Evaluation of a hybrid direct-indirect active matrix flat-panel imager using Monte Carlo simulation

Abstract

Purpose: Monte Carlo simulations were used to evaluate the imaging properties of a composite direct-indirect active matrix flat-panel imager (AMFPI) with potentially more favorable tradeoffs between x-ray quantum efficiency and spatial resolution than direct or indirect AMFPIs alone. This configuration, referred to as a hybrid AMFPI, comprises a scintillator that is optically coupled to an a-Se direct AMFPI through a transparent electrode and hole blocking layer, such that a-Se acts as both a direct x-ray converter and an optical sensor. Approach: GEANT4 was used to simulate x-ray energy deposition, optical transport, and charge signal generation processes in various hybrid AMPFI configurations under RQA5 and RQA9 x-ray beam conditions. The Fujita-Lubberts-Swank method was used to quantify the impact of irradiation geometry, x-ray converter thicknesses, conversion gain of each layer, and x-ray cross talk between layers on detective quantum efficiency (DQE). Results: Each hybrid configuration had a greater DQE than its direct AMFPI layer alone. The DQE improvement was largest at low spatial frequencies in both front- and back-irradiation (BI) geometries due to increased x-ray quantum efficiency provided by the scintillator. DQE improvements persisted at higher frequencies in BI geometry due to preferential x-ray absorption in a-Se. Matching the x-ray-to-charge conversion gains of a hybrid AMFPI's direct and indirect detection layers affects its Swank factor and, thus, DQE(0). X-ray cross talk has a negligible impact on the $\mathrm{DQE}\left(f\right)$ of hybrid AMFPIs with sufficiently high optical quantum efficiency. Conclusion: An optimized hybrid AMFPI can achieve greater DQE performance than current direct or indirect AMFPIs.

Keywords: active matrix flat-panel imager; amorphous selenium; detective quantum efficiency; digital radiography; flat-panel detector; scintillator.

Fig. 1

(a) Schematic of a direct AMFPI. X-rays are converted directly into charge via the photoconductor, which is read out by the TFT array. (b) Schematic of an indirect AMFPI. X-rays are converted into optical photons, which are then converted into charge and read out by a photodiode-TFT array. BI of indirect AMFPIs results in greater light escape efficiency and less blur and image noise than FI.

Fig. 2

Schematic of a hybrid AMFPI. Both a-Se and the scintillator function as x-ray converters. X-rays absorbed in a-Se are converted directly into charge. X-rays absorbed in the scintillator are converted into optical photons, which are detected at the scintillator/a-Se interface and converted into charge. Charge is then read out by the TFT array.

Fig. 3

(a) Schematic of our hybrid AMFPI model, which consists of GOS, a-Se, and TFT substrate glass. Four types of x-ray interactions are possible. Type 1 interactions only deposit energy in a-Se and only result in a direct signal component. Type 2 interactions only deposit energy in GOS and only result in an indirect signal component. Type 3 and 4 interactions result in x-ray cross talk from a-Se into GOS and GOS into a-Se, respectively, which occur due to either x-ray scatter or fluorescence. (b) Relative intensity plots of RQA5 and RQA9 x-ray spectra, which have average energies of 52 and 76 keV, respectively.

Fig. 4

A schematic depicting the methods used to calculate various detector performance metrics. Row 1: By integrating each single x-ray response, $S(\overrightarrow{r})$, in 2D, the total signal magnitude due to each x-ray, $S$, is derived. Each measurement of $S$ is then binned in a histogram to determine the PHS, which is used to calculate the Swank factor $A_S$ and DQE(0). Row 2: The $S(\overrightarrow{r})$ ensemble is spaced uniformly along an infinitely thin slanted slit, from which $\mathrm{MTF}(f)$ is computed via Fujita’s method. Row 3: Each $S(\overrightarrow{r})$ is integrated along the $y$-axis to derive $S(x)$. Fourier analysis is performed to compute $q\mathrm{NNPS}(f)$. $\mathrm{DQE}(f)$ is then found from $\mathrm{MTF}(f)$ and $q\mathrm{NNPS}(f)$.

Fig. 5

$\mathrm{DQE}(f)$ of a hybrid AMFPI in FI and BI geometries for both (a) RQA5 and (b) RQA9 beam qualities. Both a-Se and GOS thicknesses were $300\mu \mathrm{m}$, and optimal gain matching was assumed using $\beta =0.44$ and $W_{\mathrm{Se}}=50\mathrm{eV}$. BI results in a significant increase in $\mathrm{DQE}(f)$ at higher frequencies compared to FI.

Fig. 6

$\mathrm{DQE}(f)$ of hybrid AMFPIs with a GOS thickness of $300\mu \mathrm{m}$ and various a-Se thicknesses at (a) RQA5 and (b) RQA9. Optimal gain matching was assumed using $\beta =0.44$ and $W_{\mathrm{Se}}=50\mathrm{eV}$. Greater a-Se thickness increases $\mathrm{DQE}(f)$ at all frequencies, particularly when using lower energy x-ray spectra such as RQA5.

Fig. 7

$\mathrm{DQE}(f)$ of hybrid AMFPIs with an a-Se thickness of $300\mu \mathrm{m}$ and various GOS thicknesses at (a) RQA5 and (b) RQA9. Optimal gain matching was assumed using $\beta =0.44$ and $W_{\mathrm{Se}}=50\mathrm{eV}$. Greater GOS thickness increases $\mathrm{DQE}(f)$ at lower frequencies, particularly when using higher energy x-ray spectra such as RQA9.

Fig. 8

${\mathrm{PHS}}_{\mathrm{GOS}}$ (blue) and ${\mathrm{PHS}}_{\mathrm{a}\text{-}\mathrm{Se}}$ (magenta) using different values of $\beta$ and $W_{\mathrm{Se}}$. Greater overlap increases DQE(0). Dashed lines indicate averages. (a) $\beta =1$, $W_{\mathrm{Se}}=50\mathrm{eV}$; (b) $\beta =0.44$, $W_{\mathrm{Se}}=50\mathrm{eV}$; and (c) $\beta =1$, $W_{\mathrm{Se}}=22\mathrm{eV}$.

Fig. 9

(a) Hybrid AMFPI DQE(0) (dark blue) as a function of $\beta$, with $W_{\mathrm{Se}}=50\mathrm{eV}$. (b) Hybrid AMFPI DQE(0) (magenta) as a function of $W_{\mathrm{Se}}$ with $\beta =1$. DQE(0) of a-Se alone with $W_{\mathrm{Se}}=50\mathrm{eV}$ is shown in teal in both plots.

Fig. 10

(a) A contour plot of DQE(0) as a function of $\beta$ and $W_{\mathrm{Se}}$. Warmer colors indicate higher values. DQE(0) reaches a maximum of 0.678 (dark red) whenever optimal gain matching is achieved. Black curves trace paths through $\beta$ and $W_{\mathrm{Se}}$ as a function of $E_{\mathrm{Se}}$ for various values of $\lambda$. $5\mathrm{V}{\mu \mathrm{m}}^{-1}$ intervals are marked by asterisks. (b) DQE(0) versus $E_{\mathrm{Se}}$ for various values of $\lambda$. When $E_{\mathrm{Se}}$ is larger than $\sim 5\mathrm{V}{\mu \mathrm{m}}^{-1}$, DQE(0) is primarily a function of $\lambda$.

Fig. 11

(a) The x-ray induced luminescence spectrum of GOS and the spectral sensitivity of a-Se both with and without Te doping. (b) $\mathrm{DQE}(f)$ of a-Se alone (blue) and the hybrid AMFPI using GOS both with (red) and without (teal) Te doped a-Se.

Fig. 12

(a) PHS consisting of only the indirect component of signal. Counts due to GOS x-ray interactions are shown in blue, while those due to a-Se cross talk are shown in green. Cross talk results in an additional peak at $\sim 280\mathrm{ehp}$ due to a-Se K-fluorescence, which is negligible in magnitude. (b) PHS consisting of only the direct component of signal. Cross talk results in two additional peaks contained within that of a-Se due to gadolinium K-fluorescence. Counts due to a-Se x-ray interactions are shown in blue, while those due to GOS cross talk are shown in green. (c) Hybrid AMFPI $\mathrm{DQE}(f)$ of both direct and indirect signal components is shown with and without cross talk. Only cross talk from GOS into Se was significant, resulting in an increase at low $f$ with a degradation at all other $f$.