Browsing by Author "Milovic Fabregat, Carlos Andrés"
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- ItemA new discrete dipole kernel for quantitative susceptibility mapping(2018) Milovic Fabregat, Carlos Andrés; Acosta-Cabronero, Julio; Miguel Pinto, Jose; Mattern, Hendrik; Andía Kohnenkampf, Marcelo Edgardo; Uribe Arancibia, Sergio A.; Tejos Núñez, Cristián Andrés
- ItemA robust multi-scale approach to quantitative susceptibility mapping(2018) Acosta-Cabronero, Julio; Milovic Fabregat, Carlos Andrés; Mattern, Hendrik; Tejos Núñez, Cristián Andrés; Speck, Oliver; Callaghan, Martina F.
- ItemCalcium (Ca2+) waves data calibration and analysis using image processing techniques(2013) Milovic Fabregat, Carlos Andrés; Oses, Carolina; Villalón, Manuel J.; Uribe Arancibia, Sergio A.; Prieto Vásquez, Claudia; Andía Kohnenkampf, Marcelo Edgardo; Irarrázaval Mena, Pablo; Tejos Núñez, Cristián Andrés; Lizama, CarlosAbstract Background Calcium (Ca2+) propagates within tissues serving as an important information carrier. In particular, cilia beat frequency in oviduct cells is partially regulated by Ca2+ changes. Thus, measuring the calcium density and characterizing the traveling wave plays a key role in understanding biological phenomena. However, current methods to measure propagation velocities and other wave characteristics involve several manual or time-consuming procedures. This limits the amount of information that can be extracted, and the statistical quality of the analysis. Results Our work provides a framework based on image processing procedures that enables a fast, automatic and robust characterization of data from two-filter fluorescence Ca2+ experiments. We calculate the mean velocity of the wave-front, and use theoretical models to extract meaningful parameters like wave amplitude, decay rate and time of excitation. Conclusions Measurements done by different operators showed a high degree of reproducibility. This framework is also extended to a single filter fluorescence experiments, allowing higher sampling rates, and thus an increased accuracy in velocity measurements.
- ItemComparison of parameter optimization methods for quantitative susceptibility mapping(2020) Milovic Fabregat, Carlos Andrés; Prieto Vásquez, Claudia; Bilgic, B.; Uribe Arancibia, Sergio A.; Acosta Cabronero, J.; Irarrázaval Mena, Pablo; Tejos Núñez, Cristián Andrés
- ItemDEEP OPTICAL IMAGES of MALIN 1 REVEAL NEW FEATURES(2015) Galaz, Gaspar; Milovic Fabregat, Carlos Andrés; Suc, V.; Busta, L.; Lizana, G.; Infante Lira, Leopoldo; Royo, S.; Galaz, Gaspar; Milovic Fabregat, Carlos Andrés; Suc, V.; Busta, L.; Lizana, G.; Infante Lira, Leopoldo; Royo, S.
- ItemDeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification using Deep Learning in Magnetic Resonance Imaging(2020) della Maggiora Valdés, Gabriel Eugenio; Milovic Fabregat, Carlos Andrés; Qiu, Wenqi; Liu, Shuang; Milovic Fabregat, Carlos Andres; Sekino, Masaki; Tejos Nunez, Cristian Andres; Uribe Arancibia, Sergio A.; Irarrazaval Barros, PabloThe susceptibility of Super Paramagnetic Iron Oxide (SPIO) particles makes them a useful contrast agent for different purposes in MRI. These particles are typically quantified with relaxometry or by measuring the inhomogeneities they produced. These methods rely on the phase, which is unreliable for high concentrations. We present in this study a novel Deep Learning method to quantify the SPIO concentration distribution. We acquired the data with a new sequence called View Line in which the field map information is encoded in the geometry of the image. The novelty of our network is that it uses residual blocks as the bottleneck and multiple decoders to improve the gradient flow in the network. Each decoder predicts a different part of the wavelet decomposition of the concentration map. This decomposition improves the estimation of the concentration, and also it accelerates the convergence of the model. We tested our SPIO concentration reconstruction technique with simulated images and data from actual scans from phantoms. The simulations were done using images from the IXI dataset, and the phantoms consisted of plastic cylinders containing agar with SPIO particles at different concentrations. In both experiments, the model was able to quantify the distribution accurately.
- ItemFast nonlinear susceptibility inversion with variational regularization(2018) Milovic Fabregat, Carlos Andrés; Bilgic, Berkin; Zhao, Bo; Acosta‐Cabronero, Julio; Tejos Núñez, Cristián Andrés
- ItemMultiscale gradient domain compression for astronomical high dynamic range imaging(2016) Milovic Fabregat, Carlos Andrés; Conejero, J.; Tejos Núñez, Cristián Andrés
- ItemNonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)(2020) Polak, D.; Chatnuntawech, I.; Yoon, J.; Iyer, S. S.; Milovic Fabregat, Carlos Andrés; Lee, J.; Bachert, P.; Adalsteinsson, E.; Setsompop, K.; Bilgic, B.
- ItemQuantitative Susceptibility Map Reconstruction via a Total Generalized Variation Regularization(IEEE, 2013) Yáñez Lang, Felipe Andrés; Fan, Audrey; Bilgic, Berkin; Milovic Fabregat, Carlos Andrés; Adalsteinsson, Elfar; Irarrázaval Mena, PabloQuantitative susceptibility mapping (QSM) is a last decade new concept which allows to determine the magnetic susceptibility distribution of tissue in-vivo. Nowadays it has several applications such as venous blood oxygenation and iron concentration quantification. To reconstruct high quality maps, a regularized scheme must be used to solve this ill-posed problem, due to the dipole kernel under sampling k-space. A widely used regularization penalty is Total Variation (TV), however, we can find stair casing artifacts in reconstructions due to the assumption that images are piecewise constant, not always true in MRI. In this sense, we propose a less restrictive functional, to avoid this problem and to improve QSM quality. A second order Total Generalized Variation (TGV) does not assume piecewise constancy in the images and is equivalent to TV in terms of edge preservation and noise removal. This work describes how TGV penalty addresses an increase in imaging efficiency in magnetic susceptibility maps from numerical phantom and in-vivo data. Currently, we report higher specificity with the proposed regularization. Moreover, the robustness of TGV suggest that is a possible alternative to tissue susceptibility mapping.
- ItemQuantitative susceptibility mapping : report from the 2016 reconstruction challenge(2018) Langkammer, Christian; Schweser, Ferdinand; Shmueli, Karin; Kames, Christian; Li, Xu; Guo, Li; Milovic Fabregat, Carlos Andrés; Kim, Jinsuh; Wei, Hongjiang; Bredies, Kristian|
- ItemThe 2016 QSM Challenge : Lessons learned and considerations for a future challenge design(2020) Milovic Fabregat, Carlos Andrés; Tejos Núñez, Cristián Andrés; Acosta Cabronero, J.; Özbay, P. S.; Schwesser, F.; Marques, J. P.; Irarrázaval Mena, Pablo; Bilgic, B.; Langkammer, C.
- ItemWeak-harmonic regularization for quantitative susceptibility mapping(2019) Milovic Fabregat, Carlos Andrés; Bilgic, B.; Zhao, B.; Langkammer, C.; Tejos Núñez, Cristián Andrés; Acosta Cabronero, J.