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PET-enabled Dual-Energy CT: Image Reconstruction and A Proof-of-Concept Computer Simulation Study

Standard dual-energy computed tomography (CT) uses two different X-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and many other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrade or increasing radiation dose. This paper proposes a novel dual-energy CT imaging method that is enabled by the already-available PET data on PET/CT. Instead of using a second X-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray CT image is then combined with the low-energy X-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which does not compromise the PET activity reconstruction too much but significantly affects the performance of the gamma-ray CT for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available X-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results indicate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.


Introduction
Dual-energy (DE) computed tomography (CT) has gained increasing popularity in recent years thanks to its capability of differentiating tissue materials [1]. Different from traditional CT imaging that commonly uses single X-ray energy (≤140 keV), DECT employs two different X-ray energies, one at a lower level (e.g., 50 keV) and the other at a higher level (e.g., 80 keV), to scan the same object either sequentially by two scans or simultaneously by two X-ray sources. It obtains energy-dependent attenuation information of tissue properties and allows quantitative material decomposition [2].
Because it brings a dimension of information that is distinct from what functional positron emission tomography (PET) offers, DECT can complement PET/CT imaging. DECT Figure 1. The PET-enabled dual-energy CT imaging method exploits the annihilation radiation of PET radiotracer decays as an internal "γ-ray" source to reconstruct a high-energy attenuation image from PET emission data, which is then combined with the low-energy X-ray CT image to form a dual-energy CT image pair. based methods also often require a convergent solution to achieve the optimal performance, which is computationally costly. In comparison, the kernel method [28][29][30][31][32][33][34] encodes image prior information in the forward model of tomographic image reconstruction and requires no explicit regularization. It is easier to implement and can be more efficient and better improve PET image reconstruction than regularization-based methods [28,29]. In this work, we adopt the kernel framework and develop a kernel MLAA algorithm to incorporate the X-ray CT image prior knowledge for noise suppression in the MLAA attenuation image reconstruction.
Part of this work was presented in the 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference [36]. Compared to its conference version, this paper has been substantially extended by including the development and validation of the kernel MLAA algorithm to solve the noise challenge and a more comprehensive computer simulation study to demonstrate the feasibility of the proposed PET-enabled dual-energy CT method.

The Idea
As illustrated in Fig. 1, a standard PET/CT scan normally consists of a PET emission scan at 511 keV and a X-ray CT transmission scan commonly acquired at 80-140 kVp. The Xray CT image has been mainly used for PET attenuation correction with which the PET scan provides a functional image describing the radiotracer distribution in the subject. Our proposed method exploits the potential of a standard PET emission scan for high-energy GCT imaging. Different from X-ray CT which uses an external X-ray source to generate tomographic data, here the PET-enabled GCT exploits the internal "γ-rays" generated by annihilation radiation of PET radiotracer decay in the object. The GCT image obtained from PET is then combined with the low-energy X-ray CT to form dual-energy CT imaging.
There are potentially multiple methods for obtaining a GCT image. In this work, we exploit a PET attenuation-activity joint reconstruction method, as described below.

Joint Reconstruction of GCT Image from PET Data
PET projection measurement y can be well modeled as independent Poisson random variables with the log-likelihood function, where i denotes the index of PET detector pair and m denotes the index of time-of-flight (TOF) bin. N d is the total number of detector pairs and N t is the number of TOF bins. The expectation of the PET projection dataȳ(λ, µ) is related to the activity image λ and attenuation image µ at 511 keV through where G m is the PET detection probability matrix for the mth timing bin and r accounts for the expectation of random and scattered events. n m (µ) is the normalization factor for TOF bin m, of which the ith element is where c i,m denotes the multiplicative factor other than the attenuation correction factor and A is the system matrix for transmission imaging. For standard PET/CT imaging, the attenuation image µ is normally predetermined from a X-ray CT scan and the PET reconstruction problem only estimates the λ image [37]. µ can be approximated from a X-ray CT scan using a bilinear scaling conversion of linear attenuation coefficient from the X-ray energy (e.g., 140 kVp) to 511 keV [38].
The maximum-likelihood attenuation and activity (MLAA) estimation method [9,35] seeks the estimates of both µ and λ simultaneously by maximizing the Poisson log-likelihood, The MLAA formulation was first proposed for non-TOF data [35] but the simultaneous estimation suffers from cross-talk artifacts despite some encouraging results [35,39]. The method was later demonstrated more effective for TOF data [40,41]. A seminal theoretical work later proved that TOF data determine µ up to a constant [8,9]. Since then, the MLAA method has received a wide range of interests (e.g., [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]). It is worth noting that previous attention of MLAA reconstruction was focused on PET attenuation correction. In this paper, we exploit the MLAA reconstruction distinctly. We propose to combine the GCT image µ with the X-ray CT image to obtain a dual-energy CT image pair to enable multi-material decomposition.

Multi-material Decomposition (MMD)
For each image pixel j, the high-energy GCT attenuation value µ j and the low-energy X-ray CT attenuation value x j form a pair of dual-energy measurements u j . The tissue compositions are then described by a set of material bases, for example, air (A), soft tissue (S) or equivalently water, and bone (B): where the coefficients ρ j,k with k = {A, S, B} are the fraction of each basis material in pixel j and subject to k ρ j,k = 1.
The material basis matrix U consists of the linear attenuation coefficients of each basis material measured at the low and high energies. The estimates of ρ j are obtained using the following least-square optimization for each image pixel, 3. Improved GCT Reconstruction Using Kernel MLAA

Use of X-ray CT Image Prior
The GCT by standard MLAA reconstruction is commonly noisy. To suppress the noise, we propose to utilize available X-ray CT in PET/CT as an image prior. As illustrated in figure  2, the higher contrast and potentially much better image quality provided by a X-ray CT image can be beneficial to guide the reconstruction of a γ-ray CT image. In this work, we apply the kernel method which was originally developed for dynamic PET reconstruction (e.g., [28]) and dual-modality imaging such as PET/MR (e.g., [29,30]). Here we extend the kernel method to exploit low-energy X-ray CT image prior for reconstruction of 511 keV GCT image from PET emission data.

Kernel Representation of GCT Image
With the X-ray CT image x, we can extract a "data point" f j for each pixel j from the image, for example, using the image patch centered at j (Fig. 2). A transformed feature space can be defined by a nonlinear mapping function φ, which transforms the low-dimensional space {f j } to a very-high dimensional space {φ(f j )}. In the high-dimensional feature space, the intensity of the GCT in pixel j can be described as a linear function, denotes the coefficient vector. Because w also sits in the feature space, i.e., w = l α l φ(f l ), we then have the following equivalent kernel representation for µ j , where the kernel function κ(·, ·) is defined as the inner product of the two feature vectors φ(f j ) and φ(f l ). The form of the kernel function can be directly defined without knowing the specific form of φ. For example, the radial Gaussian kernel is which corresponds to a φ of infinite dimension. σ is a hyper-parameter. The matrix-vector form of the kernel representation for the GCT image is where K is the kernel matrix built on the X-ray image x with its (j, l)th element equal to κ(f j , f l ). The unknown parameter vector α denotes the corresponding kernel coefficient image. Although with a large matrix size, K can be built to be sparse to make a practical implementation [28].

Kernel MLAA
Inserting the kernel representation in Eq. (10) into the original MLAA formulation leads to a kernelized optimization problem as follows, Onceα is obtained, the final estimate of the GCT image is obtained bŷ To solve the optimization problem, we use the same alternating optimization strategy as used in [9]. Each iteration of the algorithm consists of two separate λ-step and α-step, 3.3.1. λ-estimation step The λ-step is a maximum-likelihood PET reconstruction problem which can be easily solved using the standard expectation-maximum (EM) algorithm, where n denotes the inner iteration number and the superscript T denotes matrix or vector transpose.p is the sensitivity image defined bŷ

3.3.2.
α-estimation step The α-step is a kernel maximum-likelihood transmission reconstruction (MLTR) problem [42] for time-of-flight PET data, where h i,m ( ) is the negative likelihood function, The kernel MLTR problem can be solved using the optimization transfer principle in [42] to construct the following quadratic surrogate, andη n is an intermediate weight sinogram, In the equations, n i = [Aµ n ] i with µ n = Kα n . η i,m ( n i ) is the optimum curvature defining a quadratic surrogate function that majorizes the function h i,m ( ) [42], where [·] + = max(0, ·) applies the non-negativity constraint.ḣ andḧ are the the first and second derivatives of h i,m ( ), respectively [42].
where ∇ denotes the gradient with respect to α. By treating "AK" as a single matrix, maximization of Q(α; α n ) can be solved using the separable quadratic surrogate (SQS) algorithm [43], where g n is the gradient of Q(α; α n ), and ω n is an intermediate weight image, with 1 denoting the all-one vector.
Following the optimization transfer principle [42], the update given by Eq. (25) is guaranteed to monotonically increase the Poisson log likelihood, i.e.,

Computer Simulation Setup
We simulated the GE Discovery 690 PET/CT scanner in 2D. The TOF timing resolution of this PET scanner was about 550 ps. The simulation was conducted using the XCAT phantom. The true PET activity image and 511 keV attenuation image are shown in Fig. 3(a) and (b), respectively. The images were first forward projected to generate noise-free PET sinogram of 11 TOF bins. A 40% uniform background was included to simulate random and scattered events. Poisson noise was then generated using 5 million expected events, unless specified otherwise. The x-ray CT image at a low-energy 80 keV is shown in Fig. 3(c). The data were reconstructed into images of 180 × 180 with a pixel size of 3.9 × 3.9 mm 2 .

Reconstruction Methods to Compare
Three reconstruction algorithms were compared in this study: (1) the standard MLAA algorithm, (2) proposed kernel MLAA, and (3) post-reconstruction kernel smoothing using the same kernel matrix K. The third algorithm is also equivalent to nonlocal means denoising [28]. Using the 3 × 3 image patches extracted from the X-ray CT image x, the kernel matrix was built using 50 nearest neighbors in a way similar to [28]. Each of the standard MLAA and kernel MLAA algorithms was run for 3000 iterations. Within each iteration, one inner iteration was used for the PET activity λ estimation step and five inner iterations were used for the attenuation µ estimation step.  For each reconstruction algorithm, two different initial image estimates were used for the GCT reconstruction. One is the uniform initial with µ init j = 0.1 cm −1 and the other is the 511 keV attenuation map converted from the X-ray CT image using a bilinear scaling.

Evaluation Metrics
Our main interest is in dual-energy CT imaging. Hence we did not specifically evaluate the algorithms for PET activity reconstruction but focused on the evaluation for the CT performance.
The quality of GCT was first assessed using the image mean squared error (MSE) defined by MSE(μ) = 10 log 10 ||μ − µ true || 2 ||µ true || 2 (dB), whereμ is an image estimate of GCT obtained with one of the MLAA reconstruction methods and µ true denotes the ground truth GCT image. For evaluating quantification, we also calculated the ensemble bias and standard deviation (SD) of the mean intensity in regions of interest (ROI) by where c true is the noise-free regional intensity andc = 1

Nr
Nr i=1 c i denotes the mean of N r realizations. N r = 5 in this study.
In addition to the comparison for GCT image quality, different reconstruction algorithms were further compared for dual-energy CT multi-material decomposition as formulated in Section 2. Image MSE, ROI bias and SD were calculated for each of the material basis fraction images.  Quality   Fig. 4 shows the reconstructed GCT attenuation images at 511 keV from the noisy PET emission data using the standard MLAA and proposed kernel MLAA algorithms with 400 iterations. Both the results of using the uniform initial and CT initial are shown. It is not surprising that the CT initial provided better image quality because the initial estimate is closer to the ground truth. For both initials, the kernel MLAA achieved much better results with lower MSE than the standard MLAA reconstruction.  initials made a large difference at early iterations but not at late iterations where the image reconstructions start to converge despite the initial starting point. In all the three reconstruction approaches, the CT initial also allowed an earlier iteration stopping to get each own best MSE than the uniform initial. This is useful as less number of iteration leads to accelerated speed. While post-reconstruction denoising improved the MLAA result, the kernel MLAA achieved a larger improvement on image quality with lower MSE. Because the CT initial demonstrated better performance than the uniform initial, hereafter we mainly present further comparisons based on the CT initial. The effect of count level is shown in Fig. 6. In addition to the 5 million count level, two additional count levels (1 million and 10 million) were also included in the study. The number of iterations was fixed at 400 for each reconstruction. With increased count level, image quality by different algorithms were all increased. The kernel MLAA remains superior over the MLAA (with or without post-reconstruction smoothing) at different count levels. Fig. 7 shows the results of ensemble bias versus SD for GCT ROI quantification in a liver region and a spine bone region. The count level was 5 million events. The iteration number varies from 0 to 3000 with a step of 100 iterations. As iteration number increases, the bias of ROI quantification is reduced while the SD is increased. After a certain number of iterations, the increasing noise may become dominant, which in turn induces higher bias. The post-reconstruction kernel smoothing approach outperformed the standard MLAA approach in a homogeneous region such as the liver but may oversmooth small targets such as the bone structures. The kernel MLAA achieved the best performance for both ROIs. At a fixed bias level, the kernel MLAA has lower SD than the other two approaches.

Comparison Results for Multi-material Decomposition
The results of applying multi-material decomposition (MMD) to the combined X-ray CT and GCT data are given in Figure 8. The MLAA and kernel MLAA reconstructions were run for 400 iterations. The ground truth of the three basis fractional images (air, soft tissue, bone) was generated using the noise-free pair of low-energy x-ray CT image and the 511 keV GCT image. The images by MLAA contain substantial noise but the regions of air and bone were still differentiated from the soft-tissue basis. Compared to MLAA, the kernel MLAA reconstruction led to a dramatic noise reduction in all the three basis images with increased image MSE. The MSE of each basis fractional image is further plotted as a function of iteration number in Fig. 8(d) in which the post-reconstruction kernel smoothing approach was also included for comparison. The kernel MLAA approach demonstrated a significant MSE  improvement over the conventional MLAA approach with or without post-reconstruction smoothing across all iterations. To demonstrate the performance of different reconstruction algorithms for ROI quantification on MMD images, Fig. 10 shows the bias versus SD trade-off plot for ROI quantification on the bone fractional image using the spine ROI as shown in Fig. 7(a). Due to over-smoothing, the post-reconstruction denoising approach had lower SD but higher bias, resulting in an even worse trade-off than the MLAA without denoising. In comparison, the kernel MLAA reconstruction achieved a consistently better trade-off than the other two approaches.

Discussions
In this paper, we demonstrated the feasibility of PET-enabled dual-energy CT imaging using computer simulation. While the standard MLAA reconstruction suffers from high noise, the kernel MLAA reconstruction can dramatically improve the GCT image quality and multimaterial decomposition by utilizing the X-ray CT image prior for MLAA reconstruction. This shows a promising direction and also provides guidance to test the method in future physical phantom experiments and real patient data studies that will be conducted as our next steps.
In the current study, we consider the X-ray CT is perfect, which however is less the case in practice. Depending on the application scenarios, X-ray CT of PET/CT may be of high noise and artifacts. Solutions include improved X-ray CT image reconstruction or deep-learning low-dose CT denoising (e.g., [44] and references therein). One of our future work will investigate the effect of X-ray CT image quality on kernel MLAA and material decomposition and how improved methods may conquer the problems. It is also possible to combine X-ray CT projection data and PET emission data to pursue joint MLAA/CT reconstruction from simultaneous emission-transmission scans, in a way similar to (while still different from) earlier and new effort on a related problem (e.g., [16,45]).
Another challenge down the road is that it is not uncommon that misalignment exists between a PET scan and an X-ray CT scan due to patient movement and physiological motion. This problem may affect both the kernel MLAA reconstruction and the match between GCT and X-ray CT for dual-energy imaging. One solution is to register the X-ray CT image to the GCT image estimated by standard MLAA with post-smoothing. The kernel MLAA and material decomposition are then implemented based on the registered X-ray CT image.
Despite the challenges, the PET-enabled dual-energy CT method has many potentials. (1) It may allow dual-energy CT imaging on PET/CT with a lower radiation exposure due to one less X-ray CT scan. In the context of whole-body 18 F-fluodeoxyglucose (FDG) PET/CT, the effective dose of a low-dose CT scan for attenuation correction and anatomic localization is about 3-10 mSv while the effective dose of 10 mCi FDG is 7 mSv. A second X-ray CT scan for dual-energy imaging may add a significant amount of radiation exposure; (2) It may enable multi-energy spectral imaging in two different ways. The first way is to combine dual-energy X-ray CT with the PET-enabled GCT at 511 keV to create triple-energy CT imaging. The second way is to derive another high-energy GCT images from a PET scan in addition to the 511 keV attenuation image. This is possible because 176 Lu in the LSO/LYSO crystals of PET detectors produces background radiation at 307 keV and 202 keV, which can be used to obtain the corresponding attenuation maps as demonstrated by Rothfuss et. al. [46]; (3) In addition, the proposed PET-enabled dual-energy imaging method also has the potential to correct X-ray CT artifacts. X-ray CT is commonly poly-energetic and suffers from scattering [47] and beam hardening effects especially in the presence of metallic implants [48]. The essentially monoenergetic 511 keV GCT enabled by PET could potentially help reduce the beam hardening, scattering, and metal artifacts of X-ray CT. We will explore these directions in our future work.

Conclusion
We have developed a PET-enabled dual-energy CT imaging method and demonstrated its proof of concept using computer simulation. Distinct from conventional dual-energy CT imaging, the proposed method does not use two X-ray CT energies but combines low-energy X-ray CT and high-energy gamma-ray CT reconstructed from time-of-flight PET emission data. A kernel MLAA algorithm has also been developed to improve image quality and validated using simulated data. The results have shown the feasibility of the method for multimaterial decomposition. As compared to a standard PET/CT scan, the proposed method can add a new dimension of information of material compositions without increasing the imaging time and cost. The method also has the potential to be extended for multi-energy spectral CT imaging.