Skip to main content
Log in

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting

  • Research Article
  • Published:
Molecular Imaging and Biology Aims and scope Submit manuscript



Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo.


We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF) to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging.


The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve.


Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

All data and code for this study are available from the corresponding authors upon reasonable request.

All applicable institutional and/or national guidelines for the care and use of animals were followed.


  1. Gleich B, Weizenecker J (2005) Tomographic imaging using the nonlinear response of magnetic particles. Nature 7046:1214–1217

    Article  Google Scholar 

  2. Goodwill PW, Saritas EU, Croft LR et al (2012) X-space MPI: magnetic nanoparticles for safe medical imaging. Adv Mater 28:3870–3877

    Article  Google Scholar 

  3. Knopp T, Sattel TF, Biederer S et al (2010) Model-based reconstruction for magnetic particle imaging. IEEE Trans Med Imaging 1:12–18

    Article  Google Scholar 

  4. Vogel P, Lother S, Rückert MA et al (2014) MRI meets MPI: a bimodal MPI-MRI tomograph. IEEE Trans Med Imaging 10:1954–1959

    Article  Google Scholar 

  5. Goodwill PW, Conolly SM (2010) The formulation of the magnetic particle imaging process: 1-D signal, resolution, bandwidth, SNR, SAR, and magnetostimulation. IEEE Trans Med Imaging 11:1851–1859

    Article  Google Scholar 

  6. Konkle JJ, Goodwill PW, Carrasco-Zevallos OM, Conolly SM (2013) Projection reconstruction magnetic particle imaging. IEEE Trans Med Imaging 2:338–347

    Article  Google Scholar 

  7. Knopp T, Weber A (2013) Sparse reconstruction of the magnetic particle imaging system matrix. IEEE Trans Med Imaging 8:1473–1480

    Article  Google Scholar 

  8. Yin L, Li W, Du Y et al (2022) Recent developments of the reconstruction in magnetic particle imaging. Vis Comput Ind Biomed Art 1:24

    Article  Google Scholar 

  9. Parkins KM, Melo KP, Chen Y, Ronald JA, Foster PJ (2021) Visualizing tumor self-homing with magnetic particle imaging. Nanoscale 12:6016–6023

    Article  Google Scholar 

  10. Wang G, Li W, Shi G et al (2022) Sensitive and specific detection of breast cancer lymph node metastasis through dual-modality magnetic particle imaging and fluorescence molecular imaging: a preclinical evaluation. Eur J Nucl Med Mol Imaging 8:2723–2734

    Article  Google Scholar 

  11. Zhang W, Liang X, Zhu L et al (2022) Optical magnetic multimodality imaging of plectin-1-targeted imaging agent for the precise detection of orthotopic pancreatic ductal adenocarcinoma in mice. EBioMedicine 80:104040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jiang Z, Han X, Du Y et al (2021) Mixed metal metal-organic frameworks derived carbon supporting ZnFe2O4/C for high-performance magnetic particle imaging. Nano Lett 7:2730–2737

    Article  Google Scholar 

  13. Du Y, Liu X, Liang Q, Liang X, Tian J (2019) Optimization and design of magnetic ferrite nanoparticles with uniform tumor distribution for highly sensitive MRI/MPI performance and improved magnetic hyperthermia therapy. Nano Lett 6:3618–3626

    Article  Google Scholar 

  14. Hayat H, Sun A, Hayat H et al (2021) Artificial intelligence analysis of magnetic particle imaging for islet transplantation in a mouse model. Mol Imaging Biol 1:18–29

    Article  Google Scholar 

  15. Sun A, Hayat H, Liu S et al (2021) 3D in vivo magnetic particle imaging of human stem cell-derived islet organoid transplantation using a machine learning algorithm. Front Cell Dev Biol 9:704483

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shen YS, Hu CE, Zhang P, Tian J, Hui H (2022) A novel software framework for magnetic particle imaging reconstruction. Int J Imaging Syst Tech 4:1119–1132

    Article  Google Scholar 

  17. Liu S, Chiu-Lam A, Rivera-Rodriguez A et al (2021) Long circulating tracer tailored for magnetic particle imaging. Nanotheranostics 3:348–361

    Article  Google Scholar 

  18. Lu K, Goodwill P, Zheng B, Conolly S (2018) Multi-channel acquisition for isotropic resolution in magnetic particle imaging. IEEE Trans Med Imaging 9:1989–1998

    Article  Google Scholar 

  19. Chan T, Wong C (1998) Total variation blind deconvolution. IEEE Trans Image Process 3:370–375

    Article  Google Scholar 

  20. Wen F, Ying R, Liu Y, Tk T (2020) A simple local minimal intensity prior and an improved algorithm for blind image deblurring. IEEE Trans Circuits Syst Video Technol 99:1–1

    Google Scholar 

  21. Pan J, Sun D, Pfister H, Yang M-H (2016) Blind image deblurring using dark channel prior [abstract]. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, Las Vegas, Nevada, pp 1628–1636

  22. Oh K, Shin CS, Kim J, Yoo SK (2019) Level-set segmentation-based respiratory volume estimation using a depth camera. IEEE J Biomed Health Inform 4:1674–1682

    Article  Google Scholar 

  23. Ma J, Nie Z, Wang C et al (2020) Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations. Phys Med Biol 22:225034

    Article  Google Scholar 

  24. Li C, Kao C, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 10:1940–1949

    Google Scholar 

  25. Sanchez-Salvador JL, Campano C, Lopez-Exposito P et al (2021) Enhanced morphological characterization of cellulose nano/microfibers through image skeleton analysis. Nanomaterials (Basel) 8:2077

    Article  Google Scholar 

  26. Cheng H, Xue M, Shi X (2003) Contrast enhancement based on a novel homogeneity measurement. Pattern Recogn 11:2687–2697

    Article  Google Scholar 

Download references


This work was funded by the National Key Research and Development Program of China (2017YFA0205200), National Natural Science Foundation of China (62027901, 81871514, 92159303, 82272111, 81227901, 81470083, 81527805); Beijing Natural Science Foundation (7212207). The Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai HLHPTP201703).

Author information

Authors and Affiliations



LW collected and analyzed the experimental data; YH and YSZ processed the in vivo image; LW and LZ wrote the manuscript; LZ, JT, and YD supervised and edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jie Tian, Lu Zhang or Yang Du.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 355 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Huang, Y., Zhao, Y. et al. Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting. Mol Imaging Biol 25, 788–797 (2023).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Key words