Browsing by Author "Castillo Passi, Carlos"

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    A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Coronado, Ronal; Cruz, Gastao; Castillo Passi, Carlos; Tejos, Cristian; Uribe, Sergio; Prieto, Claudia; Irarrazaval, Pablo
    In MR Fingerprinting (MRF), balanced Steady-State Free Precession (bSSFP) has advantages over unbalanced SSFP because it retains the spin history achieving a higher signal-to-noise ratio (SNR) and scan efficiency. However, bSSFP-MRF is not frequently used because it is sensitive to off-resonance, producing artifacts and blurring, and affecting the parametric map quality. Here we propose a novel Spatial Off-resonance Correction (SOC) approach for reducing these artifacts in bSSFP-MRF with spiral trajectories. SOC-MRF uses each pixel's Point Spread Function to create system matrices that encode both off-resonance and gridding effects. We iteratively compute the inverse of these matrices to reduce the artifacts. We evaluated the proposed method using brain simulations and actual MRF acquisitions of a standardized T1/T2 phantom and five healthy subjects. The results show that the off-resonance distortions in T1/T2 maps were considerably reduced using SOC-MRF. For T2, the Normalized Root Mean Square Error (NRMSE) was reduced from 17.3 to 8.3% (simulations) and from 35.1 to 14.9% (phantom). For T1, the NRMS was reduced from 14.7 to 7.7% (simulations) and from 17.7 to 6.7% (phantom). For in-vivo, the mean and standard deviation in different ROI in white and gray matter were significantly improved. For example, SOC-MRF estimated an average T2 for white matter of 77ms (the ground truth was 74ms) versus 50 ms of MRF. For the same example the standard deviation was reduced from 18 ms to 6ms. The corrections achieved with the proposed SOC-MRF may expand the potential applications of bSSFP-MRF, taking advantage of its better SNR property.
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    KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration
    (2023) Castillo Passi, Carlos; Coronado, Ronal Manuel; Varela Mattatall, Gabriel; Alberola López, Carlos; Botnar, René Michael; Irarrázaval Mena, Pablo
    Purpose: To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma). Methods: Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq-compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions. Results: Koma was compared to two well-known open-source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature. Conclusions: Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.
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    Novel techniques and signal models with applications in MRI
    (2018) Castillo Passi, Carlos; Irarrázaval Mena, Pablo; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
    Las adquisiciones submuestreadas son comúnmente usadas para reducir el tiempo de escaneo en Imágenes por Resonancia Magnética (IRM). Compressed Sensing permite la reconstrucción de la imagen subyacente a partir de estos datos resolviendo un problema de optimización convexo. Este método explota la raleza de la imagen usando la norma-l1 como una medida de raleza. Esta medida es esencial en el desempeño del algoritmo. En este trabajo, proponemos un método que utiliza el ´Índice de Gini (IG), un concepto originado en economía, como una medida de raleza para la reconstrucción de IRM, debido a que satisface todas las propiedades deseables para una medida de raleza. Debido a que el IG es una función cuasi-convexa, el problema de optimización es resuelto a través de resolver problemas l1 iterativamente pesados. Este algoritmo fue testeado en un fantoma numérico y con datos de IRM in vivo. Para el fantoma, una reconstrucción perfecta fue alcanzada usando el IG con Factores de Sub Muestreo (FSM) más altos que la norma-l1. Mejoras fueron también observadas para los datos in vivo, reduciendo el error al usar el IG lo que hizo posible disminuir el FSM en 0.5 al comparar el error con la norma-l1. La novedad del método propuesto es la aplicación del IG con datos complejos, submuestreo y condiciones débiles de raleza, haciéndolo apropiado para muchas aplicaciones en resonancia magnética, sin un excesivo aumento de la carga computacional.