Geometric distortion in magnetic resonance imaging systems assessed using an open-source plugin for scientific image analysis

Aoyama, Tomoo; Shimizu, Hidetoshi; Shimizu, Ikuo; Teramoto, Akira; Kaneda, Naoki; Nakamura, Kazuhiko; Nakamura, Masaru; Kodaira, Takeshi

doi:10.1007/s12194-018-0477-y

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Other authors focus on different parameters to optimize the MRI acquisition procedure. In this way, the field of view (FOV) was optimized in Aoyama et al (2018) , Walker et al (2014) to reduce the geometric distortion for the configuration of the MRI. Similarly, the position of the sample was reviewed in Bajd et al (2016b) , Bernau et al (2015) , achieving the best position as a function of the coil used.…”

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confidence: 99%

Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models

Caballero

Pérez‐Palacios

Caro

et al. 2021

PeerJ Computer Science

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The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results (r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems.

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