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  • Correia T, Schneider T, Chiribiri A Model-Based Reconstruction for Highly Accelerated First-Pass Perfusion Cardiac MRI. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11765. Springer (2019). https://doi.org/10.1007/978-3-030-32245-8_57
     
  • Vitali M, Candeo A, Farina A, Pozzi P, Brix A, Bassi A, Correia T. Accelerated dynamic light sheet microscopy: unifying time-varying patterned illumination and low-rank and sparsity constrained reconstruction. Journal of Physics: Photonics, 7, 025005 (2025). https://doi.org/10.1088/2515-7647/adad23
     
  • Obando M, Bassi A, Ducros N, Mato G, Correia T. Model-based deep learning framework for accelerated optical projection tomography. Scientific Reports, 13, 21735 (2023). https://doi.org/10.1038/s41598-023-47650-3
     
  • Ismail T, Strugnell W, Coletti C, Božic-Iven M, Weingärtner S, Hammernik K, Correia T†, Küstner T†. Cardiac MR: From Theory to Practice Front Cardiovasc Med 9, 826283 (2022). https://doi.org/10.3389/fcvm.2022.826283
     
  • Tourais J, Scannell C, Schneider T, Alskaf E, Crawley R, Bosio F, Sanchez-Gonzalez J, Doneva M, Schülke C, Meineke J, Keupp J, Smink J, Breeuwer M, Chiribiri A, Henningsson M, Correia T. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration Front Cardiovasc Med 9, 884221 (2022). https://doi.org/10.3389/fcvm.2022.884221
     
  • A Lalande, Z Chen, T Pommier, T Decourselle, A Qayyum, M Salomon, D Ginhac, Y Skandarani, A Boucher, K Brahim, M de Bruijne, R Camarasa, T Correia, X Feng, K Girum, A Hennemuth, M Huellebrand, R Hussain, M Ivantsits, J Ma, C Meyer, R Sharma, J Shi, N Tsekos, M Varela, X Wang, S Yang, H Zhang, Y Zhang, Y Zhou, X Zhuang, R Couturier, F Meriaudeau. Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge Med Image Anal 79, 102428 (2022). https://doi.org/10.1016/j.media.2022.102428
     
  • Martín-González E, Alskaf E, Chiribiri A, Casaseca-de-la-Higuera P, Alberola-López C, Nunes R, Correia T. Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI. In: Haq N., Johnson P., Maier A., Würfl T., Yoo J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2021. Lecture Notes in Computer Science, vol 12964. Springer, Cham. https://doi.org/10.1007/978-3-030-88552-6_9
     
  • Lourenço A, Kerfoot E, Dibblin C, Alskaf E, Anjari M, Bharath A, King A, Chubb H, Varela M†, Correia T†. Left Atrial Ejection Fraction Estimation Using SEGANet for Fully Automated Segmentation of CINE MRI. In: Puyol Anton E. et al. (eds) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science, vol 12592. Springer (2021). https://doi.org/10.1007/978-3-030-68107-4_14 OR arXiv:2008.13718
     
  • Carvalho C, Gaspar A, Real C, Galán-Arriola C, Moya-Sáez E, Menchón-Lara R-M, Sanchez-Gonzalez J, Alberola-López C, Nunes R, Ibáñez B, Correia T. Quantitative First-Pass Perfusion CMR: from technical principles to clinical practice (2025). arXiv:2502.13084
     
  • Correia T, Ginami G, Cruz G, Neji R, Rashid I, Botnar R, Prieto C. Optimized respiratory‐resolved motion‐compensated 3D Cartesian coronary MR angiography. Magn Reson Med 80: 2618– 2629 (2018). https://doi.org/10.1002/mrm.27208 

 

OTHER PUBLICATIONS 

  • Moya-Sáez E, Menchón-Lara RM, Sánchez-González J et al. K-CC-MoCo: A Fast k-Space-Based Respiratory Motion Correction for Highly Accelerated First-Pass Perfusion Cardiovascular MR. Magn Reson Med, 95(6):3536-3549 (2026). https://doi.org/10.1002/mrm.70287 
     
  • Vitali M, Candeo A, Farina A, et al. Accelerated dynamic light sheet microscopy: unifying time-varying patterned illumination and low-rank and sparsity constrained reconstruction. Journal of Physics: Photonics, 7, 025005 (2025). https://doi.org/10.1088/2515-7647/adad23
     
  • Olausson T, Terpstra M, Huttinga N et al. Free-running time-resolved first-pass myocardial perfusion using a multi-scale dynamics decomposition: CMR-MOTUS. Magn Reson Mater Phy (2025). https://doi.org/10.1007/s10334-025-01291-x 
     
  • Santinha J, Correia T. RAMI: Radiomics for LGE Assessment of Myocardial Infarction and Microvascular Obstruction, J Cardiovasc Magn Reson. 26: S1-926 (2024). https://doi.org/10.1016/j.jocmr.2024.100140 
     
  • Obando M, Bassi A, Ducros N, Mato G, Correia T. Model-based deep learning framework for accelerated optical projection tomography. Scientific Reports, 13, 21735 (2023). https://doi.org/10.1038/s41598-023-47650-3
     
  • Lalande A, Chen Z, Pommier T et al. Deep learning methods for automatic evaluation of delayed-enhancement MRI. The results of the EMIDEC challenge. Medical Image Analysis, 79, 102428 (2022). https://doi.org/10.1016/j.media.2022.102428
     
  • Tourais J, Scannell CM, Schneider T  et al. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration. Front Cardiovasc Med, 9:884221 (2022). https://doi.org/10.3389/fcvm.2022.884221
     
  • Ismail TF, Strugnell W, Coletti C et al. Cardiac MR: From Theory to Practice. Front Cardiovasc Med. 9:826283 (2022). https://doi.org/10.3389/fcvm.2022.826283
     
  • Martín-González E, Alskaf E, Chiribiri A et al. Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI. In: Haq, N., Johnson, P., Maier, A., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2021. Lecture Notes in Computer Science, vol 12964. Springer  (2021).  https://doi.org/10.1007/978-3-030-88552-6_9
     
  • Lourenço A, Kerfoot E, Grigorescu I et al. Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information. In: Puyol Anton E. et al. (eds) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science, vol 12592. Springer (2021). https://doi.org/10.1007/978-3-030-68107-4_34
     
  • Lourenço A, Kerfood E, Dibblin C et al.  Left Atrial Ejection Fraction Estimation Using SEGANet for Fully Automated Segmentation of CINE MRI. In: Puyol Anton E. et al. (eds) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science, vol 12592. Springer (2021). https://doi.org/10.1007/978-3-030-68107-4_14
     
  • Correia T, Ginami G, Rashid I et al. Accelerated high-resolution free-breathing 3D whole-heart T2-prepared black-blood and bright-blood cardiovascular magnetic resonance. J Cardiovasc Magn Reson 22, 88 (2020). https://doi.org/10.1186/s12968-020-00691-3
     
  • Bustin A, Rashid I, Cruz G et al. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J Cardiovasc Magn Reson 22, 24 (2020). https://doi.org/10.1186/s12968-020-00611-5
     
  • Varela M, Anjari M, Correia T et al. High-resolution CINE MRI allows estimation of 3D regional atrial strains, European Heart Journal, Volume 41, Issue Supplement_2, ehaa946.0244 (2020). https://doi.org/10.1093/ehjci/ehaa946.0244
     
  • Lourenço A, E Kerfoot E, Dibblin C et al. Automatic estimation of left atrial function from short axis CINE-MRI using machine learning, European Heart Journal, Volume 41, Issue Supplement_2, ehaa946.0229 (2020). https://doi.org/10.1093/ehjci/ehaa946.0229
     
  • Scannell CM, Correia T, Villa ADM et al. Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging. Sci Rep 10, 12684 (2020). https://doi.org/10.1038/s41598-020-69747-9
     
  • Varela M, Queiros S, Anjari M et al. Strain maps of the left atrium imaged with a novel high-resolution CINE MRI protocol. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society 2020 Jul;2020:1178-1181 (2020). https://doi.org/10.1109/embc44109.2020.9175383
     
  • Nordio G, Schneider T, Cruz G, et al. Whole‐heart T1 mapping using a 2D fat image navigator for respiratory motion compensation. Magn Reson Med; 83: 178– 187 (2020). https://doi.org/10.1002/mrm.27919
     
  • Velasco Forte MN, Valverde I, Prabhu N et al. Visualization of coronary arteries in paediatric patients using whole-heart coronary magnetic resonance angiography: comparison of image-navigation and the standard approach for respiratory motion compensation. J Cardiovasc Magn Reson 21, 13 (2019). https://doi.org/10.1186/s12968-019-0525-8
     
  • Correia T, Schneider T, Chiribiri A Model-Based Reconstruction for Highly Accelerated First-Pass Perfusion Cardiac MRI. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11765. Springer (2019). https://doi.org/10.1007/978-3-030-32245-8_57
     
  • Davis SP, Kumar S, Wisniewski L et al. Exploiting patterned illumination and detection in optical projection tomography. Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830M (2019). https://doi.org/10.1117/12.2508653
     
  • Bustin A, Ginami G, Cruz G et al. Five‐minute whole‐heart coronary MRA with sub‐millimeter isotropic resolution, 100% respiratory scan efficiency, and 3D‐PROST reconstruction. Magn Reson Med.; 81: 102– 115 (2018). https://doi.org/10.1002/mrm.27354
     
  • Correia T, Ginami G, Cruz G et al. Optimized respiratory‐resolved motion‐compensated 3D Cartesian coronary MR angiography. Magn Reson Med; 80: 2618– 2629 (2018). https://doi.org/10.1002/mrm.27208
     
  • Davis SP, Wisniewski L, Kumar S et al. Slice-illuminated optical projection tomography. Opt. Lett. 43, 5555-5558 (2018). https://doi.org/10.1364/OL.43.005555
     
  • Correia T, Cruz G, Schneider T et al, Accelerated nonrigid motion‐compensated isotropic 3D coronary MR angiography. Med. Phys., 45: 214-222 (2018). https://doi.org/10.1002/mp.12663
     
  • Ducros N, Correia T, Bassi A et al. Reconstruction of an optical inhomogeneity map improves fluorescence diffuse optical tomography. Biomedical Physics & Engineering Express 2(5): 055020 (2016). https://doi.org/10.1088/2057-1976/2/5/055020
     
  • Kumar S, Lockwood N, Ramel M  et al. Quantitative in vivo optical tomography of cancer progression & vasculature development in adult zebrafish. Oncotarget; 7: 43939-43948 (2016). https://doi.org/10.18632/oncotarget.9756
     
  • Kumar S, Lockwood N, Ramel M et al. In vivo multiplexed OPT and FLIM OPT of an adult zebrafish cancer disease model. in Biomedical Optics 2016, OSA Technical Digest  (Optical Society of America), paper CTu4A.2 (2016). https://doi.org/10.1364/CANCER.2016.CTu4A.2
     
  • Correia T, Koch M, Ale A et al. Patch-based anisotropic diffusion scheme for fluorescence diffuse optical tomography—part 2: image reconstruction. Physics in Medicine and Biology 61(4): 1439-1451 (2016). https://doi.org/10.1088/0031-9155/61/4/1452
     
  • Correia T, Arridge S. Patch-based anisotropic diffusion scheme for fluorescence diffuse optical tomography—part 1: technical principles. Physics in Medicine and Biology 61(4): 1452-1475 (2016). https://doi.org/10.1088/0031-9155/61/4/1439
     
  • Correia T, Lockwood N, Kumar S, Yin J, Ramel MC et al. Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish. PLOS ONE 10(8): e0136213 (2015). https://doi.org/10.1371/journal.pone.0136213
     
  • Kumar S, Lockwood N, Andrews N et al. Rapid in-vivo Optical Projection Tomography of Larval and Adult Zebrafish Disease Models with Angular Multiplexing and FLIM-FRET. in Optics in the Life Sciences, OSA Technical Digest (Optical Society of America), paper OT2D.2 (2015). https://doi.org/10.1364/BODA.2015.OT2D.2
     
  • Dempsey L, Cooper R, Roque T et al. Data-driven approach to optimum wavelength selection for diffuse optical imaging," J. Biomed. Opt. 20(1) 016003 (2015). https://doi.org/10.1117/1.JBO.20.1.016003
     
  • V Soloviev, T Correia, S Arridge. Fluorescence lifetime measurements in turbid media. in Fluorescence Lifetime Spectroscopy and Imaging for tissue biomedical diagnostics, ed. by L Marcu, P French, D Elson, 283-322, CRC Press (2014).https://doi.org/10.1201/b17018
     
  • Robertson JL, Ghosh A, Correia T et al. Effect of Blood in the Cerebrospinal Fluid on the Accuracy of Cerebral Oxygenation Measured by Near Infrared Spectroscopy. In: Swartz H.M., Harrison D.K., Bruley D.F. (eds) Oxygen Transport to Tissue XXXVI. Advances in Experimental Medicine and Biology, vol 812. Springer, New York, NY (2014). https://doi.org/10.1007/978-1-4939-0620-8_31
     
  • Correia T, Rudge T, Koch M et al. Wavelet-based data and solution compression for efficient image reconstruction in fluorescence diffuse optical tomography. J. Biomed. Opt. 18(8) 086008 (2013) https://doi.org/10.1117/1.JBO.18.8.086008
     
  • Chamorro-Servent J, Abascal J, Aguirre J et al. Use of Split Bregman denoising for iterative reconstruction in fluorescence diffuse optical tomography. J. Biomed. Opt. 18(7) 076016 (2013) https://doi.org/10.1117/1.JBO.18.7.076016
     
  • Correia T, Ducros N, D’Andrea C et al. Quantitative fluorescence diffuse optical tomography in the presence of heterogeneities. Opt. Lett. 38, 1903-1905 (2013). https://doi.org/10.1364/OL.38.001903
     
  • Correia T, Rudge T, Arridge S. Efficient image reconstruction in fluorescence diffuse optical tomography (fDOT) using data and solution compression. in Diffuse Optical Imaging IV, P. Taroni and H. Dehghani, eds., Vol. 8799 of SPIE Proceedings (Optical Society of America), paper 87990H (2013). https://doi.org/10.1117/12.2032548
     
  • Papademetriou MD, Richards J, Correia T et al. Cortical Mapping of 3D Optical Topography in Infants. In: Van Huffel S., Naulaers G., Caicedo A., Bruley D.F., Harrison D.K. (eds) Oxygen Transport to Tissue XXXV. Advances in Experimental Medicine and Biology, vol 789. Springer, New York, NY (2013). https://doi.org/10.1007/978-1-4614-7411-1_61
     
  • Soloviev V, Zacharakis G, Spiliopoulos G et al. Tomographic imaging with polarized light. J. Opt. Soc. Am. A 29, 980-988 (2012). https://doi.org/10.1364/JOSAA.29.000980
     
  • Correia T, Lloyd-Fox S, Everdell N et al. Three-dimensional optical topography of brain activity in infants watching videos of human movement. Physics in Medicine and Biology 57(5): 1135-1146 (2012). https://doi.org/10.1088/0031-9155/57/5/1135
     
  • Abascal JFP‐J, Chamorro‐Servent J, Aguirre J et al. Fluorescence diffuse optical tomography using the split Bregman method. Med. Phys., 38: 6275-6284 (2011). https://doi.org/10.1118/1.3656063
     
  • Correia T, Aguirre J, Sisniega A et al. Split operator method for fluorescence diffuse optical tomography using anisotropic diffusion regularisation with prior anatomical information. Biomed. Opt. Express 2, 2632-2648 (2011). https://doi.org/10.1364/BOE.2.002632
     
  • Correia T, Gibson A, Hebden J. Identification of the optimal wavelengths for optical topography: a photon measurement density function analysis. J. Biomed. Opt. 15(5) 056002 (2010). https://doi.org/10.1117/1.3484747
     
  • Correia T, Banga A, Everdell N et al. A quantitative assessment of the depth sensitivity of an optical topography system using a solid dynamic tissue-phantom. Physics in Medicine and Biology 54(20): 6277-6286 (2009). https://doi.org/10.1088/0031-9155/54/20/016
     
  • Correia T, Gibson A, Schweiger M, Hebden J. Selection of regularization parameter for optical topography. J. Biomed. Opt. 14(3) 034044 (2009). https://doi.org/10.1117/1.3156839
     
  • Correia T, Gibson A, Hebden J. Identification of the optimal wavelengths in optical topography using photon density measurement functions. Proc. SPIE 7187, Biomedical Applications of Light Scattering III, 718718 (2009); https://doi.org/10.1117/12.809295
     
  • Hebden J, Correia T, Khakoo I et al. A dynamic optical imaging phantom based on an array of semiconductor diodes. Physics in Medicine and Biology 53(21): N407-N413 (2008). https://doi.org/10.1088/0031-9155/53/21/N01
     
  • Correia T, Gibson A, Hebden J. Optimal Selection of the Regularization Parameter for Optical Topography Image Reconstruction. in Biomedical Optics, OSA Technical Digest (CD) (Optical Society of America), paper BSuE49 (2008). https://doi.org/10.1364/BIOMED.2008.BSuE49
     
  • Hebden J, Bunker J, Correia T et al. An electrically-activated dynamic tissue-equivalent phantom for assessment of diffuse optical imaging systems. Physics in Medicine and Biology 53(2): 329-337 (2007). https://doi.org/10.1088/0031-9155/53/2/002
     
  • T Correia, N Lockwood, S Kumar, J Yin, M Ramel, N Andrews, M Katan, L Bugeon, M J Dallman, J McGinty, P Frankel, P French, Simon Arridge. Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish. PLOS ONE 10(8): e0136213 (2015). https://doi.org/10.1371/journal.pone.0136213
     
  • T Correia, T Rudge, M Koch, V Ntziachristos, S Arridge. Wavelet-based data and solution compression for efficient image reconstruction in fluorescence diffuse optical tomography. J Biomed Opt 18(8) 086008 (2013). https://doi.org/10.1117/1.JBO.18.8.086008
     
  • T Correia, J Aguirre, A Sisniega, J Chamorro-Servent, J Abascal, J J. Vaquero, M Desco, V Kolehmainen, S Arridge. Split operator method for fluorescence diffuse optical tomography using anisotropic diffusion regularisation with prior anatomical information. Biomed. Opt. Express 2, 2632-2648 (2011). https://doi.org/10.1364/BOE.2.002632