Design and Validation of a Cellulose Nanofiber-based Phantom for Modeling Extracellular Water Diffusion

Authors

  • Kazuya Sakoda Department of Radiological Technology, Kagoshima Medical Technology College https://orcid.org/0000-0001-7014-7057
  • Shogo Baba Information Technology Center, Seinan Gakuin University, Fukuoka, Japan

DOI:

https://doi.org/10.7577/radopen.6648

Abstract

Introduction: The current study aimed to develop a novel diffusion phantom composed of cellulose nanofiber (CNF) and glass beads (GBs), and evaluate its validation for quantitative diffusion-weighted imaging (DWI) using short-term apparent diffusion coefficient (ADC) stability and microstructural correlation.

Methods: A cross-sectional, A series of phantoms were prepared by mixing CNF with varying amounts of GBs. Seven phantoms with GB concentrations ranging from 0 to 8.4 g per 30 ml were scanned on a 3.0 T MRI at days 0, 30, 60, and 90 using a single-shot EPI DWI (b = 0, 1000 s/mm²).

Results: ADC and CV values were derived. A strong negative correlation was observed between GB density and ADC (r = –0.998, p < 0.001). CVs remained below 5% for all GB concentrations except the highest (8.4 g), indicating excellent stability.

Conclusion: The CNF based phantom offers a stable model for simulating restricted diffusion, overcoming drawbacks of existing phantoms. Its performance may support future applications in DWI protocol standardization and preliminary validation of quantitative ADC measurements.

References

1. Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 2000; 43 :828-836. https://doi.org/10.1002/1522-2594(200006)43:6%3C828::AID-MRM8%3E3.0.CO;2-P

2. Schnapauff D, Zeile M, Niederhagen MB, Fleige B, Tunn PU, Hamm B, et al. Diffusion-weighted echo-planar magnetic resonance imaging for the assessment of tumor cellularity in patients with soft-tissue sarcomas. J Magn Reson Imaging 2009; 29 :1355-1359. https://doi.org/10.1002/jmri.21755

3. Kishimoto K, Tajima S, Maeda I, Takagi M, Ueno T, Suzuki N, et al. Endometrial cancer: correlation of apparent diffusion coefficient (ADC) with tumor cellularity and tumor grade. Acta Radiol 2016; 57:1021-1028. https://doi.org/10.1177/0284185115612249

4. Liu Y, Ye Z, Sun H, Bai R. Grading of uterine cervical cancer by using the ADC difference value and its correlation with microvascular density and vascular endothelial growth factor. Eur Radiol 2013; 23:757-765. https://doi.org/10.1007/s00330-012-2657-1

5. Santucci D, Faiella E, Calabrese A, Beomonte Zobel B, Ascione A, Cerbelli B, et al. On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior. Cancers (Basel) 2021; 13:5167. https://doi.org/10.3390/cancers13205167

6. Surov A, Gottschling S, Mawrin C, Prell J, Spielmann RP, Wienke A, et al. Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters. Transl Oncol 2015; 8:517-523. https://doi.org/10.1016/j.tranon.2015.11.012

7. Sakoda K, Baba S. Comparison of apparent diffusion coefficient (ADC) values obtained by echo planar imaging diffusion-weighted imaging (DWI) and radial acquisition regime DWI in low field MRI systems: A phantom study. Radiography (Lond) 2024; 30:1290-1296. https://doi.org/10.1016/j.radi.2024.07.005

8. Laubach HJ, Jakob PM, Loevblad KO, Baird AE, Bovo MP, Edelman RR, et al. A phantom for diffusion-weighted imaging of acute stroke. J Magn Reson Imaging 1998; 8:1349-1354. https://doi.org/10.1002/jmri.1880080627

9. Tofts PS, Lloyd D, Clark CA, Barker GJ, Parker GJ, McConville P, et al. Test liquids for quantitative MRI measurements of self-diffusion coefficient in vivo. Magn Reson Med 2000; 43:368-374. https://doi.org/10.1002/(sici)1522-2594(200003)43:3%3C368::aid-mrm8%3E3.0.co;2-b

10. Maekawa T, Hori M, Murata K, Feiweier T, Fukunaga I, Andica C, et al. Changes in the ADC of diffusion-weighted MRI with the oscillating gradient spin-echo (OGSE) sequence due to differences in substrate viscosities. Jpn J Radiol 2018; 36:415-420. https://doi.org/10.1007/s11604-018-0737-0

11. Amouzandeh G, Chenevert TL, Swanson SD, Ross BD, Malyarenko DI. Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions. Med Phys 2022; 49:3325-3332. https://doi.org/10.1002/mp.15556

12. Paudyal R, Konar AS, Obuchowski NA, Hatzoglou V, Chenevert TL, Malyarenko DI, et al. Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings. Tomography 2019; 5:15-25. https://doi.org/10.18383/j.tom.2018.00044

13. Liu YJ, Tsai TS, Li YH, Peng JH, Chang HC, Peng HH, et al. Understanding ADC variation by fat content effect using a dual-function MRI phantom. Eur Radiol Exp 2024; 8:19. https://doi.org/10.1186/s41747-023-00414-0

14. Portakal ZG, Shermer S, Jenkins C, Spezi E, Perrett T, Tuncel N, et al. Design and characterization of tissue-mimicking gel phantoms for diffusion kurtosis imaging. Med Phys 2018; 45:2476-2485. https://doi.org/10.1002/mp.12907

15. Mikayama R, Yabuuchi H, Matsumoto R, Kobayashi K, Yamashita Y, Kimura M, et al. Development of a new phantom simulating extracellular space of tumor cell growth and cell edema for diffusion-weighted magnetic resonance imaging. MAGMA 2020; 33:507-513. https://doi.org/10.1007/s10334-019-00823-6

16. K. Sakoda, S. Baba. Evaluation of the Short- and Long-Term Stability of Apparent Diffusion Coefficient Using the Commercial Product Pseudo-Blood®. Appl Magn Reson 2025; 56:759–768. https://doi.org/10.1007/s00723-025-01754-3

17. Antony Jose S, Cowan N, Davidson M, Godina G, Smith I, Xin J, et al. A Comprehensive Review on Cellulose Nanofibers, Nanomaterials, and Composites: Manufacturing, Properties, and Applications. Nanomaterials (Basel) 2025; 15:356. https://doi.org/10.3390/nano15050356

18. Sharma A, Mandal T, Goswami S. Dispersibility and Stability Studies of Cellulose Nanofibers: Implications for Nanocomposite Preparation. J Polym Environ 2021; 29: 1516–1525. https://doi.org/10.1007/s10924-020-01974-7

19. Wong OL, Yuan J, Zhou Y, Yu SK, Cheung KY. Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence. Med Phys 2021; 48:1239-1249. https://doi.org/10.1002/mp.14686

20. Miquel ME, Scott AD, Macdougall ND, Boubertakh R, Bharwani N, Rockall AG. In vitro and in vivo repeatability of abdominal diffusion-weighted MRI. Br J Radiol 2012; 85:1507-12. https://doi.org/10.1259/bjr/32269440

21. Gatidis S, Schmidt H, Martirosian P, Schwenzer NF. Development of an MRI phantom for diffusion-weighted imaging with independent adjustment of apparent diffusion coefficient values and T2 relaxation times. Magn Reson Med 2014; 72:459-463. https://doi.org/10.1002/mrm.24944

22. Andrasko J. Water in agarose gels studied by nuclear magnetic resonance relaxation in the rotating frame. Biophys J 1975; 15:1235-1243. https://doi.org/10.1016/s0006-3495(75)85896-6

23. Manenti G, Di Roma M, Mancino S, Bartolucci DA, Palmieri G, Mastrangeli R, et al. Malignant renal neoplasms: correlation between ADC values and cellularity in diffusion weighted magnetic resonance imaging at 3 T. Radiol Med 2008; 113:199-213. https://doi.org/10.1007/s11547-008-0246-9

24. Jenkinson MD, du Plessis DG, Smith TS, Brodbelt AR, Joyce KA, Walker C. Cellularity and apparent diffusion coefficient in oligodendroglial tumours characterized by genotype. J Neurooncol 2010; 96:385-392. https://doi.org/10.1007/s11060-009-9970-9

25. Eidel O, Neumann JO, Burth S, Kieslich PJ, Jungk C, Sahm F, et al. Automatic Analysis of Cellularity in Glioblastoma and Correlation with ADC Using Trajectory Analysis and Automatic Nuclei Counting. PLoS One 2016; 11:e0160250. https://doi.org/10.1371/journal.pone.0160250

26. Pi S, Cao R, Qiang JW, Guo YH. Utility of DWI with quantitative ADC values in ovarian tumors: a meta-analysis of diagnostic test performance. Acta Radiol 2018; 59:1386-1394. https://doi.org/10.1177/0284185118759708

27. Tordjman M, Mali R, Madelin G, Prabhu V, Kang SK. Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis. Eur Radiol 2020; 30:4023-4038. https://doi.org/10.1007/s00330-020-06740-w

28. Wang QP, Lei DQ, Yuan Y, Xiong NX. Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e19254. https://doi.org/10.1097/md.0000000000019254

29. Xing H, Song CL, Li WJ. Meta analysis of lymph node metastasis of breast cancer patients: Clinical value of DWI and ADC value. Eur J Radiol 2016; 85:1132-1137. https://doi.org/10.1016/j.ejrad.2016.03.019

30. Reynaud O, Winters KV, Hoang DM, Wadghiri YZ, Novikov DS, Kim SG. Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas. NMR Biomed 2016; 29:1350-1363. https://doi.org/10.1002/nbm.3577

31. Jiang X, Li H, Xie J, Zhao P, Gore JC, Xu J. Quantification of cell size using temporal diffusion spectroscopy. Magn Reson Med 2016; 75:1076-1085. https://doi.org/10.1002/mrm.25684

32. Fritz V, Martirosian P, Machann J, Thorwarth D, Schick F. Soy lecithin: A beneficial substance for adjusting the ADC in aqueous solutions to the values of biological tissues. Magn Reson Med 2023; 89:1674-1683. https://doi.org/10.1002/mrm.29543

33. Boursianis T, Kalaitzakis G, Pappas E, Karantanas AH, Maris TG. MRI diffusion phantoms: ADC and relaxometric measurement comparisons between polyacrylamide and agarose gels. Eur J Radiol 2021; 139:109696. https://doi.org/10.1016/j.ejrad.2021.109696

34. Kamimura K, Kamimura Y, Nakano T, Hasegawa T, Nakajo M, Yamada C, et al. Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI. Cancer Imaging 2023; 23:75. https://doi.org/10.1186/s40644-023-00595-2

35. Ichikawa K, Taoka T, Ozaki M, Sakai M, Yamaguchi H, Naganawa S. Impact of tissue properties on time-dependent alterations in apparent diffusion coefficient: a phantom study using oscillating-gradient spin-echo and pulsed-gradient spin-echo sequences. Jpn J Radiol 2022; 40:970-978. https://doi.org/10.1007/s11604-022-01281-2

36. Wu D, Zhang J. The Effect of Microcirculatory Flow on Oscillating Gradient Diffusion MRI and Diffusion Encoding with Dual-Frequency Orthogonal Gradients (DEFOG). Magn Reson Med 2017; 77:1583-1592. https://doi.org/10.1002/mrm.26242

Downloads

Published

2026-08-21

How to Cite

Sakoda, K., & Baba, S. (2026). Design and Validation of a Cellulose Nanofiber-based Phantom for Modeling Extracellular Water Diffusion. Radiography Open, 12(1), 17–27. https://doi.org/10.7577/radopen.6648

Issue

Section

Technical Developments

Cited by