Browsing by Author "Qiu, Wenqi"

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    DeepSPIO: 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, Pablo
    The 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.
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    MRI Pulse Sequence for Quantifying Magnetic Nanoparticles From Highly Distorted Static Field: View Line
    (2024) Liu, Shuang; Qiu, Wenqi; Della Maggiora, Gabriel; Kuwahata, Akihiro; Irarrazaval, Pablo; Sekino, Masaki
    In order to use magnetic resonance imaging (MRI) as a quantification tool for pre-clinical research involving high concentrations of magnetic nanoparticles (MNPs), a static field map can serve as the basis for subsequent quantitative analysis. However, the magnetic fields generated by high concentrations of MNPs can cause failure in conventional field mapping techniques. This study introduces a novel sequence for producing static field maps. This method has been demonstrated through the acquisition of 7T-MRI data and simulation data for ferucarbotran with a range of iron concentration from 27.9 to 3.48 mu g/mu L. The reconstructed interpolated field map and the theoretical field map showed a high degree of similarity, with a normalized mean error of 23.85% for phantom experiments and 12.02% for animal experiments using a rat. The quantification of the MNP concentration was also satisfactory, particularly considering that it was performed in areas, where the signal is completely lost.