High-performance permanent magnet array design by a fast genetic algorithm (GA)-based optimization for low-field portable MRI

Liang, Ting-Ou; Koh, Yan Hao; Qiu, Tie; Li, Er‐Ping; Yu, Wenwei; Huang, Shao Ying

doi:10.1016/j.jmr.2022.107309

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…When we selected features based on the GA, the accuracy of the SVM-RBF increased from about 83% to 89% using 56 features, while the accuracy of SVM-Gaussian, using only 7 features, increased from about 81% to 86%. Reducing the number of features improved the classification performance and increased the computational speed as was expected based on the literature [40,41].…”

Section: Resultsmentioning

confidence: 52%

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Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Najafi,

Yousefi Rezaii,

Danishvar

et al. 2023

Sensors

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The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as “healthy” (T-score > −0.9) and “unhealthy” based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA.

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Section: Resultsmentioning

confidence: 52%

“…The Genetic algorithm (GA) has been successfully used for feature selection in MRI segmentation and classification and also in combination with the SVM [40,41].…”

Section: Feature Selection Using the Genetic Algorithmmentioning

confidence: 99%

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Najafi,

Yousefi Rezaii,

Danishvar

et al. 2023

Sensors

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“…She described the use of MagTetris for fast simulations of magnetic fields. 56,57 Mathieu Sarracanie described fast acquisitions targeted for point-of-care imaging. He emphasized that fast acquisitions must be paired with other methods to mitigate noise, and that it is important to be mindful of hardware to enable optimal, advanced acquisitions.…”

Section: Scientific Sessionsmentioning

confidence: 99%

Low‐field MRI: A report on the 2022 ISMRM workshop

Campbell-Washburn

Keenan

et al. 2023

Magnetic Resonance in Med

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In March 2022, the first ISMRM Workshop on Low‐Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low‐field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low‐field MRI within traditional radiology practices, other point‐of‐care settings, and the broader community. The notion of “image quality” versus “information content” was also discussed, as images from low‐field portable systems that are purpose‐built for clinical decision‐making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

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“…Magnet design is a major part because the size of the magnet directly decides the size of an MRI scanner. In [5], a high-performance inward-outward (IO) ring based permanent magnet design (shown in Fig. 1 (a)) was proposed for head imager.…”

mentioning

confidence: 99%