Rhizophora-based particleboard bonded with soy flour and lignin as potential phantom

Abstract

Rhizophora-based particleboard was evaluated for its suitability as phantom material, especially in medical physics applications. The elemental composition, effective atomic number, micrographic structures, computed tomography (CT), and attenuation properties of Rhizophora-based particleboards were examined. These investigations considered three different particle sizes and three distinct adhesive mixtures. Rhizophora sample at particle sizes of 0 to 103 µm, with 4.5% soy flour and 1.5% lignin (C₆) presented with a homogenous compound with better uniformity compared with other samples, and Rhizophora sample at particle sizes of 104 to 210 µm, with 9% soy flour and 3% lignin (B₁₂) demonstrated an effective atomic number of 8.15, which is similar to water. C₆ also presented with a density distribution profile with close proximity to water. The measured attenuation coefficients of samples were aligned closely with those of water, as determined by XCOM. The results suggest that the formulation of soy flour and lignin as adhesives for Rhizophora-based particleboard is suitable for the fabricating of phantom material for medical physics applications, especially mainly due to its natural origin.

GRAPHICAL ABSTRACT

INTRODUCTION

Medical physics often focuses on techniques to prevent, diagnose, and treat human diseases, including cancers. Medical physics for diagnostic and therapy purposes often involves the utilisation of radiation in the technique to diagnose and treat patients. In radiation study, especially in the medical field, employment of phantom material is necessary to replace a real human body in the verification and measurement of radiation dose received by the patient. Previous studies often reported the usage of various kinds of phantoms in radiation dosimetry and also radiation protection (Katsarou et al. 2023; Luo et al. 2023).

Phantom is frequently recognised as a model that closely matches the human body and anatomy (Jusufbegović et al. 2023), composed of media with close equivalents to human soft tissue (Breslin et al. 2023) in terms of size, shape, location, density, and radiation interaction with matter. Dosimetric phantoms are model phantoms that were established in response to the evolution of computational phantoms to quantify and evaluate organ doses after irradiation, either internally or externally. The requirement to imitate the human body in radiation studies has prompted researchers to investigate numerous materials as phantoms; materials that have been declared adequate based on physico-mechanical, attenuation, and scattering investigations.

Characterisation of the phantom is vital to create a phantom that is nearly identical to a real human body, especially in terms of its attenuation and radiation properties. In order to fabricate a comparable phantom, analysis of its properties is necessary to determine the suitability as phantom material, especially in radiation study. Water has been shown to exhibit a similar affinity to soft tissue, and the IAEA has recognised it as a standard phantom. Water has predictable qualities that are suitable for radiation study and is commonly available, thus it is recommended for dosimetry reasons in radiation dosimetry (Samson et al. 2023). It is close to soft tissue in terms of mass density. However, the use of water as a phantom in any dosimetry process is quite challenging due to its irregular shape and size. One constraint is the difficulty in locating various types of radiation dosimeters because many of them are unsuitable for use in water. As a result, solid phantoms were created and developed as a replacement for water.

Rhizophoraas phantom material has received massive attention due to its close compatibility with water, and with the potential of replacing water as phantom in radiation dosimetry. Rhizophoraspp. is a type of mangrove tree that grows abundantly in the muddy tidal plain mostly found in the coastal area in Malaysia. The genus Rhizophora contains many species of mangrove (Abuarra et al. 2014). Although global mangrove distribution has fluctuated throughout geological history, Rhizophora spp. can be easily found in Malaysia, as Malaysia is the third among the top 20 mangrove-holding nations (Hamilton and Casey 2016). Rhizophora spp. commonly reaches up to five to eight metres, approximately at 16 to 26 feet, but it sometimes can reach up to 30 to 40 metres in height with growth of around 3.3 feet per year in height (Duke 2006).

Rhizophora spp. was first discovered as potential phantom material from the study by Sudin et al. (1988). One of the latest investigations of Rhizophoraas phantom material in high energy studies reported that there is good agreement of the radiological parameters determined using the scattered photon energies between the Rhizophora based phantom, solid water phantom, and theoretical values of water (XCOM), reflecting the validation of the Compton scattering technique setup (Samson et al. 2023). It is also crucial for a phantom material to be made environmentally friendly, with natural-based adhesives without any release of toxic gases throughout its usage. Thus, it is necessary to fine-tune its development and fabrication in order to produce a natural based phantom material without any inclusion of petroleum-based products, which may be harmful to the environment.

Previous literature reported various fabricated particleboards made of natural-based material, which were produced and analysed as phantom material in radiation study (Zuber et al. 2021b). Particleboard as potential phantom requires superior physical and mechanical properties, and in the form of slabs, the rigidity may improve tremendously, as the wood as a whole has natural variations in shape, density and structural integrity due to knots, grain patterns, and other imperfections.

In this work, Rhizophora-based particleboard bonded with soy flour and lignin was investigated in terms of its elemental composition, effective atomic number, micrographic structure, computed tomography (CT), and attenuation properties, to determine its suitability as phantom material.

EXPERIMENTAL

Preparation of Particleboard

Rhizophora-based particleboards bonded with different percentages of soy flour and lignin as an adhesive mixture were prepared at different various particle sizes (211 to 500, 104 to 210, 0 to103) µm. Soybean flour (type I) and lignin (alkali, low sulfonate content) (both in powder form) were purchased from Sigma Aldrich, Germany, and used as received. Rhizophorawood was first collected from a mangrove factory in Perak, before it underwent several processes – cleaning, debarking, drying, planing, grinding, and sieving to prepare for the fabrication procedure. During the planing process, the trunks were dried and cut further into smaller size using a surface planer machine. The reduced wood chips were then ground into smaller particles using a wood grinder. The particulates were allowed to dry for a few days before being sieved, and they were dried again before hot-pressing. For the fabrication process, hot press was used to produce the particleboard at a target density of 1.0 g·cm^-3, at a dimension of approximately (23.0 × 23.0 × 0.5) cm³. Equation 1 (Tousi et al. 2014) was used to compute the quantity of each component used in the fabrication of the particleboard,

Elemental Composition Analysis for Adhesive-Bonded Particleboard

Elemental composition of the samples was obtained using scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDXA) (FEI Quanta FEG-650, Netherlands) at the Centre for Global Archaeological Research Earth Material Characterisation Laboratory, Universiti Sains Malaysia. Each sample was fixed onto a specimen holder using a double-sided adhesive tape, and the images were captured under the magnification of 1000 ×, 2000 × and 4000 ×. The effective atomic number Z_eff was calculated by Eq. 2,

where a_i and z_i are electron fraction and atomic number of the i^th element in the sample respectively, andm is the experimental coefficient for biological materials and water with value of 3.4. The electron fraction of the i^th element was calculated by Eq. 3,

where w_i and A_i are fractional weight and atomic mass of the i^th element, respectively.

Analysis of Micrographic Structure of the Sample in ImageJ

The micrographs of the sample obtained from SEM-EDXA were analysed in ImageJ to evaluate the homogeneity of the particleboard. An average of circular area was obtained from ImageJ software and mean measurement was taken to evaluate the uniformity and homogeneity of the mixture.

CT Analyses of Sample in ImageJ

CT images and density distribution profiles of the particleboard were obtained using a medical X-ray CT scanner (Somatom Definition.AS+, Siemens, Germany). The samples, shaped cylindrically with dimensions approximately 7.0 cm in length and 3.0 cm in diameter, were assembled using wood adhesive. They were trimmed to achieve a smooth cylindrical form, aligning with the section of the CIRS 062 M water-equivalent electron density phantom. Following the production of plug-density phantoms from each sample, they were scanned using a reference water-equivalent plug-density phantom. The plug phantom inserts encompass variations for lung (inhale), adipose tissue, breast, bone, muscle, and liver. All samples were scanned using an X-ray peak energy set at 120 kVp and 187 mAs, utilising a tungsten X-ray target. During the scanning process, Adaptive Iterative Dose Reduction 3D (AIDR 3D) was implemented to minimise radiation dose and noise, while optimising image quality. Subsequently, the images were reconstructed with a filter kernel (FC18, without additional beam hardening correction) at a slice thickness of 1.0 mm. The full field of view image was utilised for each imaging modality. Analysis of the acquired images was conducted using the ImageJ.exe software, wherein the density profiles were generated based on CT raw data to create a plot profile across the phantom volume. A horizontal line was drawn across each plug phantom, and the average densities along the line were derived as shown in Fig. 1.

Fig. 1. Line profile for lateral distance measurement and analysis

Attenuation Analyses of Sample Using Monte Carlo GATE

The simulation utilized a custom-built personal computer (PC) with INTEL Core I9 14900K 24 Cores 32 Threads 3.2Ghz LGA1700 Processor with an Ubuntu 22.04.4 LTS operating system (OS) GATE v9.4 including the Geant4 v11.2.1 and Root v6.28/12 platforms. The samples’ linear and mass attenuation coefficients were determined using a Ludlum setup with a lead equivalent configuration. The source was positioned inside a shielded container, roughly 30.0 cm away from the Ludlum detector. The detector, which measures about 6.7 cm diameter and 22.9 cm in length, was equipped with a collimator featuring a 0.5 mm diameter and positioned around 30.0 cm above the surface. The sample was placed about 10.0 cm from the Ludlum detector. For this work, samples with a predetermined density of 1.0 g·cm^-3 were subjected to irradiation with a sealed radiation source of ¹³⁷Cs, which has an effective peak energy of 0.662 MeV. The setup was generated using GATE software, which utilised macro files to simulate the configuration, adapting the SPECTHead example for this specific work. The setup was modelled using the GATE MC package, simulating 10 million particle histories. After initialising ROOT for output, the simulation data were analysed to count events within a specified energy window. From this data, the linear and mass attenuation coefficients were calculated.

RESULTS AND DISCUSSION

Figure 2 illustrates the preparation of Rhizophora raw wood for the fabrication process which include the collection of the wood, cutting, debarking, planing, and grinding of the wood particles. Wood particles were then dried for a few hours up to a few days before the sieving process to prepare samples at three different particle sizes.

Fig. 2. (a) Wood truck collected from mangrove factory; (b) Debarking process of the wood trunk; (c) Planing process to produce wood particles; (d) Wood particle underwent grinding process; (e) Sieving process to prepare the wood particles at different particle sizes

Elemental Analysis for Adhesive-Bonded Particleboard

The elemental composition in atomic, weight percentiles and effective atomic number of Rhizophorasamples is described in Tables 1 and 2. Figure 3 demonstrates the spectrum processing for each sample.

Based on the result, the elemental composition of the samples in weight percentiles demonstrated high values in carbon and oxygen, representing the natural element of the wood particles, with the formulation of soy flour and lignin as natural-based adhesives. Previous literature reported that the elevated levels of carbon and oxygen in the composites stem from their increased flow rates during high-temperature processing, which is closely tied to the chemical makeup of the samples and holds noteworthy importance in the particleboard phantom (Samson et al. 2020). Such attributes contribute to enhanced performance as a dosimetric phantom, yielding superior outcomes in practical applications (Samson et al. 2020).

Table 1. Atomic Percentage of the Elemental Composition for Each Sample

A = 211 to 500 µm, B = 104 to 210 µm, C = 0 to 103 µm particle size ranges; 0 = 0% soy flour and lignin, 6 = 4.5% soy flour and 1.5% lignin, 12 = 9% soy flour and 3% lignin

Table 2. Weight Percentage and Effective Atomic Number Zeff of the Elemental Composition for Each Sample

A = 211 to 500 µm, B = 104 to 210 µm, C = 0 to 103 µm particle size ranges; 0 = 0% soy flour and lignin, 6 = 4.5% soy flour and 1.5% lignin, 12 = 9% soy flour and 3% lignin

There were several metal elements detected in the composition, including strontium, manganese and titanium in small fractions, which may be due to the exposure of the samples towards gold for conductivity during the scanning electron microscopy and energy-dispersive x-ray analyses. However, the minuscule percentile of the metal element is deemed negligible in this work. In this work, the effective atomic number was calculated and results demonstrated that B₁₂ presented with effective atomic number of 8.15, which is the closest to water as compared to other sample. This may be due to the presence of impurities such as the unnecessary metal elements.

Fig. 3. Spectrum processing for (a) A0; (b) A6; (c) A12; (d) B0; (e) B6; (f) B12; (g) C0; (h) C6; (i) C12 in EDX analysis

Analysis of Micrographic Structure of the Sample in Imagej

Table 3 illustrates the micrograph of the Rhizophora samples at different percentages of soy-lignin adhesives at (a) 1000 × (b) 2000 × and (c) 4000 ×. Based on the figure, sample A₀presented with discernible void spaces in between the particulates, indicating lack of homogeneity in between the Rhizophora wood particles without the presence of adhesive for bonding. The same was observed for B₀and C₀, whereas, for samples B₆and B_12, it is apparent that the fabricated samples did not reach a satisfactory homogeneity of compounds based on the micrograph, which may indicate the inadequate hot-pressing procedure during the fabrication process. Large void and air gaps presented in samples B₆and B₁₂suggested weak merging and bonding between the particulate, which requires further processing.

The ImageJ analysis for magnification of 1000 ×, 2000 × and 4000 × is shown in Table 4. Based on the grayscale histogram analysis, C₆ presented the smallest mean, indicating its superior homogeneity compared to other samples. ImageJ software was used to analyse SEM images to study the presence of void spaces in the fabricated sample (Thilagashanthi et al. 2021). C₆demonstrated a compound with little to no void or air gaps, stipulating its potential as phantom material. Multiple void spaces presented in the micrograph elucidate the inhomogeneity and non-uniformity within the compound post-hot-pressing, which may affect its physical and mechanical strength. Previous literature reported that the presence of voids resulting from lumen and hole formation in composites can cause uneven distribution and decreased density (Samson et al. 2020). The incorporation of adhesives in the formulation stipulated the improved inhomogeneity, as manifested in C₆, with potential of enhanced physical and mechanical properties (Samson et al. 2020). It is crucial for phantom material made from particleboard to be superior in mechanical stability. This will allow for a higher usage rate, thus contributing to cost-effectiveness.

Table 3. Micrograph of the Rhizophora Samples at Different Percentages of Soy-Lignin Adhesives at (a) 1000 × (b) 2000 × and (c) 4000 ×

A = 211 to 500 µm, B = 104 to 210 µm, C = 0 to 103 µm particle size ranges; 0 = 0% soy flour and lignin, 6 = 4.5% soy flour and 1.5% lignin, 12 = 9% soy flour and 3% lignin

Table 4. The ImageJ analysis at 1000 ×, 2000 × and 4000 × Magnification

4.5% soy flour and 1.5% lignin, 12 = 9% soy flour and 3% lignin

CT Analyses of Sample in Image J

The CT numbers given by the Hounsfield unit (HU) were a result of the density of the material used and the scale range from -1000 (indicating darker shades) to +1000 (indicating lighter shades). Lower density causes darker shades of grey, whereas higher density value produces lighter shades. The HU value of air is -1000, and denser material, such as bone, has a value of +1000. Water has an HU value of 0, and the HU of soft tissue ranges from -200 to +200 (Sichel et al. 2000). Based on the average CT number, the closest CT number to water was recorded by C₁₂, (Zuber et al. 2021a) indicating that C₁₂has comparable attenuation properties to water. These results align with previous studies (Marashdeh et al. 2012; Mohd Yusof et al. 2017).

CT numbers between -200 and +200 HU were similar to water and soft tissues (Zuber et al. 2021a). The mean CT number may be affected by the inconsistent application of adhesives in the sample. Larger discrepancies occurred when the value was compared with other average CT number, indicating lack of homogeneity due to larger particles. However, all the samples were within the required CT number for soft tissue. Figure 4 shows the fabricated plug phantom, whereas Fig. 5 illustrates the CT images of the plug phantom at various percentages of soy flour and lignin at different particle sizes. The electron density profiles of the samples are shown in Figs. 6 through 8.

Fig. 4. Fabricated plug phantom at different percentages of adhesives and different particle sizes

Close proximity of densities across the plug phantoms can be seen at 0 to 103 µm particle sizes, as compared to plug phantom at bigger particle sizes, in sample bonded with 12% adhesives. The same goes with plug phantom bonded with 6% adhesives. Based on the figures, the density distribution profile of adhesive bonded samples presented with close proximities to those of water at the smallest particle sizes of 0 to 103 µm, with C₆to be the closest of them all. The density profile of binderless sample at 104 to 210 µm particle sizes also was revealed to be closest to those of water. In this study, uniformity across the profile can be observed for majority of the samples, indicating little variation in the uniformity of the fabricated samples. This is largely attributed to the methodology employed during the formulation mixing, cold-pressing, and hot-pressing processes. Disparity in terms of density for both adhesives may result in a heterogeneous grayscale appearance if not thoroughly mixed within the plug phantom.

Fig. 5. CT images of fabricated plug phantoms at various percentages of soy flour and lignin and at different particle sizes

Fig. 6. Density profiles for binderless samples

Fig. 7. Density profile for samples with 6% adhesives

geometric arrangement of the lead equivalent configuration as modelled in GATE. Table 5 depicts the linear and mass attenuation coefficients of the samples in comparison to those of water calculated with the XCOM. The measured attenuation coefficients were aligned closely with those of water, as determined by XCOM, a widely recognised database for photon cross sections, with C₁₂ and A₁₂ presented with the closest values to water.

Fig. 9. (a) Geometric arrangement of the lead equivalent configuration as modeled in GATE; (b) Visualisation of energy transmission for attenuation simulated in GATE

Table 5. Simulated Linear and Mass Attenuation Coefficients of the Samples in Comparison to Those of Water Calculated with the XCOM

A = 211 to 500 µm, B = 104 to 210 µm, C = 0 to 103 µm particle size ranges; 0 = 0% soy flour and lignin, 6 = 4.5% soy flour and 1.5% lignin, 12 = 9% soy flour and 3% ligninµ (cm^-1) = linear attenuation coefficient, µ/ρ (cm²g^-1) = mass attenuation coefficient

This alignment is particularly noteworthy because water is used as a reference standard in radiological studies due to its compositional similarities to human soft tissue (Büyükyıldız et al. 2024). This close agreement is significant because water’s attenuation properties are often used as a benchmark in radiological contexts, given its similarity to human soft tissue in terms of composition and density (Zuber et al. 2021b). Thus, the material in question closely mimics the radiological properties of human soft tissue.

Human soft tissues, such as muscle, fat, and organs, consist largely mainly of water (Meher et al. 2024), contributing to their similar radiological behavior. Thus, materials that exhibit attenuation coefficients close to those of water are likely to replicate the interaction between human tissues and ionising radiation accurately. Such materials are excellent candidates for use as tissue-equivalent materials in medical physics applications. By closely mimicking the radiological properties of human tissues, these materials can be used in the calibration and testing of medical imaging devices, as well as in research and training settings (McGarry et al. 2020). This enables more accurate dosimetry, improved imaging techniques, and better training tools which are crucial for advancing medical technology and ensuring patient safety during radiological procedures. Hence, the close agreement between the measured coefficients and those of water supports the potential of the evaluated material to serve as a realistic surrogate for human tissue in various medical physics applications.

CONCLUSIONS

1. The findings highlight the promising potential of Rhizophora-based particleboard, bonded with soy flour and lignin, for medical physics applications with good physical and mechanical properties.

2. The C₆ sample exhibited good uniformity and homogeneity, indicating its potential as a phantom material.

3. Notably, the B₁₂ sample demonstrated an effective atomic number of 8.15, closely resembled that of water, suggesting its suitability for medical physics applications.

4. The C₆ also exhibited a density distribution profile that closely resembled that of water.

5. The attenuation coefficients of the samples closely matched those of water, as determined by XCOM.

ACKNOWLEDGMENTS

The authors acknowledge the Universiti Kebangsaan Malaysia Geran Galakan Penyelidik Muda (GGPM-2023-027), Universiti Sains Malaysia Short-Term Grant (304/PFIZIK/6315322) and the Universiti Sains Malaysia Bridging Grant (304.PPSK.6316324).

References

Abuarra, A., Hashim, R., Bauk, S., Kandaiya, S., and Tousi, E. T. (2014). “Fabrication and characterization of gum Arabic bonded Rhizophora spp. particleboards,” Mater. Des. 60, 108-115.

Breslin, T., Paino, J., Wegner, M., Engels, E., Fiedler, S., Forrester, H., Rennau, H., Bustillo, J., Cameron, M., Häusermann, D., Hall, C., Krause, D., Hildebrandt, G., Lerch, M., and Schültke, E. (2023). “A novel anthropomorphic phantom composed of tissue-equivalent materials for use in experimental radiotherapy: Design, dosimetry and biological pilot study,” Biomimetics 8(2), 230 DOI: 10.3390/biomimetics8020230

Büyükyıldız, M., Kaur, P., Thakur, S., and Atlı, R. (2024). “Study of phantom materials close to 1 keV photon energy for radiation applications,” Radiat. Phys. Chem. 215, article 111375. DOI: 10.1016/j.radphyschem.2023.111375

Duke, N. C. (2006). “Indo-West Pacific stilt mangroves: Rhizophora apiculata, R. mucronata, R. stylosa, R. X annamalai, R. X lamarckii,” Permanent Agriculture Resources (PAR).

Hamilton, S. E., and Casey, D. (2016). “Creation of a high spatio‐temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21),” Glob. Ecol. Biogeogr. 25, 729-738.

Hatton, J., McCurdy, B., and Greer, P. B. (2009). “Cone beam computerized tomography: the effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy,” Phys. Med. Biol. 54, N329-46. DOI: 10.1088/0031-9155/54/15/N01

Jusufbegović, M., Pandžić, A., Busuladžić, M., Čiva, L.M., Gazibegović-Busuladžić, A., Šehić, A., Vegar-Zubović, S., Jašić, R., and Beganović, A. (2023). “Utilisation of 3D printing in the manufacturing of an anthropomorphic paediatric head phantom for the optimisation of scanning parameters in CT,” Diagnostics 13(2), article 328. DOI: 10.3390/diagnostics13020328

Katsarou, M., Zwiebel, B., Chowdhury, R.P., Shames, M., Berger, T., Przybyla, B., and Bismuth, J. (2023). “Experimental analysis of radiation protection offered by a novel exoskeleton-based radiation protection system versus conventional lead aprons,” J. Vasc. Interv. Radiol. 34(8), 1345-1352 DOI: 10.1016/j.jvir.2023.03.033

Luo, X., Qiu, R., Wu, Z., Yan, S., Zhang, H., and Li, J. (2023). “A body-size-dependent series of Chinese adult standing phantoms for radiation dosimetry,” J. Radiol. Prot. 43, article 11501. DOI: 10.1088/1361-6498/acad0d

Marashdeh, M. W., Bauk, S., Tajuddin, A. A., and Hashim, R. (2012). “Measurement of mass attenuation coefficients of Rhizophora spp. binderless particleboards in the 16.59–25.26keV photon energy range and their density profile using x-ray computed tomography,” Appl. Radiat. Isot. 70, 656-662. DOI: 10.1016/j.apradiso.2012.01.008

McGarry, C. K., Grattan, L. J., Ivory, A. M., Leek, F., Liney, G. P., Liu, Y., Miloro, P., Rai, R., Robinson, A., Shih, A. J., Zeqiri, B., and Clark, C. H. (2020). “Tissue mimicking materials for imaging and therapy phantoms: A review,” Phys. Med. Biol. 65(23), 23TR01 DOI: 10.1088/1361-6560/abbd17

Meher, A. K., Kumar, E. K., Gangwar, A., Panda, S. K., and Pradhan, R. C. (2024). “Review on mechanobiological analysis and computational study of human tissue (soft and hard) using machine learning techniques: A mechanical perspective,” Arch. Comput. Methods Eng. 31, 957-972. DOI: 10.1007/s11831-023-10003-4

Samson, Damilola Oluwafemi, Aziz, M. Z. A., Shukri, A., Jafri, M. Z. M., Hashim, R., Zuber, S. H., Hashikin, N. A. A., Rabba, J. A., Samson, P. A., and Yusof, M. F. M. (2023). “Radiological and dosimetric evaluation of biomaterial composite phantoms with high energy photons and electrons,” Health Phys. 10-1097.

Samson, D. O., Jafri, M. Z. M., Shukri, A., Hashim, R., Sulaiman, O., Aziz, M. Z. A., and Yusof, M. F. M. (2020). “Measurement of radiation attenuation parameters of modified defatted soy flour–soy protein isolate-based mangrove wood particleboards to be used for CT phantom production,” Radiat. Environ. Biophys. 59, 483-501. DOI: 10.1007/s00411-020-00844-z

Samson, Damilola O, Shukri, A., Hashikin, N. A. A., Zuber, S. H., Aziz, M. Z., Hashim, R., Yusof, M. F., Rabaiee, N. A., and Gemanam, S. J. (2023). “Dosimetric characterization of DSF/NaOH/IA-PAE/R. spp. phantom material for radiation therapy,” Polymers (Basel). DOI: 10.3390/polym15010244

Sichel, J. Y., Dangoor, E., Eliashar, R., Aksoy, F. G., Dano, I., and Gomori, M. (2000). “Artifactual thickening of the sinus walls on computed tomography: a phantom model and clinical study,” Ann. Otol. Rhinol. Laryngol. 109, 859-862. DOI: 10.1177/000348940010900914

Sudin, C., Tajuddin, A. A., and Bradley, D. A. (1988). “Evaluation of tissue-equivalent media for dosimetric studies,” in: Proceeding of Local Seminar Activities on Radiation Physics, Biophysics and Medical Physics, Kuala Lumpur, Malaysia. pp. 71-80.

Thilagashanthi, T., Gunasekaran, K., and Satyanarayanan, K. S. (2021). “Microstructural pore analysis using SEM and ImageJ on the absorption of treated coconut shell aggregate,” J. Clean. Prod. 324, 129217. DOI: 10.1016/j.jclepro.2021.129217

Thomas, S. J. (1999). “Relative electron density calibration of CT scanners for radiotherapy treatment planning,” Br. J. Radiol. 72, 781-786. DOI: 10.1259/bjr.72.860.10624344

Tousi, E. T., Hashim, R., Bauk, S., Jaafar, M. S., Abuarra, A. M. H., and Ababneh, B. (2014). “Some properties of particleboards produced from Rhizophora spp. as a tissue-equivalent phantom material bonded with Eremurus spp.,” Measurement 54, 14-21. DOI: 10.1016/j.measurement.2014.04.004

Yusof, M. F. M., Hashim, R., Tajuddin, A. A., Bauk, S., and Sulaiman, O. (2017). “Characterization of tannin-added Rhizophora spp. particleboards as phantom materials for photon beams,” Ind. Crops Prod. 95, 467-474. DOI: 10.1016/j.indcrop.2016.10.057

Zuber, S. H., Hashikin, N. A. A., Mohd Yusof, M. F., Aziz, M. Z. A., and Hashim, R. (2021a). “Characterization of soy-lignin bonded Rhizophora spp. particleboard as substitute phantom material for radiation dosimetric studies – Investigation of CT number, mass attenuation coefficient and effective atomic number,” Appl. Radiat. Isot. 170, 109601. DOI: 10.1016/j.apradiso.2021.109601

Zuber, S. H., Yusof, M. F. M., Hashikin, N. A. A., Samson, D. O., Aziz, M. Z. A., and Hashim, R. (2021b). “Rhizophora spp. as potential phantom material in medical physics applications – A review,” Radiat. Phys. Chem. 189, article 109731. DOI: 10.1016/j.radphyschem.2021.109731