Extraction method of nanoparticles concentration distribution from magnetic particle image and its application in thermal damage of magnetic hyperthermia
Yundong Tang(汤云东)1,†, Ming Chen(陈鸣)1, Rodolfo C.C. Flesch2, and Tao Jin(金涛)3
1 College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; 2 Departamento de Automação e Sistemas, Universidade Federal de Santa Catarina, 88040-900 Florianópolis, SC, Brazil; 3 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Abstract Magnetic particle imaging (MPI) technology can generate a real-time magnetic nanoparticle (MNP) distribution image for biological tissues, and its use can overcome the limitations imposed in magnetic hyperthermia treatments by the unpredictable MNP distribution after the intratumoral injection of nanofluid. However, the MNP concentration distribution is generally difficult to be extracted from MPI images. This study proposes an approach to extract the corresponding concentration value of each pixel from an MPI image by a least squares method (LSM), which is then translated as MNP concentration distribution by an interpolation function. The resulting MPI-based concentration distribution is used to evaluate the treatment effect and the results are compared with the ones of two baseline cases under the same dose: uniform distribution and MPI-based distribution considering diffusion. Additionally, the treatment effect for all these cases is affected by the blood perfusion rate, which is also investigated deeply in this study. The results demonstrate that the proposed method can be used to effectively reconstruct the concentration distribution from MPI images, and that the weighted LSM considering a quartic polynomial for interpolation provides the best results with respect to other cases considered. Furthermore, the results show that the uniformity of MNP distribution has a positive correlation with both therapeutic temperature distribution and thermal damage degree for the same dose and a critical power dissipation value in the MNPs. The MNPs uniformity inside biological tissue can be improved by the diffusion behavior after the nanofluid injection, which can ultimately reflect as an improvement of treatment effect. In addition, the blood perfusion rate considering local temperature can have a positive effect on the treatment compared to the case which considers a constant value during magnetic hyperthermia.
Yundong Tang(汤云东), Ming Chen(陈鸣), Rodolfo C.C. Flesch, and Tao Jin(金涛) Extraction method of nanoparticles concentration distribution from magnetic particle image and its application in thermal damage of magnetic hyperthermia 2023 Chin. Phys. B 32 094401
[1] Usov N A and Gubanova E M 2020Nanomaterials10 1320 [2] Albarqi H A, Demessie A A, Sabei F Y, Moses A S, Hansen M N, Dhagat P, Taratula O R and Taratula O 2002Pharmaceutics12 1020 [3] Gu Y Y, Yoshikiyo M, Namai A, Bonvin D, Martinez A, Pinol R, Tellez P, Silva N J O, Ahrentorp F, Johansson C, Marco-Brualla J, Moreno-Loshuertos R, Fernandez-Silva P, Cui Y W, Ohkoshi S I and Millan A 2020RSC Adv.10 28786 [4] Li W Y, Li W T, Li B Q, Dong L J, Meng T H, Huo G, Liang G Y and Lu X G 2021Chin. Phys. B30 104402 [5] Yu X G, Ding S W, Yang R P, Wu C W and Zhang W 2021Ceram. Int.47 5909 [6] Abu-Bakr A F and Zubarev A Y 2020Eur. Phys. J. Spec. Top.229 323 [7] Tang Y D, Wang Y S, Flesch R C C and Jin T 2023J. Phys. D: Appl. Phys.56 145402 [8] Tang Y D, Zou J, Flesch R C C and Jin T 2022Chin. Phys. B32 034304 [9] Rytov R A, Bautin V A and Usov N A 2022Sci. Rep.12 3023 [10] Tang Y D, Zou J, Flesch R C C and Jin T 2023Appl. Math. Model114 583 [11] Yan X H, Xu Z Y, Chen W H and Pan Y 2021Comput. Biol. Med.128 104105 [12] Tay Z W, Savliwala S, Hensley D W, Fung K L B, Colson C, Fellows B D, Zhou X Y, Huynh Q, Lu Y, Zheng B, Chandrasekharan P, Rivera-Jimenez S M, Rinaldi-Ramos C M and Conolly S M 2021Small Methods5 2100796 [13] Tay Z W, Hensley D W, Chandrasekharan P, Zheng B and Conolly S M 2020IEEE Trans. Med. Imaging39 1724 [14] Sebastian A R, Ryu S H, Ko H M and Kim S H 2019IEEE Access7 96094 [15] Droigk C, Maass M and Mertins A 2022Phys. Med. Biol.67 045014 [16] Murase K, Aoki M, Banura N, Nishimoto K, Mimura A, Kuboyabu T and Yabata I 2015OJMI05 85 [17] Lu Y, Rivera-Rodriguez A, Tay Z W, Hensley D, Fung K L B, Colson C, Saayujya C, Huynh Q, Kabuli L, Fellows B, Chandrasekharan P, Rinaldi C and Conolly S 2020Int. J. Hyperther.37 141 [18] Zheng B, von See M P, Yu E, Gunel B, Lu K, Vazin T, Schaffer D V, Goodwill P W and Conolly S M 2016Theranostics6 291 [19] Marco A and Martínez J J 2010Linear Algebra Appl.433 1254 [20] Wang G J, Li W J, Zhang L P, Sun L J, Chen P, Yu L N and Ning X 2022IEEE Trans. Neural. Netw. Learn. Syst.33 3264 [21] Dyer S A and He X 2001IEEE Instrum. Meas. Mag.4 46 [22] Adepitan J O, Usikalu M R and Falayi E O 2019J. Phys. Conf. Ser.1299 012045 [23] Zhou R G and Wan C 2021Int. J. Theor. Phys.60 2115 [24] Li J, Su J and Zeng X 2019Comput. Opt.43 99 [25] Etminan A, Dahaghin A, Emadiyanrazavi S, Salimibani M, Eivazzadeh-Keihan R, Haghpanahi M and Maleki A 2022J. Therm. Biol.110 103371 [26] Astefanoaei I, Dumitru I, Stancu A and Chiriac H 2014Chin. Phys. B23 044401 [27] Ding S W, Wu C W, Yu X G, Dai C, Zhang W and Gong J P 2022Magnetochemistry8 63 [28] Rosensweig R E 2002J. Magn. Magn. Mater.252 370 [29] Yu X, Mi Y, Wang L C, Li Z R, Wu D A, Liu R S and He S L 2021Chin. Phys. B30 017503 [30] Polychronopoulos N D, Gkountas A A, Sarris I E and Spyrou L A 2021Appl. Sci.11 9526 [31] Tang Y D, Zou J, Flesch R C C, Jin T and He M H 2022Chin. Phys. B31 014401 [32] Kandala S K, Sharma A, Mirpour S, Liapi E and Ivkov R 2021Int. J. Hyperther.38 611 [33] Astefanoaei I, Dumitru I, Chiriac H and Stancu A 2016IEEE T. Magn.52 7403105 [34] Attaluri A, Kandala S K, Wabler M, Zhou H, Cornejo C, Armour M, Hedayati M, Zhang Y G, DeWeese T L, Herman C and Ivkov R 2015Int. J. Hyperther.31 359 [35] Rodrigues H F, Capistrano G, Mello F M, Zufelato N, Silveira-Lacerda E and Bakuzis A F 2017Phys. Med. Biol.62 4062 [36] He J T, Liu C, Li T, Liu Y W, Wang S C, Zhang J, Chen L, Wang C, Feng Y B, Floris G, Wang Z Q, Zhang X, Zhao L W, Li Y, Shao H B and Ni Y C 2020Int. J. Med.17 2269
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
