Persona:
Santa Marta Pastrana, Cristina María

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ORCID
0000-0001-8664-5990
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Santa Marta Pastrana
Nombre de pila
Cristina María
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2011 - 20172
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Autor: Romero, Eduardo C. × Tipo: artículo ×

Resultados de la búsqueda

Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Multiresolution Reconstruction for Cone-Beam Tomography from Raw Data Projections Using 3D Ridgelets
    (Springer Nature, 2011-03-01) Romero, Eduardo C.; Gómez Gómez, Francisco; Santa Marta Pastrana, Cristina María
    This paper presents a novel method which reconstructs any desired 3D image resolution from raw cone-beam CT data. X-ray attenuation through the object is approximated using ridgelet basis functions which allow us to have multiresolution representation levels. Since the Radon data have preferential orientations by nature, a spherical wavelet transform is used to compute the ridgelet coefficients from the Radon shell data. The whole method uses the classical Grangeat’s relation for computing derivatives of the Radon data which are then integrated and projected to a spherical wavelet representation and back-reconstructed using a modified version of the well known back-projection algorithm. Unlike previous reconstruction methods, this proposal uses a multiscale representation of the Radon data and therefore allows fast display of low-resolution data level.
  • Miniatura
    Publicación
    A sparse Bayesian representation for super-resolution of cardiac MR images
    (Elsevier, 2017-02) Velasco, Nelson F.; Rueda Olarte, Andrea del Pilar; Romero, Eduardo C.; Santa Marta Pastrana, Cristina María
    High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series –observations from different non-orthogonal series composed of anisotropic voxels – with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.