Quantitative Bio-Imaging Lab

📍 CCMAR - Portugal

About QBI

The Quantitative Bio-Imaging Lab is part of CCMAR, which is located within the Gambelas Campus of the University of Algarve (Faro, Portugal), and conducts research in the fields of medical and biological image acquisition, processing, reconstruction and analysis. This includes the development of rapid imaging, motion correction, deep learning-based reconstruction and quantification approaches for Magnetic Resonance Imaging and Optical Imaging for a broad spectrum of application fields, such as cardiology, oncology and marine biology.

OPTIMAR ~ OPTical Imaging 🐟

The EU-funded OPTIMAR project aims to investigate the specific molecular signalling pathways associated with abnormal flatfish metamorphosis using 3D optical imaging techniques.

Cardiac MRI 🧲

Our research is also focused on the development of accelerated acquisition, undersampled reconstruction and motion correction techniques for free-breathing cardiac Magnetic Resonance Imaging (MRI) for diagnosing cardiovascular disease. We are particularly interested in the application of these methods to 3D whole-heart cardiac MRI and quantitative first-pass myocardial perfusion and their translation into clinical practice.

News: We have been awared a 🌟 CaixaResearch Health grant 🌟 to develop quantitative cardiac MRI methods.🎉
🔗Prompt diagnosis of coronary heart disease to prevent early mortality

We have several Post-Doc and PhD positions HERE 👩🏽‍⚕️👩🏻‍💻

Deep Learning 👩🏻‍💻

Our active research projects aim to develop deep learning-based methods for image reconstruction from accelerated scans, automated detection, segmentation and classification tasks for diagnosis, prognosis, and therapy response prediction.

News: Our project "Uncovering the secrets of marine fish with NVIDIA" has been awarded an 🌟 NVIDIA 🌟 Academic grant 🎉

👩‍🎓 Would you like to do a PhD with us?

Check our projects 🔎 Host: CCMAR | Project title/description: PhD Fellowship | Artificial Intelligence & BioImaging

Educational Resources 📓

QBI hosts educational courses in the area of biomedical imaging, which mixes didactic lectures with hands-on tutorials and programming exercises covering, for example, the principles of tomography.

🔎 Attending Science is Wondeful 2021?
🔗 Download our activity files as ZIP or separately

Publications 📰

QBI’s journal publications and conference proceedings

👉 Full list

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

Tourais J, Scannell C, 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

Lalande A, Chen Z, Pommier T et al. 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 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, Cham. 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 BIOS 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 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