Evidence map›Paper›PMID 41799560›Full record

ArticleAIP advances2026

Computational design of a 3D magnetic particle imaging (MPI) prototype.

Shahriar Mostufa, Bahareh Rezaei, Kai Wu

Abstract read
In one paragraph

Article in AIP advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shahriar MostufaDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409, USA.ORCID https://orcid.org/0000-0002-3326-4817
Bahareh RezaeiDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409, USA.
Kai WuDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409, USA.ORCID https://orcid.org/0000-0002-9444-6112

Funding

Additive Manufacturing Wearable Magnetic Sensors: Revolutionizing Cardiac Health Monitoring with Machine Learning for Arrhythmia ClassificationR16GM158539 · NIGMS · TEXAS TECH UNIVERSITY · PI Kai Wu · 2025 to 2026
$276k
Multi-tracer Magnetic Particle Imaging (MMPI): Tracer Design and Multi-tracer Guided Image ReconstructionR03EB036435 · NIBIB · TEXAS TECH UNIVERSITY · PI Kai Wu · 2025 to 2026
$157k
NIBIB NIH HHS R03 EB036435NIGMS NIH HHS R16 GM158539
6 · The paper itself

Abstract

Magnetic particle imaging (MPI) is an emerging imaging modality that exploits the magnetization response of magnetic nanoparticle tracers. While MPI offers substantially higher resolution compared to magnetic resonance imaging, its translation to human-scale applications remains limited. These challenges stem from the requirement of high-intensity electric currents to generate strong magnetic fields, as well as reduced field uniformity with increasing coil spacing. To overcome these barriers, comprehensive simulation studies are essential for guiding MPI prototype design and performance optimization. In this work, we present a finite element method (FEM)-based design of a three-dimensional (3D) MPI prototype. The system integrates electromagnetic coils for the selection, drive, and focus fields, along with a gradiometer configuration for signal reception. Each coil's geometry and magnetic field were first simulated independently to validate its ability to generate the desired magnetic field and subsequently combined into a full-system design with time-domain input excitation signals. This framework achieved 3D field-free point (FFP) scanning within a 20 mm

Identifiers

PMID41799560
PMCPMC12967246

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.