Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

• Published in: Journal of magnetic resonance imaging Vol. 61; no. 1; pp. 42 - 51
• Main Authors: Nigam, Saumya, Gjelaj, Elvira, Wang, Rui, Wei, Guo‐Wei, Wang, Ping
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2025; Wiley Subscription Services, Inc
• Subjects: Algorithms; Animals; Artificial intelligence; Deep Learning; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging techniques; Imaging, Three-Dimensional - methods; Induced voltage; Ionizing radiation; Learning algorithms; Machine Learning; Magnetic fields; magnetic particle imaging; Magnetic Resonance Imaging - methods; Magnetite Nanoparticles; Review; Spatial discrimination; Spatial resolution; Temporal resolution; deep learning; machine learning; artificial intelligence; magnetic particle imaging
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29294

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. Level of Evidence 5 Technical Efficacy Stage 1