Jie Zhao, Minhua Zhu, Ling Xia, Yifeng Fan, Feng Liu
{"title":"一种基于PSA-SQP混合算法的MRI磁体被动摆振优化方法。","authors":"Jie Zhao, Minhua Zhu, Ling Xia, Yifeng Fan, Feng Liu","doi":"10.1038/s41598-025-13751-4","DOIUrl":null,"url":null,"abstract":"<p><p>In Magnetic Resonance Imaging (MRI), achieving a highly uniform main magnetic field (B<sub>0</sub>) is essential for producing detailed images of human anatomy. Passive Shimming (PS) is a technique used to enhance B<sub>0</sub> uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using Linear Programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. This work aims to improve the efficacy of passive shimming by optimally balancing field quality, iron usage, and harmonics, leading to a smoother field profile. This study introduces a hybrid algorithm that combines a Pattern Search Algorithm with Sequential Quadratic Programming (PSA-SQP) to enhance shimming performance. Additionally, a regularization method is employed to effectively reduce the use of iron pieces. The magnetic field improved from 462 ppm to 6.7 ppm, utilizing merely 0.8 kg of iron in a 400 mm Diameter of Spherical Volume (DSV) of a 7T MRI magnet. Compared to traditional LP optimization techniques, this method notably enhanced magnetic field uniformity by 98.5% and reduced the iron weight requirement by 91.7%, showcasing impressive performance. The proposed new passive shimming algorithm is more effective in improving magnetic field uniformity for MRI applications.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"28419"},"PeriodicalIF":3.9000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12322119/pdf/","citationCount":"0","resultStr":"{\"title\":\"A novel passive shimming optimization method of MRI magnet based on a PSA-SQP hybrid algorithm.\",\"authors\":\"Jie Zhao, Minhua Zhu, Ling Xia, Yifeng Fan, Feng Liu\",\"doi\":\"10.1038/s41598-025-13751-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In Magnetic Resonance Imaging (MRI), achieving a highly uniform main magnetic field (B<sub>0</sub>) is essential for producing detailed images of human anatomy. Passive Shimming (PS) is a technique used to enhance B<sub>0</sub> uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using Linear Programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. This work aims to improve the efficacy of passive shimming by optimally balancing field quality, iron usage, and harmonics, leading to a smoother field profile. This study introduces a hybrid algorithm that combines a Pattern Search Algorithm with Sequential Quadratic Programming (PSA-SQP) to enhance shimming performance. Additionally, a regularization method is employed to effectively reduce the use of iron pieces. The magnetic field improved from 462 ppm to 6.7 ppm, utilizing merely 0.8 kg of iron in a 400 mm Diameter of Spherical Volume (DSV) of a 7T MRI magnet. Compared to traditional LP optimization techniques, this method notably enhanced magnetic field uniformity by 98.5% and reduced the iron weight requirement by 91.7%, showcasing impressive performance. The proposed new passive shimming algorithm is more effective in improving magnetic field uniformity for MRI applications.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"28419\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12322119/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-13751-4\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-13751-4","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A novel passive shimming optimization method of MRI magnet based on a PSA-SQP hybrid algorithm.
In Magnetic Resonance Imaging (MRI), achieving a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive Shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using Linear Programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. This work aims to improve the efficacy of passive shimming by optimally balancing field quality, iron usage, and harmonics, leading to a smoother field profile. This study introduces a hybrid algorithm that combines a Pattern Search Algorithm with Sequential Quadratic Programming (PSA-SQP) to enhance shimming performance. Additionally, a regularization method is employed to effectively reduce the use of iron pieces. The magnetic field improved from 462 ppm to 6.7 ppm, utilizing merely 0.8 kg of iron in a 400 mm Diameter of Spherical Volume (DSV) of a 7T MRI magnet. Compared to traditional LP optimization techniques, this method notably enhanced magnetic field uniformity by 98.5% and reduced the iron weight requirement by 91.7%, showcasing impressive performance. The proposed new passive shimming algorithm is more effective in improving magnetic field uniformity for MRI applications.
期刊介绍:
We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections.
Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021).
•Engineering
Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live.
•Physical sciences
Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics.
•Earth and environmental sciences
Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems.
•Biological sciences
Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants.
•Health sciences
The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.