Research Lines

Cardiovascular and pulmonary imaging

This research line deals with the development of techniques for early detection of cardiac pathologies from ultrasound and magnetic resonance images. Systems have been proposed for the characterization of myocardial movement based on the spatio-temporal registration of image sequences. Other studies tried to identify the myocardial substrate of arrhythmias, which allows us to assess the success of possible therapies. Using new segmentation algorithms, it has also been possible to study the cardiac chambers or the displacement of the mitral and aortic valves in tomography image sequences (4D-CTA).

Machine learning algorithms have also been proposed to identify biomarkers for the diagnosis and prognosis of pulmonary pathologies from tomography images.

Collaborations

• Applied Chest Imaging Laboratory. Brigham and Women's Hospital. Harvard Medical School. Boston (MA, USA).
• Cardiovascular Imaging Lab. University of California San Diego (CA, USA).
• National Heart, Lung, and Blood Institute. National Institutes of Health. Bethesda (MD, USA).
• Hospital G.U. Gregorio Marañón. Madrid (Spain).

Some publications

• Ortuño, J.E., et al. "Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts". Med. Image Anal., 65:101748. 2020 (doi: 10.1016/j.media.2020.101748).
• Bermejo-Peláez, D., et al. "Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks". Sci Rep, 10:338. 2020 (doi: 10.1038/s41598-019-56989-5).
• Jimenez-Carretero, D., et al. "A Graph-Cut Approach for Pulmonary Artery-Vein Segmentation in Noncontrast CT Images". Med. Image Anal., 52:144-159. 2019 (doi: 10.1016/j.media.2018.11.011).
• Ávila, P., et al. "Scar Extension Measured by Magnetic Resonance–Based Signal Intensity Mapping Predicts Ventricular Tachycardia Recurrence After Substrate Ablation in Patients With Previous Myocardial Infarction". JACC-Clin. Electrophysiol., 1:353-365. 2015 (doi: 10.1016/j.jacep.2015.07.006).
• Perez-David, E., et al. "Noninvasive Identification of Ventricular Tachycardia-Related Conducting Channels Using Contrast-Enhanced Magnetic Resonance Imaging in Patients With Chronic Myocardial Infarction". J. Am. Coll. Cardiol., 57:184-194. 2011 (doi: 10.1016/j.jacc.2010.07.043).
• Ledesma-Carbayo, M.J., et al. "Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation". IEEE Trans. Med. Imaging, 24:1113-1126. 2005 (doi: 10.1109/TMI.2005.852050).

Research Lines

Optical and microscopic imaging techniques

From optical coherent tomography (OCT) images and using advanced image processing techniques, screening and monitoring systems for high prevalence pathologies, such as glaucoma and some skin cancers, have been proposed and validated. Low-cost and non-invasive systems have also been proposed for neutrophil counting in chemotherapy patients. Likewise, image processing methods have been proposed for the study of embryonic development using advanced microscopy images (harmonic images, two photons, SPIM, etc.).

Collaborations

• Hospital G.U. Gregorio Marañón. Madrid (Spain).
• Center for Medical Physics and Biomedical Engineering. Medical University of Vienna (Austria).
• Department of Radiology and Nuclear Medicine. Radboud University Medical Center. Nijmegen (The Netherlands).
• Medlumics S.L. Tres Cantos (Spain).
• Research Laboratory of Electronics. Massachasetts Institute of Technology (MIT). Cambridge (MA, USA).
• Leuko Labs Inc. Cambridge (MA, USA).
• BioEmergencies (USR3695 CNRS). Gif-sur-Yvette (France).

Some publications

• Gómez-Valverde, J.J., et al. "Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images". J. Biomed. Opt., 26:065001. 2021 (doi: 10.1117/1.JBO.26.6.065001).
• Gómez-Valverde, J.J., et al. "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning". Biomed. Opt. Express, 10:892-913. 2019 (doi: 10.1364/BOE.10.000892).
• Guerra, P., et al. "Real Time Signal Processing and Data Handling with dedicated hardware in handheld OCT Device". J. Instrum., 10:C11001. 2015 (doi: 10.1088/1748-0221/10/11/C11001).
• Pablo-Trinidad, A., et al. "Automated detection of neutropenia using noninvasive video microscopy of superficial capillaries". Am. J. Hematol., 94:E219-E222. 2019 (doi: 10.1002/ajh.25516).
• Bourquard, A., et al. "Non-invasive Detection of Severe Neutropenia in Chemotherapy Patients by Optical Imaging of Nailfold Microcirculation". Sci Rep, 8:5301. 2018 (doi: 10.1038/s41598-018-23591-0).
• Castro-González, C., et al. "A Digital Framework to Build, Visualize and Analyze a Gene Expression Atlas with Cellular Resolution in Zebrafish Early Embryogenesis". PLoS Comput. Biol., 10:e1003670. 2014 (doi: 10.1371/journal.pcbi.1003670).
• Luengo-Oroz, M.A., et al. "Image Analysis for Understanding Embryo Development: A Bridge from Microscopy to Biological Insights". Curr. Opin. Genet. Dev., 21:630-637. 2011 (doi: 10.1016/j.gde.2011.08.001).
• Olivier, N., Luengo-Oroz, M.A., Duloquin, L., et al. "Cell Lineage Reconstruction of Early Zebrafish Embryos Using Label-Free Nonlinear Microscopy". Science, 329:967-971. 2010 (doi: 10.1126/science.1189428).
• Santos, A., Young, I.T. "Model-Based Resolution: Applying the Theory in Quantitative Microscopy" Appl. Optics, 39:2948-2958. 2000 (doi: 10.1364/AO.39.002948).

Research Lines

Image-guided surgery and radiotherapy

Multimodal image processing algorithms have been proposed for the planning of various surgical cases (epilepsy, liver tumors, heart diseases, etc.). A methodology for planning and dosimetry in intraoperative radiotherapy has also been proposed, including the registration of preoperative tomography images and intraoperative projective radiographs.

Collaborations

• GMV Soluciones Globales Internet, S.A.U. Tres Cantos (Spain).
• Hospital G.U. Gregorio Marañón. Madrid (Spain).
• Hospital Clínic de Barcelona (Spain).

Some publications

• Alfano, F., et al. "Breast Tumor Localization by Prone to Supine Landmark Driven Registration for Surgical Planning". IEEE Access, 10:122901-122911. 2022 (doi: 10.1109/ACCESS.2022.3223658).
• Goswami, S.S., et al. "A New Workflow for Image-Guided Intraoperative Electron Radiotherapy Using Projection-Based Pose Tracking". IEEE Access, 8:137501-137516. 2020 (doi: 10.1109/ACCESS.2020.3011915).
• Valdivieso-Casique, M.F., et al. "RADIANCE—A planning software for intra-operative radiation therapy". Transl Cancer Res, 4:196-209. 2015 (doi: 10.3978/j.issn.2218-676X.2015.04.05).
• Guerra, P., et al. "Feasibility Assessment of the Interactive Use of a Monte Carlo Algorithm in Treatment Planning for Intraoperative Electron Radiation Therapy". Phys. Med. Biol., 59:7159-7179. 2014 (doi: 10.1088/0031-9155/59/23/7159).
• Fernandez-de-Manuel, L., et al. "Organ-Focused Mutual Information for Nonrigid Multimodal Registration of Liver CT and Gd-EOB-DTPA-enhanced MRI". Med. Image Anal., 18:22-35. 2014 (doi: 10.1016/j.media.2013.09.002).
• Martí Fuster, B., et al. "FocusDET, A New Toolbox for SISCOM Analysis. Evaluation of the Registration Accuracy Using Monte Carlo Simulation". Neuroinformatics, 11:77-89. 2013 (doi: 10.1007/s12021-012-9158-x).

Research Lines

Fetal and neonatal imaging

New techniques for magnetic resonance image acquisition, reconstruction and processing have been proposed, providing support for image-based analysis of neurodevelopment including clinical applications and exploratory studies. Machine learning and statistical models have been developed for diagnosis, prognosis and understanding of perinatal development based on anatomical, micro-structural and functional brain information as well as whole-body scans.

Collaborations

• Department of Perinatal Imaging and Health. School of Biomedical Engineering and Imaging Sciences. King's College London (UK).

Some publications

• Cordero-Grande, L., et al. "Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration". IEEE Trans. Med. Imaging, 42:810-822. 2023 (doi: 10.1109/TMI.2022.3217725).
• Dimitrova, R., et al. "Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age". Neuroimage, 243:118488. 2021 (doi: 10.1016/j.neuroimage.2021.118488).
• Carney, O., et al. "Incidental findings on brain MR imaging of asymptomatic term neonates in the Developing Human Connectome Project". EClinicalMedicine, 38:100984. 2021 (doi: 10.1016/j.eclinm.2021.100984).
• Wilson, S., et al. "Development of human white matter pathways in utero over the second and third trimester". Proc. Natl. Acad. Sci., 118:e2023598118. 2021 (doi: 10.1073/pnas.2023598118).
• Christiaens, D., et al. "Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI". Neuroimage, 225:117437. 2021 (doi: 10.1016/j.neuroimage.2020.117437).
• Ng, I.H.X., et al. "Investigating altered brain development in infants with congenital heart disease using tensor-based morphometry". Sci Rep, 10:14909. 2020 (doi: 10.1038/s41598-020-72009-3).
• Dimitrova, R., et al. "Heterogeneity in Brain Microstructural Development Following Preterm Birth". Cereb. Cortex, 30:4800-4810. 2020 (doi: 10.1093/cercor/bhaa069).

Research Lines

Image processing

This activity has a direct impact on the development of the remaining research lines. Spatio-temporal image registration (or superposition) algorithms have been developed to characterize myocardial movement or to compensate for respiratory movement during thoracic imaging. 2D-3D registration and fusion algorithms have also been proposed to create high-quality three-dimensional volumes from projection images, useful in applications as diverse as microscopy imaging or coronary angiography. Work has also been done on the combination of anatomical and functional images in multimodality registration.

Some publications

• Ortuño, J.E., et al. "Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts". Med. Image Anal., 65:101748. 2020 (doi: 10.1016/j.media.2020.101748).
• Jimenez-Carretero, D., et al. "A Graph-Cut Approach for Pulmonary Artery-Vein Segmentation in Noncontrast CT Images". Med. Image Anal., 52:144-159. 2019 (doi: 10.1016/j.media.2018.11.011).
• Esteban, O., et al. "Surface-driven registration method for the structure-informed segmentation of diffusion MR images". Neuroimage, 139:450-461. 2016 (doi: 10.1016/j.neuroimage.2016.05.011).
• Fernandez-de-Manuel, L., et al. "Organ-Focused Mutual Information for Nonrigid Multimodal Registration of Liver CT and Gd-EOB-DTPA-enhanced MRI". Med. Image Anal., 18:22-35. 2014 (doi: 10.1016/j.media.2013.09.002).
• Martí Fuster, B., et al. "FocusDET, A New Toolbox for SISCOM Analysis. Evaluation of the Registration Accuracy Using Monte Carlo Simulation". Neuroinformatics, 11:77-89. 2013 (doi: 10.1007/s12021-012-9158-x).
• Rubio-Guivernau, J.L., et al. "Wavelet-based image fusion in multi-view three-dimensional microscopy". Bioinformatics, 28:238-245. 2012 (doi: 10.1093/bioinformatics/BTR609).
• Wollny, G., et al. "Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis". Med. Image Anal., 16:1015-1028. 2012 (doi: 10.1016/j.media.2012.02.004).
• Zöllner, F.G., et al. "Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses". Comput. Med. Imaging Graph., 33:171-181. 2009 (doi: 10.1016/j.compmedimag.2008.11.004).
• Ledesma-Carbayo, M.J., et al. "Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation". IEEE Trans. Med. Imaging, 24:1113-1126. 2005 (doi: 10.1109/TMI.2005.852050).
• Malpica, N., et al. "Tracking of regions-of-interest in myocardial contrast echocardiography". Ultrasound Med. Biol., 30:303-309. 2004 (doi: 10.1016/j.ultrasmedbio.2003.11.007).

Research Lines

Computation of image-based biomarkers

In this research line, work has been done on the development of algorithms for the automatic extraction of information from images and clinical data. The pharmacokinetic analysis of perfusion imaging (DCE-MRI), or the extraction of characteristics (radiomics) using learning algorithms (including deep learning) can be highlighted. As an example, quantitative biomarkers derived from clinical images have been proposed for the prediction of response to immunotherapy in oncology (non-small cell lung cancer and gliobastoma).

Collaborations

• Applied Chest Imaging Laboratory. Brigham and Women's Hospital. Harvard Medical School. Boston (MA, USA).
• Biomedical Applications of Magnetic Resonance research group. Universitat Autònoma de Barcelona (Spain).

Some publications

• Farina, B., et al. "Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients". J. Transl. Med., 21:174. 2023 (doi: 10.1186/s12967-023-04004-x).
• Bermejo-Peláez, D., et al. "Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT". Sci Rep, 12:9387. 2022 (doi: 10.1038/s41598-022-13298-8).
• Pradillo, J.M., et al. "Influence of metabolic syndrome on post-stroke outcome, angiogenesis and vascular function in old rats determined by dynamic contrast enhanced MRI". J. Cereb. Blood Flow Metab., 41:1692-1706. 2021 (doi: 10.1177/0271678X20976412).
• Núñez, L.M., et al. "Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction". Sci Rep, 10:19699. 2020 (doi: 10.1038/s41598-020-76686-y).
• Bermejo-Peláez, D., et al. "Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks". Sci Rep, 10:338. 2020 (doi: 10.1038/s41598-019-56989-5).
• Nardelli, P., et al. "Pulmonary Artery-Vein Classification in CT Images Using Deep Learning". IEEE Trans. Med. Imaging, 37:2428-2440. 2018 (doi: 10.1109/TMI.2018.2833385).
• Ortuño, J.E., et al. "DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data". BMC Bioinformatics, 14:316. 2013 (doi: 10.1186/1471-2105-14-316).
• Zöllner, F.G., et al. "Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses". Comput. Med. Imaging Graph., 33:171-181. 2009 (doi: 10.1016/j.compmedimag.2008.11.004).

Research Lines

New technologies for image acquisition

We have worked on low-cost systems for the detection of global diseases, such as microscopy image acquisition devices based on 3D printing and mobile phones, with the aim of to contribute to the diagnosis in developing countries of eye diseases, childhood tuberculosis, intestinal parasites, etc. Along these lines, work has also been done on applications such as Malaria Spot and Tuber Spot, where citizens can help researchers diagnose diseases through games.

On the other hand, new data acquisition systems have been designed, built and validated for PET (positron emission tomography) and OCT (optical coherent tomography) tomography) using integrated digital architectures (system-on-chip).

Work has also been done on tomographic reconstruction, especially on fast methods of 3D statistical reconstruction for high-resolution PET cameras, as well as in experimental proton therapy systems.

Collaborations

• Spotlab S.L. (Madrid, Spain).
• Medlumics S.L. (Tres Cantos, Spain).
• Department of Physics "E. Fermi". Università degli Studi di Pisa (Italy).
• Unidad de Medicina y Cirugía Experimental. Hospital G.U. Gregorio Marañón.

Some publications

• Cordero-Grande, L., et al. "Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration". IEEE Trans. Med. Imaging, 42:810-822. 2023 (doi: 10.1109/TMI.2022.3217725).
• García Delgado, L., Postigo, M., et al. "Remote analysis of sputum smears for mycobacterium tuberculosis quantification using digital crowdsourcing". PLoS One, 17:e0268494. 2022 (doi: 10.1371/journal.pone.0268494).
• García-Villena, J., et al. "3D-Printed Portable Robotic Mobile Microscope for Remote Diagnosis of Global Health Diseases". Electronics, 10:2408. 2021 (doi: 10.3390/electronics10192408).
• Linares, M., et al. "Collaborative Intelligence and Gamification for On-Line Malaria Species Differentiation". Malar. J., 18:21. 2019 (doi: 10.1186/s12936-019-2662-9).
• Luengo-Oroz, M.A., et al. "Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears". J. Med. Internet Res., 14:e167. 2012 (doi: 10.2196/jmir.2338).
• Guerra, P., et al. "Real Time Signal Processing and Data Handling with dedicated hardware in handheld OCT Device". J. Instrum., 10:C11001. 2015 (doi: 10.1088/1748-0221/10/11/C11001).
• Sportelli, G., et al. "First full-beam PET acquisitions in proton therapy with a modular dual-head dedicated system". Phys. Med. Biol., 59:43-60. 2014 (doi: 10.1088/0031-9155/59/1/43).
• Sportelli, G., et al. "Reprogrammable Acquisition Architecture for Dedicated Positron Emission Tomography". IEEE Trans. Nucl. Sci., 58:695-702. 2011 (doi: 10.1109/TNS.2011.2113193).
• Guerra, P., et al. "Real-Time Digital Timing in Positron Emission Tomography". IEEE Trans. Nucl. Sci., 55:2531-2540. 2008 (doi: 10.1109/TNS.2008.2005896).
• Ortuño, J.E., et al. "Efficient methodologies for system matrix modelling in iterative image reconstruction for rotating high resolution PET". Phys. Med. Biol., 55:1833-1861. 2010 (doi: 10.1088/0031-9155/55/7/004).