Imaging parameters reflect response to anti-TNF therapy in Crohn's Disease. Br J Radiol 2015; 88(1055): 2015-47 (doi: 10.1007/s00330-013-3010-z).
  26. A Latifoltojar, N Dikaios, A Ridout,…, S Punwani. Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate. Prostate Cancer Prostatic Dis. 2015; 18(4): 343-51 (doi: 10.1038/pcan.2015.33).
  27. M Abd-Alazeez, N Ramachandran, N Dikaios,…, S Punwani: Multiparametric MRI for detection of radiorecurrent prostate cancer: added value of apparent diffusion coefficient maps and dynamic contrast-enhanced images. Prostate Cancer Prostatic Dis. 2015; 18(2):128-36 (doi: 10.1038/pcan.2014.55).
  28. M Abd-Alazeez, HU Ahmed, M Arya, C Allen, N Dikaios, A Freeman, M Emberton, A Kirkham. Can multiparametric magnetic resonance imaging predict upgrading of transrectal ultrasound biopsy results at more definitive histology? Urol Oncol. 2014; 32(6): 741-7 (doi: 10.1016/j.urolonc.2014.01.008).
  29. E Panagiotaki, RW Chan, N Dikaios, HU Ahmed, J O'Callaghan, A Freeman, D Atkinson, S Punwani, DJ Hawkes, DC Alexander: Microstructural Characterization of Normal and Malignant Human Prostate Tissue With Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours Magnetic Resonance Imaging. Invest Radiol. 2015; 50(4): 218-27 (doi: 10.1097/RLI.0000000000000115).
  30. B Trémoulhéac, N Dikaios, D Atkinson, S Arridge: Dynamic MR image reconstruction-separation from under-sampled (k-t)-space via low-rank plus sparse prior IEEE Trans Med Imaging. 2014; 33(8):1689-701 (doi: 10.1109/TMI.2014.2321190).
  31. V Hamy, N Dikaios, S Punwani,…, D Atkinson: Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI. Med Image Anal. 2013; 18(2):301-313 (doi: 10.1016/j.media.2013.10.016).
  32. JC Makanyanga, D Pendsé, N Dikaios,… , SA Taylor: Evaluation of Crohn's disease activity: Initial validation of a magnetic resonance enterography global score (MEGS) against faecal calprotectin. Eur Radiol. 2013; 24(2):277-87 (doi: 10.1007/s00330-013-3010-z).
  33. K Thielemans, C Tsoumpas, S Mustafovic, T Beisel, P Aguiar, N Dikaios, MW Jacobson: STIR: software for tomographic image reconstruction release 2. Phys Med Biol. 2012; 57(4):867-83 (doi: 10.1088/0031-9155/57/4/867).
  34. N Karakatsanis, N Sakellios, NX Tsantilas, N Dikaios,… , K Nikita: Comparative evaluation of two commercial PET scanners, ECAT EXACT HR+ and Biograph 2, using GATE. Nuclear Instruments and Methods in Physics Research Section A 2006; 569:368-372 (doi: 10.1016/j.nima.2006.08.110).
teaching

I am physically located at the Mathematics Research Center 4, Soranou Efesiou str., 11527 Athens. Tel. +30 210 6597 662. e-mail: ndikaios@academyofathens.gr teaching

I am always looking for talented and motivated master students and PhD candidates. If you are interested in my research, drop me an email. If funding is not available, I am keen to work with good candidates to obtain scholarships either internally or from external funders.

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