ReviewJournal of magnetic resonance imaging : JMRI2025
Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.
Review in Journal of magnetic resonance imaging : JMRI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- From Magnetic Moment to Magnetic Particle Imaging: A Comprehensive Review on MPI Technology, Tracer Design and Biological Applications.Pharmaceutics · 2026Review
- Computational design of a 3D magnetic particle imaging (MPI) prototype.AIP advances · 2026Article
- Multifunctional Nanoparticles in Traumatic Brain Injury: From Targeted Imaging and Diagnosis to Innovative Therapeutics.International journal of nanomedicine · 2026Review
- Cardiovascular Imaging Applications, Implementations, and Challenges Using Novel Magnetic Particle Imaging.Bioengineering (Basel, Switzerland) · 2025Review
- Precision nanomaterials in colorectal cancer: advancing photodynamic and photothermal therapy.RSC advances · 2025Review
- Spillover can limit accurate signal quantification in MPI.Npj imaging · 2025Article
- Exploring the diagnostic potential: magnetic particle imaging for brain diseases.Military Medical Research · 2025Review
- Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.Biomolecules · 2025Review
- Advances in Vascular Diagnostics using Magnetic Particle Imaging (MPI) for Blood Circulation Assessment.Advanced healthcare materials · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.