Introduction
In recent years, metal halide perovskites (MHPs), which are denoted as ABX3 (e.g., A: Cs+, MA+, B: Pb2+, Bi2+, and X: Br-, I-) [1], as illustrated in Fig. 1(a), with high X-ray absorption capabilities, high charge carrier mobilities μ, and long carrier lifetimes τ, have been considered promising alternatives to traditional semiconductors such as Si, α-Se, CdTe, CdZnTe, diamond, HgI2, and Ga2O3 [2-7]. MHPs can be grown in both single-crystal (SC) and polycrystalline (PC) forms, and can be integrated with various readout circuits. Owing to their orientation-dependent transport behavior and low defect concentrations, SC MHPs exhibit excellent potential for X-ray detection. For instance, flat-panel detectors (FPDs) fabricated from hybrid organic-inorganic SC MAPbBr3 [8, 9] and MAPbI3 [10, 11] achieved significantly higher X-ray detection sensitivity than the commercial α-Se detector. Despite their superior X-ray detection performance, the fabrication of SC MHPs with scalable dimensions for large-area X-ray detectors is challenging and expensive. Fortunately, experiments have demonstrated that a conversion layer made of PC MHPs can be more easily scaled up to cover large areas at a much lower cost [12, 13]. The first PC MAPbI3-based FPD prototype was demonstrated in 2015, which enabled the direct detection of soft X-ray photons [14]. In 2017, Kim et.al. reported the possibility of direct hard-X-ray imaging using a thick CH3NH3PbI3 film on a thin-film transistor (TFT) array for the first time [12]. In 2021, Deumel et al. developed a new procedure for manufacturing a direct X-ray detector by soft-sintering CH3NH3PbI3 on a hydrogenated amorphous silicon TFT array, employing a grid structure to adhere the thick perovskite film mechanically [15]. In 2022, Xia et al. prepared a TFT array via soft pressing and in situ polymerization of a multifunctional binder (TMTA) for a CH3NH3PbI3 film [16]. In 2024, Liu et al. developed a novel complementary metal oxide semiconductor (CMOS) array-based dynamic perovskite X-ray detector using a CsPbBr3 film [17]. Although many studies have demonstrated the excellent potential of FPDs composed of MHP materials for X-ray imaging, as illustrated in Fig. 1(c), few studies have extended this research to spectral imaging, which is a technique that captures X-ray attenuation information at multiple energy levels. In this manner, X-ray spectral imaging allows the distinction between different materials. Consequently, X-ray spectral imaging can enhance the image contrast and specificity, making it valuable for medical, industrial, and security imaging applications [18, 19]. In 2022, Pang et al. proposed a perovskite X-ray detector with a vertical structure that offers the opportunity for improved multienergy X-ray imaging [20]. However, this innovative vertical design presents challenges for fabricating detectors with large surface areas. Therefore, alternative solutions for utilizing MHP materials in spectral imaging require further investigation.
In biomedical X-ray imaging applications, the dual-layer FPD (DL-FPD) has emerged as a promising tool for quantitative dual-energy X-ray imaging [21-23]. The DL-FPD comprises two stacked FPD layers with varying thicknesses. In this configuration, lower-energy X-ray photons are detected by the top layer, whereas higher-energy photons are detected by the bottom layer. Studies have also explored the use of DL-FPDs in dual-energy computed tomography imaging [24-27]. However, current DL-FPDs primarily employ scintillator materials, which pose a significant challenge: the imaging performance of the bottom detector, including its spatial resolution, sensitivity, and detective quantum efficiency (DQE), is inferior to that of the top detector. Consequently, the results of dual-energy X-ray imaging are limited. Alternatively, the DL-FPD can be constructed using MHP materials, as shown in Fig. 1(d). As opposed to scintillator-based indirect X-ray detectors, MHP-based X-ray detectors can directly convert X-ray photons into electric charges, which are then driven by the applied electric field and subsequently collected by the electrodes. DL-FPDs made of MHP materials offer improved imaging performance compared with scintillator-based detectors. For instance, because the applied electric field can be well confined, signal spreading in direct-conversion X-ray FPD is less severe than in indirect-conversion X-ray FPD, where X-ray-converted visible light photons may spread across several neighboring detector elements. Consequently, the spatial resolution of the bottom layer of MHP-based DL-FPDs can be enhanced. Despite the potential for the improved imaging performance of MHP-based DL-FPDs, comprehensive investigations to evaluate the imaging performance of direct X-ray DL-FPDs have not yet been conducted.
This study mainly focuses on estimating and evaluating the responses of a novel direct-conversion X-ray DL-FPD made of MHP material. Specifically, the imaging performance, including the X-ray sensitivity, modulation transfer function (MTF), and DQE, of the MHP-based direct X-ray DL-FPD is explored. To achieve this, numerical calculations are conducted based on a linear cascaded model (see the workflow illustrated in Fig. 1(e)). Moreover, the dependency of the overall imaging performance on various factors, such as the thickness of the MHP material layer, X-ray beam spectra, and external electric fields, is investigated. Additionally, the electronic readout noise effect is analyzed by considering three different readout arrays: hydrogenated amorphous silicon TFT (α-Si: TFT), metal oxide TFTs based on indium gallium zinc oxide (IGZO-TFT), and CMOS, as shown in Fig. 1(b). To date, -Si: TFT arrays have been the leading choice for perovskite X-ray FPDs owing to their simple structure, low cost, and suitability for large-area applications. The TFT [28] consists of a substrate, a gate electrode separated by a dielectric layer, an amorphous silicon layer, and source and drain electrodes, all of which are arranged to control the flow of charge carriers in the semiconductor channel. The CMOS technology [29] combines both p-type and n-type metal-oxide-semiconductor field-effect transistors to achieve low power consumption and high noise immunity, offering advantages such as a lower readout noise and higher readout speeds, making them promising alternatives. IGZO-TFTs [30] have a structure similar to that of α-Si:TFT, with the primary difference being the use of an IGZO layer instead of amorphous silicon, which results in less readout noise and increased readout speed; these are intermediate between those of α-Si: TFT and CMOS, positioning IGZO-TFT as a competitive option for fabricating X-ray detectors.
The remainder of this paper is organized as follows. Section 2 reviews the linear cascade model and deduces the calculations for the sensitivity, MTF, and DQE. Section 3 introduces the main parameters considered in this study and presents details of the numerical calculations. Section 4 presents the results of the DL-FPD responses obtained using different parameters. Section 5 provides a discussion and brief conclusion.
Detector response model
Linear cascade model
The seven dominant stages of the linear cascade model [31-34], which describe the propagation of signals and noise from X-ray photons to electric signals in a direct FPD, are summarized as follows: [stage 0:]
stage 0. X-ray input. (X-ray quanta
stage 1. Absorption of X-ray photons. (Absorption efficiency
stage 2. Electron-hole cloud effect. (MTFm(E;f) owing to charge cloud. See Eq. 14.)
stage 3. Electron-hole conversion. (Charge conversion multiplication
stage 4. Electron-hole collection. (Charge collection efficiency
stage 5. Electron-hole blurring by charged traps. (MTFtr(E;f) owing to charge trapping. See Eq. 21.)
stage 6. Electron-hole blurring by pixel aperture (MTFa(f) owing to aperture collection. See Eq. 25).
stage 7. Signal output. (Additive noise power spectrum owing to dark current shot noise σshot and electronic readout noise σreadout. See Eq. 28.)
The variables E, x and f denote the X-ray photon energy, vertical position in the detector, and spatial frequency, respectively. Essentially, the above seven stages can be divided into three steps: the gain step, including stages 1, 2, and 4; the stochastic blurring step, including stages 3 and 5; and the deterministic blurring step, including stage 6. Further details of the cascaded model can be found in Appendix 5.
For a given gain stage n, the propagations of the signal quantum Φn and noise power spectrum NPSn are expressed as follows [31, 33]:
Response evaluation
To evaluate the responses of the perovskite DL-FPD, the sensitivity, MTF, and DQE are calculated separately for the top and bottom layers. Specifically, the sensitivity Si represents the ability to convert X-ray photons into electric charges for the i-th layer, where i = t or b denotes the top or bottom layer in a DL-FPD, respectively. Mathematically:
Methods
Detector configuration
In this study, a metal-semiconductor-metal (MSM) structure with ohmic contacts is assumed for the perovskite detector, ignoring the internal photoconductive gain. Key parameters that may affect the DL-FPD imaging performance, such as the input beam spectra, layer thickness, electric field, dark current, readout noise, and material structure, are considered during the numerical calculations.
Material structure
In this study, calculations are performed for both SC and PC MAPbI3. The mobility-lifetime product for electrons (μτe) and holes (μτh) is assumed to be equal, and it is set to 1×10-3 cm2· V-1 for SC MAPbI3 and 1×10-4 cm2·V-1 for PC MAPbI3 [10-12, 15].
Beam spectra
In total, three X-ray beam spectra with different radiation qualities (RQA) are simulated according to the International Electrotechnical Commission (IEC) 61267:1994 guidelines: (a) RQA5, (b) RQA7, and (c) RAQ9 (see Table 1 and Fig. 2(a)).
| Radiation qualities | Tube voltage (kV) | Half-value layer (Al) | Added filtration (mm Al) | X-ray quanta (mm-2·μGy-1) |
|---|---|---|---|---|
| RQA5 | 70 | 7.1 | 21 | 30174 |
| RQA7 | 90 | 9.1 | 30 | 32362 |
| RQA9 | 120 | 11.5 | 40 | 31077 |
Layer thickness
The total thickness L of the top and bottom MHP layers is fixed at 1.0 mm to ensure sufficient (≥80%) X-ray attenuation. As shown in Fig. 2(b), the attenuation efficiencies of the 1.0 mm MAPbI3 layer are 82%, 88%, and 96% for the RQA5, RQA7, and RQA9 beams, respectively. In addition, the thickness of the top layer, denoted by Lt, increases from 100 μm to 500 μm at intervals of 100 μm. This corresponds to a top-layer thickness occupation, denoted by Lt/L, of 10%, 20%, 30%, 40%, and 50%. The sensitivities, MTF(f), and DQE(f) of the top and bottom layers are calculated for different Lt/L values.
Electric field
The electric field inside each MHP layer, as shown in Fig. 1(c), is calculated as the ratio of the applied bias voltage Ui to the layer thickness Li, that is,
Dark current
As discussed in Sect. 5.0.8, the dark current has an impact on the DQE owing to the introduction of additive noise. Assuming ohmic-type contacts, the dark current density can be modeled as
Readout noise
Similarly, the electronic readout noise σreadout also affects the DQE by introducing additive noise, as discussed in Sect. 5.0.8. Three types of pixelated readout circuits are compared in this study: CMOS, IGZO-TFT, and traditional α-Si: TFT, with typical readout noise levels of 200 e-, 700 e-, and 2000 e-, respectively [30].
Numerical study
Specifically, the RQA5, RQA7, and RQA9 beam spectra are generated using SpekCal [38]. The pixel size is set to 100 μm.
The mass density ρmass of MAPbI3 material is 4.16 g/cm3. The X-ray absorption coefficients are obtained from the National Institute of Standards and Technology (NIST) database. The electron-hole pair creation energy [12, 15] W± is 4.7 eV. The signal integration period Δt is set to 10.0 ms. The incident X-ray exposure is examined at 1.0 and 0.1 μGy to investigate the effects of the electric field and readout noise, whereas it is fixed at 1 μGy to investigate the effects of the other parameters. More detailed settings are presented in Table 2. All numerical calculations are performed using Python (version 3.10) on a desktop (Dell XPS, Intel i7-13700, 16 GB DDR5 RAM).
| Parameter | Specifications |
|---|---|
| X-ray spectra | RQA5, RQA7, RQA9 |
| Dose (μGy) | 0.1, 1 |
| Pixel size (μm) | 100 |
| Δt (ms) | 10 |
| σreadout (e-) [30] | 200 (CMOS), 700 (IGZO-TFT), 2000 (α-Si: TFT) |
| L (μm) | Lt+Lb=1000, Lt/L=10%, 20%, 30%, 40%, 50% |
| F (V/μm) | 0.01, 0.05, 0.1, 0.5, 1 |
| Material [10-12, 15] | MAPbI3 |
| ρmass (g/cm3) | 4.16 |
| W± (eV) | 4.7 |
| μτe,h (cm2/V) | 1×10-3 (SC), 1×10-4 (PC) |
Results
Dependence on material thickness
The calculated sensitivity responses are presented in Fig. 3 for five different Lt/L ratios: 10%, 20%, 30%, 40%, and 50%. The solid and dashed lines represent the sensitivity responses of the top and bottom layers, respectively. The input beam spectrum is set to RQA7. The sensitivity increases as the electric field increases and reaches its maximum when the external electric field exceeds a certain threshold. In general, SC MAPbI3 achieves its maximum sensitivity at a lower electric field than PC MAPbI3. This indicates that SC MAPbI3 can achieve sufficient charge collection efficiency at a lower electric field than PC MAPbI3. Moreover, the sensitivity responses of the top and bottom detector layers exhibit a competitive relationship as Lt/L varies. Specifically, the sensitivity of the top detector layer increases as Lt/L increases, whereas that of the bottom detector layer decreases. This occurs because more X-ray photons are absorbed by the top layer, thereby reducing the number of X-ray photons collected by the bottom layer.
Finally, the ratio Lt/L is crucial for determining the relative sensitivities of the top and bottom layers. As can be observed, the sensitivity of the top layer exceeds that of the bottom layer when Lt/L≥30%. Conversely, the sensitivity becomes lower than that of the bottom layer when Lt/L≤20%. Therefore, a ratio Lt/L between 20% and 30% is preferred to achieve similar sensitivities for the top and bottom layers.
The estimated MTF(f) curves obtained from the five different Lt/L ratios are plotted in Fig. 4. The input spectrum is set to RQA7 and the external electric field is set to 0.1 V/μm for both layers. The ideal MTF, which is determined by the pixel aperture collection blurring, is indicated by cyan lines.
For the SC MAPbI3 material, the generated MTF(f) curves are almost identical across different Lt/L values and closely resemble the ideal MTF(f), as shown in Fig. 4(a). This indicates that the SC MAPbI3 material has a minimal impact on the spatial resolution of the detector. However, under the same conditions, the MTF(f) of the detector made of PC MAPbI3 varies dramatically with different Lt/L ratios. Most of these values are lower than the ideal MTF(f), except for the very thin top layer when Lt/L=10% (see Fig. 4(b)). Furthermore, the MTF(f) of the top detector layer exhibits a competitive relationship with that of the bottom layer as Lt/L varies. For very thin top detector layers, such as Lt/L=10%, the top MTF(f) is notably higher than the bottom MTF(f), as indicated by the solid and dashed blue lines in Fig. 4(b). As Lt/L increases, the MTF(f) of the top detector layer decreases while the MTF(f) of the bottom detector layer increases. This is because of the more severe charge trapping with increasing material thickness. When the material thicknesses become equal, that is, Lt/L=50%, as expected, the MTF(f) of the top detector layer approaches that of the bottom layer.
The estimated DQE(f) curves obtained from the different Lt/L values are plotted in Fig. 5. The input spectrum is set to RQA7, the electric field is set to 0.1 V/μm for both detector layers, the entrance dose is set to 1 μGy, and CMOS readout noise is assumed. As observed for both SC and PC MAPbI3, the DQE(f) of the top detector layer increases with an increasing Lt/L. In contrast, the DQE(f) of the bottom detector layer decreases with a larger Lt/L. This is because more X-ray photons are absorbed by the top layer as it becomes thicker, whereas fewer X-ray photons are absorbed by the bottom layer. In addition, the DQE(f) values of the detector made from PC MAPbI3 decrease more rapidly in the high spatial frequency range compared with those made from SC MAPbI3, especially for the bottom layer (see Fig. 5(b). This is attributed to the more severe signal blurring effect caused by charge trapping in PC materials. Similar to the sensitivity responses, the DQE(f) of the top detector layer is lower than that of the bottom layer when Lt/L≤20%, but exceeds that of the bottom layer when Lt/L≥30%. Thus, it can be inferred that an Lt/L ratio between 20% and 30% can help to achieve similar DQE(f) values for the top and bottom layers.
Overall, the sensitivity, MTF(f), and DQE(f) of the top layer exhibit a competitive relationship with those of the bottom layer for various values of Lt/L. In the following studies, we concentrate on scenarios in which Lt/L = 30%.
Dependence on beam spectrum
The estimated sensitivities with respect to the three different input beam spectra, that is, RQA5, RQA7, and RQA9, are shown in Fig. 6. For the top detector layer, the sensitivity responses for RQA5 and RQA7 are identical but higher than those of RQA9 (see the solid lines in Fig. 6). This is jointly determined by the X-ray absorption efficiency and charge-conversion multiplication. However, for the bottom layer, the sensitivity response is the highest for RQA9 and lowest for RQA5, as indicated by the dashed lines in Fig. 6. This is because the bottom layer is sufficiently thick to generate similar absorption efficiencies for X-ray photons of different energies. As a result, higher sensitivity is achieved for the higher-energy beam spectrum. Interestingly, the sensitivities of the top and bottom detector layers are similar for RQA9. This indicates that an appropriately low Lt/L should be selected for the low-energy spectrum to generate similar sensitivity responses in the top and bottom detector layers.
The obtained MTF(f) curves for different input spectra are plotted in Fig. 7. The electric field is fixed at 0.1 V/μm for both layers.
The MTF(f) curves are almost identical and closely resemble the ideal MTF(f) for the SC MAPbI3 material (see Fig. 7(a)). However, the MTF(f) curves are consistently lower than the ideal MTF(f) for the PC MAPbI3, as shown in Fig. 7(b). This is owing to the charge-trapping-induced signal blurring effect inside the PC MAPbI3 material. Specifically, the high-energy spectrum tends to generate a slightly better MTF(f) than the low-energy spectrum for the bottom layer.
The DQE(f) responses are plotted in Fig. 8. The entrance dose is set to 1 μGy, and CMOS readout noise is assumed. For the top layer, the DQE(f) is the highest for RQA5 and lowest for RQA9 (see the solid lines in Fig. 8. Conversely, the DQE(f) is the highest for RQA9 and lowest for RQA5 in the bottom layer, as indicated by the dashed lines in Fig. 8. Moreover, the high-energy spectrum RQA9 exhibits a narrower DQE(f) gap between the top and bottom layers than the RQA5 and RQA7 spectra. Consequently, a lower Lt/L ratio is preferable to achieve similar DQE(f) responses between the top and bottom layers.
Dependence on electric field
The MTF(f) responses with respect to five different electric field strengths, namely 0.01, 0.05, 0.1, 0.5, and 1.0 V/μm, are plotted in Fig. 9. The input spectrum is set to RQA7 and Lt/L is set to 30%. For the SC MAPbI3, the MTF(f) exhibits less dependence on the electric field, except for the lower bottom detector layer with an ultra-small electric field of 0.01 V/μm (see Fig. 9(a)). Conversely, for the detectors made of PC MAPbI3, the MTF(f) of the bottom detector layer depends heavily on the electric field. Increasing the electric field can significantly enhance the MTF(f) response, as shown in Fig. 9(b). At high electric fields, MTF(f) of the bottom detector layer can become comparable to or even outperform that of the top detector layer. For example, the bottom layer with 0.5 V/μm exhibits a similar MTF(f) to that of the top layer with 0.05 V/μm. However, increasing the electric field would lead to a higher dark current density, potentially compromising the detection limit and DQE(f) response. Hence, the electric field must be optimized to generate a comparable MTF(f) in the top and bottom layers.
The DQE(f) responses with respect to the different electric fields are shown in Fig. 10 for 1 μGy and 0.1 μGy. An RQA7 input spectrum and CMOS readout noise are assumed. For the SC MAPbI3, the electric field has a negligible impact on DQE(f), except for the ultra-low electric field of 0.01 V/μm, as shown in the plots in Fig. 10(a) and (c). Conversely, reducing the electric field dramatically degrades the DQE(f) of the detector made from PC MAPbI3 (see the plots in Fig. 10(b) and (d)). This is owing to the insufficient charge collection efficiency of PC MAPbI3. Consequently, DL-FPDs made from PC MAPbI3 can achieve a similar DQE(f) to that of SC MAPbI3 if a higher electric field is applied. Increasing the electric field does not always enhance the DQE(f) because the charge collection efficiency saturates once it exceeds a certain field strength. For example, the DQE(f) curve obtained using 0.5 V/μm is similar to that obtained using 1 V/μm, as shown in Fig. 10. Under these conditions, continuously increasing the electric field will lead to an increased dark current, but does not improve the DQE(f).
Dependence on readout noise
The DQE(f) responses with respect to three different electronic readout noise levels are plotted in Fig. 11 for the CMOS, IGZO-TFT, and α-Si: TFT circuits. The electric field is set to 0.5 V/μm to ensure sufficient charge collection efficiency for both layers. In addition, only the PC MAPbI3 material is investigated. The DQE(f) responses of IGZO-TFT are comparable to those of CMOS and higher than those of α-Si: TFT for a dose level of 1 μGy (see Fig. 11(a)). For a dose level of 0.1 μGy, the DQE(f) responses of CMOS are slightly higher than those of IGZO-TFT, but significantly higher than those of α-Si: TFT (see Fig. 11(b)). Therefore, the back-plate readout noise has a more pronounced impact on low-dose imaging tasks. That is, a back-plate with lower readout pixel noise is suggested in low-dose imaging scenarios.
Discussion and Conclusion
In this study, the responses of a direct-conversion perovskite DL-FPD were investigated numerically based on a linear cascade signal model under various configurations. Specifically, the sensitivity, MTF(f), and DQE(f) were evaluated and compared across settings, including the beam spectrum, material structure, material thickness, electric field, and readout noise. Although some material parameters such as the attenuation coefficient and mobility-lifetime product (μτ) were tailored to the MAPbI3 material in this study, the methods and results obtained can be easily generalized to analyze DL-FPDs made from other MHP materials such as CsPbBr3.
The material thickness has a strong impact on the sensitivity, MTF(f), and DQE(f) responses of the top and bottom detector layers. In particular, the sensitivity and DQE(f) of the top detector layer improve as the Lt/L ratio increases, whereas those of the bottom layer decrease. In contrast, the MTF(f) of the top layer is degraded with an increase in Lt/L, whereas that of the bottom layer is enhanced. Consequently, the thickness should be optimized to ensure good low-energy imaging performance, especially for the top layer. In addition, the X-ray beam spectra may significantly affect the sensitivity and DQE(f) responses, whereas they have a minimal impact on the MTF(f). In high-energy imaging scenarios, the sensitivity and DQE(f) of the top layer may decrease, whereas those of the bottom layer may increase. Increasing the electric field can improve the sensitivity, MTF(f), and DQE(f), thereby potentially bridging the performance gap between PC and SC materials. Nonetheless, the sensitivity may become saturated when it exceeds a certain threshold. Moreover, increasing the electric field may consistently increase the dark current and potentially degrade the DQE response. Therefore, an appropriate electric field is required to achieve satisfactory imaging performance. In addition, a pixel array with lower readout noise is necessary to achieve high DQE performance for low-dose imaging applications.
This study has several limitations. First, a fairly ideal detector setting was considered without taking into account the non-uniform material responses. Clearly, these non-idealities can potentially impact the detector imaging performance. Second, we assumed that the detector functions as a photoresistor with no photoconductive gain. However, perovskite-based detectors may be photodiodes with a p-i-n structure, which could have a much higher sensitivity owing to their internal photoconductive gain [47, 48]. In addition, traps that cause signal blurring may enhance the sensitivity and SNR [49]. As a result, balancing signal blurring with the SNR is important. Third, the μτ values were assumed to be equal for electrons and holes in MAPbI3 [50]. However, μτh may be slightly greater than μτe. Consequently, the performance of the MAPbI3 detector, such as the sensitivity, MTF, and DQE, could be slightly affected when the electrodes are interchanged. Fourth, we assumed a uniform electric field in the region directly opposite to the electrodes, with a zero electric field at the pixel boundaries. In reality, a weak electric field exists at the boundaries, which may lead to issues such as signal loss or charge sharing, thus degrading the MTF and DQE of the detector. However, these issues can be mitigated via advanced designs such as the implementation of guard rings and nodal separation on CMOS chips [51]. Fifth, only one of the most popular MHP materials, MAPbI3, was investigated in this study. For detector development, it is necessary to explore other MHP materials in detail. Finally, the imaging performance of the MHP DL-FPD was only investigated for a total material thickness of 1.0 mm. For MHP detectors made of a thicker material, it is necessary to repeat the entire analysis. Finally, no experiments were performed to verify the numerical predictions in this study, owing to the lack of a DL-FPD prototype. In future studies, detailed investigations should be conducted to validate the findings presented in this work.
In conclusion, the direct-conversion DL-FPD made of MHP material has the potential to achieve superior sensitivity, consistent spatial resolution, and high detection efficiency compared with the traditional indirect-conversion DL-FPD made of scintillator materials. In the future, performing high-quality dual-energy imaging using novel direct-conversion perovskite DL-FPDs will be very useful.
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