ArticleMolecular imaging and biology2021
Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.
Article in Molecular imaging and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 31 citations in OpenAlex.
- Systematic evaluation of magnetic particle imaging quantification strategies in biologically relevant scenarios using anatomically correct 3D printed mouse phantoms.Physics in medicine and biology · 2026Article
- From Magnetic Moment to Magnetic Particle Imaging: A Comprehensive Review on MPI Technology, Tracer Design and Biological Applications.Pharmaceutics · 2026Review
- On the effect of reference and sample volume on magnetic particle imaging quantification.Journal of magnetism and magnetic materials · 2025Article
- Sustainable Nanotechnology and Artificial Intelligence to Empower Image-Guided Therapy for Precision Healthcare.BME frontiers · 2025Review
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Advances in Vascular Diagnostics using Magnetic Particle Imaging (MPI) for Blood Circulation Assessment.Advanced healthcare materials · 2024Review
- Brown Adipose Tissue as a Unique Niche for Islet Organoid Transplantation: Insights From In Vivo Imaging.Transplantation direct · 2024Article
- Magnetic Particle Imaging Reveals that Iron-Labeled Extracellular Vesicles Accumulate in Brains of Mice with Metastases.ACS applied materials & interfaces · 2024Article
- Progress in magnetic particle imaging signal and iron quantification methodsNanoscale advances · 2023Article
- Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.iScience · 2023Article
- Sensitive and quantitative in vivo analysis of PD-L1 using magnetic particle imaging and imaging-guided immunotherapy.European journal of nuclear medicine and molecular imaging · 2023Article
- Principles and applications of magnetic nanomaterials in magnetically guided bioimaging.Materials today physics · 2023Article
- Magnetic Particle Imaging of Transplanted Human Islets Using a Machine Learning Algorithm.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Nanotechnology in Immunotherapy for Type 1 Diabetes: Promising Innovations and Future Advances.Pharmaceutics · 2022Review
- 3DFrontiers in cell and developmental biology · 2021Article
- Current Progress and Perspective: Clinical Imaging of Islet Transplantation.Life (Basel, Switzerland) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeCurrent approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis. PROCEDURES: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts.
resultsThe K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data.
conclusionsWe have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.
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.