Browsing by Author "Prieto, C."

Now showing 1 - 11 of 11
  • Thumbnail Image
    Item
    Atypical Mg-poor Milky Way Field Stars with Globular Cluster Second-generation-like Chemical Patterns
    (2017) Fernández Trincado J.; Zamora, O.; García Hernández, D.; Souto, D.; Dell'Agli, F.; Schiavon, R.; Geisler, D.; Tang, B.; Villanova, S.; Chanamé, Julio; Hasselquist, S.; Mennickent, R.; Cunha, K.; Shetrone, M.; Prieto, C.; Vieira, K.; Zasowski, G.; Sobeck, J.; Hayes, C.; Majewski, S.; Placco, V.; Beers, T.; Schleicher, D.; Robin, A.; Mészáros, S.; Masseron, T.; Pérez, A.; Anders, F.; Meza, A.; Alves-Brito A.; Carrera, R.; Minniti, D.; Lane, R.; Fernández-Alvar E.; Moreno, E.; Pichardo, B.; Pérez-Villegas, A.; Schultheis, M.; Roman-Lopes, A.; Fuentes, C.; Nitschelm, C.; Harding, P.; Bizyaev, D.; Pan, K.; Oravetz, D.; Simmons, A.; Ivans, I.; Blanco-Cuaresma, S.; Hernández J.; Alonso-García, J.; Valenzuela, O.
  • Thumbnail Image
    Item
    Cardiac functional assessment without electrocardiogram using physiological self-navigation
    (2014) Kolbitsch, C.; Prieto, C.; Schaeffter, T.
    PurposeElectrocardiogram (ECG)-gated cine MRI provides highly accurate functional assessment of the heart. Nevertheless, reliable ECG signals are not always available due to patient's electrophysiology or due to high MR field strengths. Here, a novel framework for cardiac functional assessment using physiological information is presented, which is obtained from MR image data.
  • Thumbnail Image
    Item
    Convalescent plasma in COVID-19. Mortality-safety first results of the prospective multicenter FALP 001-2020 trial
    (2020) Gazitúa, R.; Briones, J. L.; Selman, C.; Villarroel Espíndola, F.; Aguirre, A.; González Steigmaier, R.; Cereceda, K.; Mahave, M.; Rubio, B.; Ferrer Rosende, P.; Sapunar, J.; Marsiglia, H.; Morales, R.; Yarad, F.; Balcells Marty, María Elvira; Rojas, Luis; Nervi Nattero, Bruno; Nien, J. K.; Garate, J.; Prieto, C.; Palma, S.; Escobar, C.; Bascuñan, J.; Muñoz, R.; Pinto, M.; Cardemil, D.; Navarrete, M.; Reyes, S.; Espinosa, V.; Yáñez, N.; Caglevic, C.
  • Thumbnail Image
    Item
    Highly efficient motion-corrected simultaneous cardiac PET-MR imaging
    (2017) Munoz, C.; Neji, R.; Marsden, P.; Reader, A.J.; Botnar, R.M.; Prieto, C.
  • Thumbnail Image
    Item
    Multi-parametric liver tissue characterization using MR fingerprinting: Simultaneous T1, T2, T2*, and fat fraction mapping
    (2020) Jaubert, O.; Arrieta, C.; Cruz, G.; Bustin, A.; Schneider, T.; Georgiopoulos, G.; Masci, P. G.; Sing-Long C., Carlos A.; Botnar, René Michael; Prieto, C.
  • Thumbnail Image
    Item
    Nonrigid motion modeling of the liver from 3-D undersampled self-gated golden-radial phase encoded MRI
    (2012) Buerger, C.; Clough, R.E.; King, A.P.; Schaeffter, T.; Prieto, C.
    Magnetic resonance imaging (MRI) has been commonly used for guiding and planning image guided interventions since it provides excellent soft tissue visualization of anatomy and allows motion modeling to predict the position of target tissues during the procedure. However, MRI-based motion modeling remains challenging due to the difficulty of acquiring multiple motion-free 3-D respiratory phases with adequate contrast and spatial resolution. Here, we propose a novel retrospective respiratory gating scheme from a 3-D undersampled high-resolution MRI acquisition combined with fast and robust image registrations to model the nonrigid deformation of the liver. The acquisition takes advantage of the recently introduced golden-radial phase encoding (G-RPE) trajectory. G-RPE is self-gated, i.e., the respiratory signal can be derived from the acquired data itself, and allows retrospective reconstructions of multiple respiratory phases at any arbitrary respiratory position. Nonrigid motion modeling is applied to predict the liver deformation of an average breathing cycle. The proposed approach was validated on 10 healthy volunteers. Motion model accuracy was assessed using similarity-, surface-, and landmark-based validation methods, demonstrating precise model predictions with an overall target registration error of TRE = 1.70 +/- 0.94 mm which is within the range of the acquired resolution.
  • Thumbnail Image
    Item
    PET image reconstruction using multi-parametric anato-functional priors
    (2017) Mehranian, A.; Belzunce, M.A.; Niccolini, F.; Politis, M.; Prieto, C.; Turkheimer, F.; Hammers, A.; Reader, A.J.
  • Thumbnail Image
    Item
    Prospective high-resolution respiratory-resolved whole-heart MRI for image-guided cardiovascular interventions
    (2012) Kolbitsch, C.; Prieto, C.; Buerger, C.; Harrison, J.; Razavi, R.; Smink, J.; Schaeffter, T.
  • Thumbnail Image
    Item
  • No Thumbnail Available
    Item
    TRIO a Technique for Reconstruction Using Intensity Order: Application to Undersampled MRI
    (2011) Ramirez Marin, Leonardo Raul; Prieto, C.; Sing-Long, C.; Uribe Arancibia, Sergio A.; Batchelor, P.; Tejos NÚñez, Cristian Andrés; Irarrazaval Barros, Pablo
    Long acquisition times are still a limitation for many applications of magnetic resonance imaging (MRI), specially in 3-D and dynamic imaging. Several undersampling reconstruction techniques have been proposed to overcome this problem. These techniques are based on acquiring less samples than specified by the Nyquist criterion and estimating the nonacquired data by using some sort of prior information. Most of these reconstruction methods use prior information based on estimations of the pixel intensities of the images and therefore they are prone to introduce spatial or temporal blurring. Instead of using the pixel intensities, we propose to use information that allows us to sort the pixels of an image from darkest to brightest. The set of order relations which sort the pixels of an image has been called intensity order. The intensity order of an image can be estimated from low-resolution images, adjacent slices in volumetric acquisitions, temporal correlation in dynamic sequences or from prior reconstructions. Our technique for reconstruction using intensity order (TRIO) consists of looking for an image that satisfies the intensity order and minimizes the discrepancy between the acquired and reconstructed data. Results show that TRIO can effectively reconstruct 2-D-cine cardiac MR images (under-sampling factor of 4), estimating correctly the temporal evolution of the objects. Furthermore, TRIO is used as a second stage reconstruction after reconstructing with other techniques, keyhole, sliding window and k-t BLAST, to estimate the order information. In all cases the images are improved by TRIO.
  • No Thumbnail Available
    Item
    Unsupervised reconstruction of accelerated cardiac cine MRI using neural fields
    (2025) Catalán, T.; Courdurier Bettancourt, Matías Alejandro; Osses, A.; Fotaki, A.; Botnar, René Michael; Sahli Costabal, F.; Prieto, C.
    Background: Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions. Several regularization approaches that exploit spatial–temporal redundancy have been proposed to reconstruct undersampled cardiac cine MRI. More recently, methods based on supervised deep learning have been also proposed to further accelerate acquisition and reconstruction. However, these techniques rely on usually large dataset for training, which are not always available and might introduce biases. Methods: In this work we propose NF-cMRI, an unsupervised approach based on implicit neural field representations for cardiac cine MRI. We evaluate our method in in-vivo undersampled golden-angle radial multi-coil acquisitions for undersampling factors of 13x, 17x and 26x. Results: The proposed method achieves excellent scores in sharpness and robustness to artifacts and comparable or improved spatial–temporal depiction than state-of-the-art conventional and unsupervised deep learning reconstruction techniques. Conclusions: We have demonstrated NF-cMRI potential for cardiac cine MRI reconstruction with highly undersampled data.