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Summary Medical X-ray computed tomography requires imaging at a low dose rate and in a large area. Halide perovskites have shown high potential for X-ray detection, but a pressing challenge is the current lack of low-cost methods for large-scale fabrication of high-quality thick perovskite films. Here we demonstrate and elucidate an aerosol-liquid-solid process to enable continuous growth of uniform halide perovskite films at low temperature over a large area of 100 cm2, which are vertically monolithic, and thus beneficial to carrier transport. Direct-conversion X-ray detectors based on the representative CsPbI2Br films in conjunction with a deliberated, interface-engineered C-electrode have realized an unprecedented sensitivity (≥1.48 × 105 μC Gyair−1 cm−2) and a low limit of detection (280 nGyair s−1). We have further demonstrated the high-resolution radiographic imaging capability of these films. These results lay the groundwork for large-scale application of halide perovskites in radiation detectors.

Photon counting computed tomography (PCCT) has the ability to identify individual photons, resulting in quantitative material identification. Meanwhile, several technical challenges still exist in current PCCT imaging systems, including increased noise and suboptimal bin selection. These nonideal effects can substantially degrade the reconstruction performance and material estimation accuracy. To address these issues, in this work, we present a novel system for high-performance spectral PCCT imaging, which is a combination of multiple dynamic modulations, interpolation-based measurements processing strategy and advanced reconstruction method. For simplicity, this new PCCT imaging system is referred to as "MDM-PCCT". Specifically, the multiple dynamic modulations consist of dynamic kVp modulation, dynamic spectrum modulation and dynamic energy threshold modulation. In the dynamic kVp modulation, three kVp values, i.e., 80, 110 and 140, are included, and the tube voltage waveform follows a sinusoidal curve which is more practical than the rectangular curve in the fast kV switching mode. In the dynamic spectrum modulation, the X-ray spectra are processed by selective spatial-spectral filters to balance the X-ray fluxes and increase the spectral separation. In the dynamic energy threshold modulation, the energy threshold is adaptively changed to determine the optimal bin selection. Furthermore, we propose an energy threshold determination method and interpolation-based measurements processing strategy to address the issue of non-uniform and sparse-view PCCT measurements, respectively. In addition, by considering the intrinsic characteristics of the MDM-PCCT images, we utilize an enhanced total variation regularized model for images reconstruction. Finally, numerical and preclinical studies demonstrate that the presented MDM-PCCT imaging system is capable of yielding uniform and high-fidelity PCCT measurements with noise consistency, and the presented reconstruction method further improves the image quality and material decomposition accuracy.

Positron emission tomography (PET) imaging is a noninvasive technique that makes it possible to probe biological metabolic processes in vivo . However, PET image reconstruction is challenging due to the ill-posedness of the inverse problem. Many image reconstruction methods have been proposed over the past few years to improve diagnostic performance. However, most of these methods can compromise the reconstruction of important high-frequency structural details after aggressive denoising. To address this problem, in this work, we propose a novel deep learning method that reconstructs PET images using a cascading back-projection neural network (bpNet). This network consists of a domain translation operation, which acts as prior knowledge, cascaded with a modified encoder-decoder network. The image reconstruction pipeline ranges from the sinogram to the back-projection image and then to the PET image. Quantitative results from simulation data and Derenzo phantom experiments with the small animal PET prototype system developed in our laboratory clearly demonstrate that our proposed method provides favorable reconstructed image quality, especially for low-count PET image reconstruction.

Perovskite materials in different dimensions show great potential in direct X‐ray detection, but each with limitations stemming from its own intrinsic properties. Particularly, the sensitivity of two‐dimensional (2D) perovskites is limited by poor carrier transport while ion migration in three‐dimensional (3D) perovskites causes the baseline drifting problem. To circumvent these limitations, herein a double‐layer perovskite film is developed with properly aligned energy level, where 2D (PEA)2MA3Pb4I13 (PEA=2‐phenylethylammonium, MA=methylammonium) is cascaded with vertically crystallized 3D MAPbI3. In this new design paradigm, the 3D layer ensures fast carrier transport while the 2D layer mitigates ion migration, thus offering a high sensitivity and a greatly stabilized baseline. Besides, the 2D layer increases the film resistivity and enlarges the energy barrier for hole injection without compromising carrier extraction. Consequently, the double‐layer perovskite detector delivers a high sensitivity (1.95 × 104 μC Gyair −1 cm−2) and a low detection limit (480 nGyair s−1). Also demonstrated is the X‐ray imaging capacity using a circuit board as the object. This work opens up a new avenue for enhancing X‐ray detection performance via cascade assembly of various perovskites with complementary properties., By integrating a layered 2D perovskite with a vertically crystallized MAPbI3, a double‐layer perovskite is constructed for direct X‐ray detection, showing stable baseline, a high sensitivity of 19 503 μC Gyair −1 cm−2, and a low detection limit of 480 nGyair s−1. This work provides a strategy to unlock the performance limitations stemming from the intrinsic properties of the perovskite.

Background: Recently, the paradigm of computed tomography (CT) reconstruction has shifted as the deep learning technique evolves. In this study, we proposed a new convolutional neural network (called ADAPTIVE-NET) to perform CT image reconstruction directly from a sinogram by integrating the analytical domain transformation knowledge. Methods: In the proposed ADAPTIVE-NET, a specific network layer with constant weights was customized to transform the sinogram onto the CT image domain via analytical back-projection. With this new framework, feature extractions were performed simultaneously on both the sinogram domain and the CT image domain. The Mayo low dose CT (LDCT) data was used to validate the new network. In particular, the new network was compared with the previously proposed residual encoder-decoder (RED)-CNN network. For each network, the mean square error (MSE) loss with and without VGG- based perceptual loss was compared. Furthermore, to evaluate the image quality with certain metrics, the noise correlation was quantified via the noise power spectrum (NPS) on the reconstructed LDCT for each method. Results: CT images that have clinically relevant dimensions of 512×512 can be easily reconstructed from a sinogram on a single graphics processing unit (GPU) with moderate memory size (e.g., 11 GB) by ADAPTIVE-NET. With the same MSE loss function, the new network is able to generate better results than the RED-CNN. Moreover, the new network is able to reconstruct natural looking CT images with enhanced image quality if jointly using the VGG loss. Conclusions: The newly proposed end-to-end supervised ADAPTIVE-NET is able to reconstruct high- quality LDCT images directly from a sinogram.

X-ray radiation is harmful to human health. Thus, obtaining a better reconstructed image with few projection view constraints is a major challenge in the computed tomography (CT) field to reduce radiation dose. In this study, we proposed and tested a new algorithm that combines penalized weighted least-squares using total generalized variation (PWLS-TGV) and dictionary learning (DL), named PWLS-TGV-DL to address this challenge. We first presented and tested this new algorithm and evaluated it through both data simulation and physical experiments. We then analyzed experimental data in terms of image qualitative and quantitative measures, such as the structural similarity index (SSIM) and the root mean square error (RMSE). The experiments and data analysis indicated that applying the new algorithm to CT data recovered images more efficiently and yielded better results than the traditional CT image reconstruction approaches.

Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radiation of the regular dose CT. However, ensuring image quality while reducing the radiation dose in the low-dose CT imaging is a major challenge. Compared to classical filtered back-projection (FBP) algorithms, statistical iterative reconstruction (SIR) methods for modeling measurement statistics and imaging geometry can significantly reduce the radiation dose, while maintaining the image quality in a variety of CT applications. To facilitate low-dose CT imaging, we in this study proposed an improved statistical iterative reconstruction scheme based on the penalized weighted least squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL), which is named as a method of PWLS-TV-DL. To evaluate this PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, and analyzed the results in terms of image quality and calculation. The results show that the proposed method is better than the comparison methods, which indicates the potential of applying this PWLS-TV-DL method to reconstruct low-dose CT images.

Sparse-view CT is a promising approach for reducing the x-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection algorithm suffer from severe streaking artifacts. Iterative reconstruction algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, a model-driven deep learning CT image reconstruction method, which unrolls the iterative optimization procedures into a deep neural network, has shown exciting prospects for improving image quality and shortening the reconstruction time. In this work, we explore a generalized unrolling scheme for such an iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

In this work, a novel, to the best of our knowledge, approach based on an x-ray thin lens imaging theory is proposed to predict the angular sensitivity responses of dual-phase-grating differential phase contrast (DPC) interferometers. Experimental validations have been performed to demonstrate the high accuracy of theoretical predictions using two different setups: one with real source images and the other with virtual source images. This new sensitivity calculation method is helpful to optimize the DPC imaging performance of a dual-phase-grating system.

Purpose The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. Methods In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. Results Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth. Conclusions In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

Objective: The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system. Methods: In this work, a novel deep CNN based phase signal extraction and image noise suppression algorithm (named as XP-NET) is developed. The numerical phase phantom, the ex vivo biological specimen and the ACR breast phantom are evaluated via the numerical simulations and experimental studies, separately. Moreover, images are also evaluated under different low radiation levels to verify its dose reduction capability. Results: Compared with the conventional analytical method, the novel XP-NET algorithm is able to reduce the bias of large DPC signals and hence increasing the DPC signal accuracy by more than 15%. Additionally, the XP-NET is able to reduce DPC image noise by about 50% for low dose DPC imaging tasks. Conclusion: This proposed novel end-to-end supervised XP-NET has a great potential to improve the DPC signal accuracy, reduce image noise, and preserve object details. Significance: We demonstrate that the deep CNN technique provides a promising approach to improve the grating-based XPCI performance and its dose efficiency in future biomedical applications.

Purpose In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. Results The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

In this work, we developed a new theoretical framework using wave optics to explain the working mechanism of the grating based X-ray differential phase contrast imaging (XPCI) interferometer systems consist of more than one phase grating. Under the optical reversibility principle, the wave optics interpretation was simplified into the geometrical optics interpretation, in which the phase grating was treated as a thin lens. Moreover, it was derived that the period of an arrayed source, e.g., the period of a source grating, is always equal to the period of the diffraction fringe formed on the source plane. When a source grating is utilized, the theory indicated that it is better to keep the periods of the two phase gratings different to generate large period diffraction fringes. Experiments were performed to validate these theoretical findings.

As a quantitative CT imaging technique, the dual-energy CT (DECT) imaging method attracts a lot of research interests. However, material decomposition from high energy (HE) and low energy (LE) data may suffer from magnified noise, resulting in severe degradation of image quality and decomposition accuracy. To overcome these challenges, this study presents a novel DECT material decomposition method based on deep neural network (DNN). In particular, this new DNN integrates the CT image reconstruction task and the nonlinear material decomposition procedures into one single network. This end-to-end network consists of three compartments: the sinogram domain decomposition compartment, the user-defined analytical domain transformation operation (OP) compartment, and the image domain decomposition compartment. By design, both the first and third compartments are responsible for complicated nonlinear material decomposition, while denoising the DECT images. Natural images are used to synthesized the dual-energy data with assumed certain volume fractions and density distributions. By doing so, the burden of collecting clinical DECT data can be significantly reduced, therefore the new DECT reconstruction framework becomes more easy to be implemented. Both numerical and experimental validation results demonstrate that the proposed DNN based DECT reconstruction algorithm can generate high quality basis images with improved accuracy.

Low-dose computed tomography (CT) has attracted much attention in clinical applications since X-ray radiations can cause serious health risks to patients. Sparse-view CT imaging is one of the major ways to reduce radiation dose. However, if reconstructing the sparse-view CT images with conventional filtered backprojection (FBP) algorithm, image quality may be significantly degraded due to the severe streaking artifacts. Therefore, iterative sparse-view CT image reconstruction (IR) algorithms have been developed and utilized to improve image quality. One drawback of using the IR algorithms is they usually spend long computation time. Additionally, adjusting and optimizing the hyper-parameters that are needed during iteration procedures is also time-consuming, some- times may even depend on individual experience. These potential drawbacks strongly limit the wide applications of IR algorithms. Aiming at partially overcome such difficulties, in the present work, we propose a deep iterative reconstruction (DIR) framework to generalize the conventional IR algorithms by mapping them into the deep neural network (DNN) technique. With this proposed DIR algorithms, the prior term, the data fidelity term, and the hyper-parameters can all be represented and learned by network. By doing so, some generalized iterative models can be used to perform high quality sparse-view CT image reconstructions. Numerical experiments based on clinical patient data demonstrated that the proposed DIR algorithms can mitigate the streaking artifacts more effectively while well preserving the subtle structures.

Due to the high hardware complexity and low dose efficiency of existing X-ray phase contrast imaging, the biomedical and clinical applications of this novel imaging technique have been hindered. This study proposes a deep learning method, named DeepPhase, to extract differential phase contrast (DPC) image from two dual-energy absorption images. It obviates the need of dedicated DPC imaging devices such as Talbot–Lau gratings and is compatible with diagnostic-level dual-energy X-ray imaging hardware. Given two dual-energy absorption images for an object, all we need to produce its DPC image at a certain energy is a well-trained DeepPhase network. Results demonstrate that, compared with conventional Talbot–Lau interferometry, DeepPhase achieves high-quality DPC imaging at multiple dual-energy combinations and low radiation dose.

Medical X-ray computed tomography requires fast imaging at a low dose rate and in a large area. Halide perovskites have shown high potential for X-ray detection, but a pressing challenge is the current lack of low-cost methods for large-scale fabrication of high-quality thick perovskite films. Here we demonstrate and elucidate an aerosol-liquid-solid process to enable continuous growth of uniform halide perovskites films at low temperature over a large area of 100 cm2, which are vertically monolithic and monocrystalline, and thus beneficial to carrier transport. Direct conversion X-ray detectors based on the representative CsPbI2Br films in conjunction with a deliberated, interface-engineered C-electrode have realized an unprecedented sensitivity (≥1.48×105 μC Gyair−1cm−2). We have further demonstrated the high-resolution radiographic imaging capability of these films. These results lay the groundwork for large scale application of halide perovskites in radiation detectors.

In dental computed tomography (CT) scanning, high-quality images are crucial for oral disease diagnosis and treatment. However, many artifacts, such as metal artifacts, downsampling artifacts and motion artifacts, can degrade the image quality in practice. The main purpose of this article is to reduce motion artifacts. Motion artifacts are caused by the movement of patients during data acquisition during the dental CT scanning process. To remove motion artifacts, the goal of this study was to develop a dental CT motion artifact-correction algorithm based on a deep learning approach. We used dental CT data with motion artifacts reconstructed by conventional filtered back-projection (FBP) as inputs to a deep neural network and used the corresponding high-quality CT data as labeled data during training. We proposed training a generative adversarial network (GAN) with Wasserstein distance and mean squared error (MSE) loss to remove motion artifacts and to obtain high-quality CT dental images. In our network, to improve the generator structure, the generator used a cascaded CNN-Net style network with residual blocks. To the best of our knowledge, this work describes the first deep learning architecture method used with a commercial cone-beam dental CT scanner. We compared the performance of a general GAN and the m-WGAN. The experimental results confirmed that the proposed algorithm effectively removes motion artifacts from dental CT scans. The proposed m-WGAN method resulted in a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and a lower root-mean-squared error (RMSE) than the general GAN method.

Most contemporary X-ray detectors adopt device structures with non/low-gain energy conversion, such that a fairly thick X-ray photoconductor or scintillator is required to generate sufficient X-ray-induced charges, and thus numerous merits for thin devices, such as mechanical flexibility and high spatial resolution, have to be compromised. This dilemma is overcome by adopting a new high-gain device concept of a heterojunction X-ray phototransistor. In contrast to conventional detectors, X-ray phototransistors allow both electrical gating and photodoping for effective carrier-density modulation, leading to high photoconductive gain and low noise. As a result, ultrahigh sensitivities of over 105 μC Gyair-1 cm-2 with low detection limit are achieved by just using an ≈50 nm thin photoconductor. The employment of ultrathin photoconductors also endows the detectors with superior flexibility and high imaging resolution. This concept offers great promise in realizing well-balanced detection performance, mechanical flexibility, integration, and cost for next-generation X-ray detectors.

This article introduces a new scintillator material Rb2AgBr3 that has good luminescence properties from the perspective of synthesis, characterization, and theoretical calculation. A scintillation detector is constructed, and successful X‐ray static and dynamic imaging is achieved, pointing to a promising direction for X‐ray indirect detectors in future.

Creating monolithic silicon/perovskite structures is a promising approach to engage emerging perovskite materials with silicon circuitry, which is essential to achieve industry-scale applications such as high performance X-ray detection. In particular, to achieve pixelated perovskite is a key step to address the issue of electrical crosstalk and low spatial resolution in imaging caused by inefficient migration and collection of carriers in the direction of signal collection. However, with the existing top-down methods, the thickness of patterned perovskite is limited to be in submicron level, which is far from highly efficient absorption of X-ray energy, further compromising the detection sensitivity. Here, we successfully demonstrate 3D hybrid perovskite crystal arrays with the thickness of ~300 μm on pixelated silicon substrate (forming co-axial silicon/perovskite heterojunction arrays) by melting PbBr2 and in situ chemical vapor conversion. Both morphological and optical properties of the resulting heterojunction arrays are systematically investigated. Furthermore, we demonstrate a high performance direct-conversion flat panel X-ray detector which exhibits high sensitivity of 242 μC Gyair−1 cm−2 that is much higher than the commercially available α-Se, and fast response speed (rising and falling time are 0.5 ms and 1.3 ms, respectively). The proposed strategy addresses the trade-off problem between the high sensitivity (requiring ~ mm-thickness perovskite crystal) and high spatial resolution (patterning of perovskite crystal) to achieve high-performance X-ray detection. This work not only offers a new pathway to fabricate pixelated μm-thick perovskite-based X-ray detectors, but also impacts on the application and functionalization of perovskite materials in silicon circuitry.

The single-shot x-ray Talbot–Lau interferometer-based differential phase contrast (DPC) imaging is able to accelerate time-consuming data acquisition; however, the extracted phase image suffers from severe image artifacts. Here, we propose to estimate the DPC image via a deep convolutional neural network (CNN) incorporated with the physical imaging model. Instead of training the CNN with thousands of labeled data beforehand, both phantom and biological specimen validation experiments show that high-quality DPC images can be automatically generated from only one single-shot projection image with a certain periodic moiré pattern. This work provides a new, to the best of our knowledge, paradigm for single-shot x-ray DPC imaging.

The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts.

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.

In this study, we propose to remove Moiré image artifact induced by system instabilities in grating-based x-ray interferometry imaging using convolutional neural network (CNN) technique. This method reduces Moiré image artifact in image-domain via a learned image post-processing procedure, rather than developing signal retrieval optimization algorithms to minimize the inconsistencies between acquired phase stepping data and assumed signal model. To achieve this aim, we suggested to train the CNN network using dataset synthesized from both natural images and experimentally acquired Moiré artifact-only images. In particular, a novel approach is developed to generate a large number of various high quality Moiré artifact-only images from finite groups of experimental phase stepping data. Both numerical and experimental results demonstrate that the developed CNN method is able to effectively remove the undesired Moiré image artifact. As a result, the image quality of a practical grating-based x-ray interferometry system can be greatly improved.

Breast cancer is the most common type of cancer in women. Mammography has been a good method of performing early screening to detect breast cancer. An alternative technique, digital breast tomosynthesis (DBT), which is based on limited-angle tomography, solves the overlapping problem associated with mammography but also has limitations. To decrease the limitations of motion artifacts and poor reconstructed image quality that are imposed by the use of a rotating anode with a single X-ray source in conventional DBT, we have developed and demonstrated a stationary DBT (s-DBT) system that uses a carbon nanotube (CNT) X-ray source array. This arc array consists of 9 X-ray-generating focal spots distributed across a 38.7° angle. Compared with a traditional thermal emission X-ray source, a CNT X-ray source has several desirable attributes, such as miniaturization, fast response time, efficient focusing and improved control of emitted electrons; as a result, the tested system can acquire projection images by electronically switching individual X-ray sources on and off conveniently without mechanical motion of either the X-ray source or the detector, leading to improved spatial resolution of acquired images and reduced radiation doses. The tested system was developed in an attempt to find a feasible method to avoid adverse effects and improve image quality relative to conventional DBT systems, and the results reconstructed using a modified form of FDK algorithm indicate the achievement of this objective.

The use of grating based Talbot-Lau interferometry permits the acquisition of differential phase contrast (DPC) imaging with a conventional medical x-ray source and detector. However, due to the limited area of the gratings, limited area of the detector, or both, data truncation image artifacts are often observed in tomographic DPC acquisitions and reconstructions, such as tomosynthesis (limited-angle tomography). When data are truncated in the conventional x-ray absorption tomosynthesis imaging, a variety of methods have been developed to mitigate the truncation artifacts. However, the same strategies used to mitigate absorption truncation artifacts do not yield satisfactory reconstruction results in DPC tomosynthesis reconstruction. In this work,several new methods have been proposed to mitigate data truncation artifacts in a DPC tomosynthesis system. The proposed methods have been validated using experimental data of a mammography accreditation phantom, a bovine udder, as well as several human cadaver breast specimens using a bench-top DPC imaging system at our facility.

PURPOSE In grating-based x-ray multi-contrast imaging, signals of three contrast mechanisms-absorption contrast, differential phase contrast (DPC), and dark-field contrast-can be estimated from the same set of acquired data. The estimated signals, N0 (related to absorption), N1 (related to dark-field), and φ (related to DPC) may be intrinsically biased. However, it is yet unclear how large these biases are and how the data acquisition parameters affect the biases in the extracted signals. The purpose of this paper was to address these questions. METHODS The biases of the extracted signals (i.e., N0 , N1 and φ) were theoretically studied for a well-known signal estimation method. Experimental data acquired from a grating-based x-ray multi-contrast benchtop imaging system with a photon counting detector were used to validate the theoretical results for the signal biases of the three contrast mechanisms. RESULTS Both theoretical and experimental studies showed the following results: (1) The bias of signal estimation for the absorption contrast signal is zero; (2) The bias of signal estimation for N1 is inversely proportional to the number of phase steps and to the average fringe visibility of the grating interferometer, but the ratio between the bias and the signal level (i.e., the relative bias) is independent of the number of phase steps; (3) The bias of signal estimation for φ depends on the mean DPC signal level, the total exposure level of the multi-contrast data acquisition, and the mean fringe visibility of the interferometer. CONCLUSIONS In grating-based x-ray multi-contrast imaging, the estimated absorption contrast signal is unbiased; the estimated dark-field contrast signal is biased, but the relative bias is only dependent on the mean fringe visibility of the interferometer and the exposure level. The estimated DPC signal may be biased, and the bias level depends on the mean signal level, the exposure level, and the interferometer performance.

In grating based multi-contrast x-ray imaging, signals of three contrast mechanisms, namely absorption contrast, differential phase contrast (DPC) and dark-field contrast, can be estimated from a single data acquisition with several phase steps. The extracted signals, N 0 (related to absorption), N 1 (related to dark-field) and φ (related to DPC) may be intrinsically biased. In this work, the biases of the extracted N 0 , N 1 and φ from the well-known least square fitting method were theoretically derived. Furthermore, numerical simulation experiments were used to validate the derived theoretical formulae for the signal bias of all three contrast mechanisms. The theoretical predictions were in good agreement with the results of the simulations. The bias of the absorption contrast is zero. The signal bias for N 1 is inversely proportional to the number of phase steps and to the average fringe visibility of the grating interferometer. The bias of φ is related to several parameters, including the total exposure, the fringe visibility produced by the interferometer system, and the ground truth of φ. The larger the exposure and fringe visibility, the smaller the bias of φ.

The noise performance of grating-based differential phase contrast (DPC) imaging system is strongly dependent on the fringe visibility of the grating interferometer. Since the grating interferometer system is usually designed to be operated at a specific energy, deviation from that energy may lead to visibility loss and increased noise. By incorporating an energy-discriminating photon counting detector (PCD) into the system, photons with energies close to the operation energy of the interferometer can be selected, which offers the possibility of contrast-tonoise ratio (CNR) improvement. In our previous work, a singular value decomposition (SVD)-based rank one approximation method was developed to improve the CNR of DPC imaging. However, as the noise level and energy sensitivity of the interferometer may vary significantly from one energy bin to another, the signal and noise may not be separated well using the previously proposed method, therefore the full potential of the SVD method may not be achieved. This work presents a weighted SVD-based method, which maintains the noise reduction capability regardless of the similarity in the noise level across energy bins. The optimal weighting scheme was theoretically derived, and experimental phantom studies were performed to validate the theory and demonstrate the improved radiation dose efficiency of the proposed weighted SVD method.

Recent advances in single photon counting detectors (PCDs) are opening up new opportunities in medical imaging. However, the performance of PCDs is not flawless. Problems such as charge sharing may deteriorate the performance of PCD. This work studied the dependence of the signal and noise properties of a cadmium telluride (CdTe)-based PCD on the charge sharing effect and the anti-charge sharing (ACS) capability offered by the PCD. Through both serial and parallel cascaded systems analysis, a theoretical model was developed to trace the origin of charge sharing in CdTe-based PCD, which is primarily related to remote k-fluorescence re-absorption and spatial spreading of charge cloud. The ACS process was modeled as a sub-imaging state prior to the energy thresholding stage, and its impact on the noise power spectrum (NPS) of PCD can be qualitatively determined by the theoretical model. To validate the theoretical model, experimental studies with a CdTe-based PCD system (XC-FLITE X1, XCounter AB) was performed. Two x-ray radiation conditions, including an RQA-5 beam and a 40 kVp beam, were used for the NPS measurements. Both theoretical predictions and experimental results showed that ACS makes the NPS of the CdTe-based PCD flatter, which corresponds to reduced noise correlation length. The flatness of the NPS is further boosted by increasing the energy threshold or reducing the x-ray energy, both of which reduce the likelihood of registering multiple counts from the same incidenting x-ray photon.

In this paper, a novel method was developed to improve the radiation dose efficiency, viz., contrast to noise ratio normalized by dose (CNRD), of the grating-based X-ray differential phase contrast (DPC) imaging system that is integrated with an energy-resolving photon counting detector. The method exploits the low-dimensionality of the spatial-spectral DPC image matrix acquired from different energy windows. A low rank approximation of the spatial-spectral image matrix was developed to reduce image noise while retaining the DPC signal accuracy for every energy window. Numerical simulations and experimental phantom studies have been performed to validate the proposed method by showing noise reduction and CNRD improvement for each energy window.

For grating interferometer-based x-ray differential phase contrast (DPC) imaging systems, their noise performance is strongly dependent on both the visibility of the interference fringe pattern and the total number of photons used to acquire and extract the DPC signal. For a given interferometer, it is usually designed to work at a specific x-ray energy, therefore any deviation from the designed energy may result in certain visibility loss. In this work, a single photon counting detector (PCD) was incorporated into a DPC imaging system, which enabled photons with energies close to the designed operation energy of the interferometer to be selectively used for DPC signal extraction. This approach led to significant boost in the fringe visibility, but it also discarded x-ray photons with other energies incident on the detector and might result in degradations of the overall radiation dose efficiency of the DPC imaging systems. This work presents a novel singular value decomposition (SVD)-based method to leverage the entire spectrum of x-ray photons detected by the PCD, enabling both fringe visibility improvement and reduction in image noise. As evidenced by the results of experimental phantom studies, the contrast-to-noise ratio of the final DPC images could be effectively improved by the proposed method.

One of the most exciting aspects of the grating based x-ray differential phase contrast (DPC) acquisition method is the concurrent generation of the so-called dark field (DF) signal, along with the classical absorption signal and the novel DPC signal. The DF signal is associated with local distribution of small angle scatterers in an image object, while the absorption signal and DPC signal are often used to characterize the relatively uniform structure of the image object. Besides the endogenous image contrast, exogenous contrast media are often used in x-ray imaging to locally enhance the image signal. This paper proposes a potential contrast medium for DF signal enhancement: microbubbles (MBs). MBs have already been developed for clinical use in ultrasound imaging, and recent experimental studies have shown that MBs may also enhance the DF signal, although it remained unclear how the physical characteristics of the MBs quantitatively impact the DF signal. In this paper, a systematic study was performed to investigate the quantitative relationships between the DF signal and the following properties of MBs: size, concentration, shell thickness, size uniformity, and whether gold nanoparticles were attached. The experimental results demonstrated that, an increased MB size (about 4 microns) may generate a stronger DF signal for our DPC imaging system; additionally, a moderately increased shell thickness and the use of gold nanoparticles on the shell surface also resulted in further enhancement of the DF signal. These findings may provide critical information needed for using MBs as the contrast agent of x-ray DF imaging.

Differential phase contrast imaging is a promising new image modality that utilizes the refraction rather than the absorption of x-rays to image an object. A Talbot-Lau interferometer may be used to permit differential phase contrast imaging with a conventional medical x-ray source and detector. However, the current size of the gratings fabricated for these interferometers are often relatively small. As a result, data truncation image artifacts are often observed in a tomographic acquisition and reconstruction. When data are truncated in x-ray absorption imaging, the methods have been introduced to mitigate the truncation artifacts. However, the same strategy to mitigate absorption truncation artifacts may not be appropriate for differential phase contrast or dark field tomographic imaging. In this work, several new methods to mitigate data truncation artifacts in a multi-contrast imaging system have been proposed and evaluated for tomosynthesis data acquisitions. The proposed methods were validated using experimental data acquired for a bovine udder as well as several cadaver breast specimens using a benchtop system at our facility.

A grating-based x-ray multi-contrast imaging system integrates a source grating G0, a diffraction grating G1, and an analyzer grating G2 into a conventional x-ray imaging system to generate images with three contrast mechanisms: absorption contrast, differential phase contrast, and dark field contrast. To facilitate the potential translation of this multi-contrast imaging system into a clinical setting, our group has developed several single-shot data acquisition methods to eliminate the necessity of the time-consuming phase stepping procedure. These methods have enabled us to acquire multi-contrast images with the same data acquisition time currently used for absorption imaging. One of the proposed methods is the use a staggered G2 grating. In this work, we propose to incorporate this staggered G2 grating into a state-of-the-art breast tomosynthesis imaging system to generate tomosynthesis images with three contrast mechanisms. The introduction of this staggered G2 grating will reject scatter and thus improve image contrast at the detector plane, but it will also absorb some x-ray photons reaching detector, thus increasing noise and reducing the contrast to noise ratio (CNR). Therefore, a key technical question is whether the CNR and dose efficiency can be maintained for absorption imaging after the introduction of this staggered G2 grating. In this paper, both the CNR and scatter-to-primary ratio (SPR) of absorption imaging were investigated with Monte Carlo simulations for a variety of staggered G2 grating designs.

In a grating interferometer-based x-ray differential phase contrast (DPC) imaging system, an analyzer grating (i.e. a G2 grating) is typically used to help record the important refraction information obtained with the phase stepping technique. Such a method requires the sequential movement of the G2 grating as well as multiple x-ray exposures to perform phase stepping, and thus conventional DPC imaging is very time-consuming. Additionally, it also has some mechanical instability issues due to the movement of the G2 grating. To accelerate the data acquisition speed and achieve single shot x-ray DPC imaging with a collinear type G2 grating, in this study, a new signal extraction method had been investigated. With this alternative approach, a non-zero angle of rotation between the diffraction pattern (generated by the G1 grating) and the collinear G2 grating is used during the entire data acquisition. Due to this deliberate grating misalignment, a visible moire pattern with a certain period shall be detected. Initial experiments have demonstrated that this new signal extraction method is able to provide us with three different types of signal: absorption, differential phase, and the dark field image signals. Although the spatial resolution for both the differential phase and the dark field images is blurred by several pixel length due to the used interpolation operation, the absorption image maintains the same spatial resolution as in the conventional x-ray imaging. This developed novel signal analysis method enables single shot DPC imaging and can greatly reduce the data acquisition time, thus facilitating the implementation of DPC imaging in the medical field.

Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure to acquire data that enables the extraction of phase information. This method prolongs the time needed for data acquisition by several times compared with conventional x-ray absorption image acquisitions. A novel analyzer grating design was developed in this work to eliminate the additional data acquisition time needed to perform phase stepping in DPCI. The new analyzer grating was fabricated such that the linear grating structures are shifted from one detector row to the next; the amount of the lateral shift was equal to a fraction of the x-ray diffraction fringe pattern. The x-ray data from several neighboring detector rows were then combined to extract differential phase information. Initial experimental results have demonstrated that the new analyzer grating enables accurate DPCI signal acquisition from a single x-ray exposure like conventional x-ray absorption imaging.

Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure that involves sequential grating motion and multiple x-ray exposures to obtain x-ray phase information. Such a data acquisition process breaks the continuous data acquisition into several step-and-shoot data acquisition sequences. Between two neighboring x-ray pulses, the acquisition will have to be stopped for the grating to translate into the next phase stepping position. This setup also requires that the grating not be fixed. If the gratings are to be mounted onto a fast-rotating gantry (such as those used in x-ray CT), this translation of the grating would add another potential source of mechanical instability. To accelerate the data acquisition speed and improve the mechanical stability of of DPCI data acquisitions, a new grating design was developed. In this method, one of the gratings used in DPCI was divided into four-row groups, within each group, grating structures have a designed offset with respect to their neighboring rows. This design allows the acquired data from any adjacent four detector rows to be combined in order to retrieve the needed x-ray differential phase information from a single x-ray exposure. Both numerical simulations and initial phantom experiments have demonstrated that the new interferometer design can enable DPCI image acquisitions without this well-known overhead in data acquisition time.

Unlike conventional x-ray absorption imaging, x-ray differential phase contrast imaging (DPCI) uses a phase retrieval algorithm to obtain x-ray phase information from a group of x-ray intensity measurements. As a result, the noise performance of DPCI is expected to differ from that of x-ray absorption imaging. Given the total number of x-ray photons used in imaging, lower noise variance in estimated phase contrast images suggests superior dose efficiency, which is one of the most desirable feature in x-ray imaging. When an algorithm is used to retrieve the phase information, it is important to understand what the lowest possible noise variance would be and whether the algorithm used to retrieve the phase information yields the lowest possible noise variance. To address these questions for differential phase contrast imaging, we studied the noise performance of DPC imaging using the powerful Cramer-Rao lower bound (CRLB) in statistical signal estimation method. Results demonstrated that the noise variances in DPCI images obtained by the algorithmic phase retrieval are always higher than the CRLB, which implies a possible sub-optimality of current phase estimation method. The results also call for the need to apply statistical signal estimation theory to DPCI in order to further improve its noise performance and dose efficiency.

Purpose: The anatomical noise power spectra (NPS) for differential phase contrast (DPC) and dark field (DF) imaging have recently been characterized using a power-law model with two parameters, alpha and beta, an innovative extension to the methodology used in x-ray attenuation based breast imaging such as mammography, DBT, or cone-beam CT. Beta values of 3.6, 2.6, and 1.3 have been measured for absorption, DPC, and DF respectively for cadaver breasts imaged in the coronal plane; these dramatic differences should be reflected in their detection performance. The purpose of this study was to determine the impact of anatomical noise on breast calcification detection and compare the detection performance of the three contrast mechanisms of a multi-contrast x-ray imaging system. Methods: In our studies, a calcification image object was segmented out of the multi-contrast images of a cadaver breast specimen. 50 measured total NPS were measured from breast cadavers directly. The ideal model observer detectability was calculated for a range of doses (5–100%) and a range of calcification sizes (diameter = 0.25–2.5 mm). Results: Overall we found the highest average detectability corresponded to DPC imaging (7.4 for 1 mm calc.), with DF the next highest (3.8 for 1 mm calc.), and absorption the lowest (3.2 for 1 mm calc.). However, absorption imaging also showed the slowest dependence on dose of the three modalities due to the significant anatomical noise. DPC showed a peak detectability for calcifications ∼1.25 mm in diameter, DF showed a peak for calcifications around 0.75 mm in diameter, and absorption imaging had no such peak in the range explored. Conclusion: Understanding imaging performance for DPC and DF is critical to transition these modalities to the clinic. The results presented here offer new insight into how these modalities complement absorption imaging to maximize the likelihood of detecting early breast cancers. J. Garrett, Y. Ge, K. Li: Nothing to disclose. G.-H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.

By adding a Talbot–Lau interferometer to a conventional x-ray absorption computed tomography (CT) imaging system, both differential phase contrast (DPC) signal and absorption contrast signal can be simultaneously measured from the same set of CT measurements. The imaging performance of such multi-contrast x-ray CT imaging systems can be characterized with standard metrics such as noise variance, noise power spectrum, contrast-to-noise ratio, modulation transfer function (MTF), and task-based detectability index. Among these metrics, the measurement of the MTF can be challenging in DPC-CT systems due to several confounding factors such as phase wrapping and the difficulty of using fine wires as probes. To address these technical challenges, this paper discusses a viable and reliable method to experimentally measure the MTF of DPC-CT. It has been found that the spatial resolution of DPC-CT is degraded, when compared to that of the corresponding absorption CT, due to the presence of a source grating G0 in the Talbot–Lau interferometer. An effective MTF was introduced and experimentally estimated to describe the impact of the Talbot–Lau interferometer on the system MTF.

Purpose: In x-ray absorption mammography, it has been found that the anatomical background noise can be characterized by a power law dependence on the spatial frequency, NPS a (f) ≈ αf −β . In this letter, the authors present the first experimental results of the corresponding exponents, β, for differential phase contrast (β DPC) and dark field contrast (β DF) mammography. Methods: A grating-based x-ray multicontrast imaging acquisition benchtop system was used to simultaneously acquire mammograms with three different contrast mechanisms from 15 cadaver breasts under the same x-ray data acquisition conditions. The cadaver breasts were imaged in the coronal plane. The authors’ experimental implementation of the well documented method [Burgess, Jacobson, and Judy, Med. Phys. 28, 419–437 (2001)] to extract the exponent β was first validated using anonymized clinical mammograms. Experiments were then used to determine β for the three types of mammograms for each cadaver breast acquired with our multicontrast imaging system: absorption contrast mammogram (β Abs.), differential phase contrast mammogram (β DPC), and dark-field contrast mammogram (β DF). Results: The measured β values, acquired in the coronal plane with the benchtop multicontrast imaging system are β Abs. = 3.61 ± 0.49, β DPC = 2.54 ± 0.75, and β DF = 1.44 ± 0.49 for absorption, differential phase, and dark field mammogram, respectively. Conclusions: The β values for differential phase contrast and dark field mammography are significantly lower than the measured value of β for the corresponding absorption contrast mammograms. The greatly reduced β value of the anatomical background noise in differential phase contrast and dark field mammograms may suggest potentially improved diagnostic performance for certain types of breast cancer imaging tasks.

Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI vol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo®, GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d′. Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Conclusions: Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

Purpose: The purpose of this work was to experimentally assess the unique spatial resolution characteristics of statistical model based iterative reconstruction (MBIR) along both axial and z directions and to examine the impact of spatial resolution on the task-based diagnostic detection performance. Methods: An anthropomorphic chest phantom was repeatedly scanned 100 times using a clinical CT scanner at four dose levels (25%, 50%, 75%, 100%). Both FBP and MBIR (Veo™) were used for reconstruction. Nine objects with contrasts ranging from 13 to 1710 HU were used to assess spatial resolution. The axial and z resolutions were quantified locally in the image domain by point spread functions (PSF) and slice sensitivity profiles (SSP) respectively. The tradeoff between local image noise and resolution and their joint impact on the channelized Hotelling observer (CHO) detectabilities (d ' ) was investigated. Results: The axial resolution of MBIR improved monotonically with increasing dose and contrast level (FWHM_PSF=2.0 mm at 13 HU/25% dose and was 0.8 mm 1710 HU/100% dose). In comparison, axial resolution of FBP was independent of dose and contrast (FWHM_PSF=1.2 mm). The z resolution of MBIR demonstrated similar contrast dependence but only negligible dose dependence. The spatial resolution of MBIR and FBP became equivalent at some transitional contrast levels (280 HU for 25% dose and 90 HU for 100% dose), above which MBIR demonstrated superior resolution than FBP (and vice versa). Spatial resolution and noise assessed at the same location demonstrated a strong tradeoff in MBIR, and CHO detectability index d' was improved by MBIR for all contrast and dose levels. Conclusion: MBIR produces images with unique spatial resolution characteristics and introduces new challenges to its clinical use and evaluation. One potential solution, as suggested by this work, is to perform rigorous spatial resolution and noise measurements at different dose levels for each specific task. K. Li, J. Garrett, Y. Ge: Nothing to disclose; G.-H. Chen: Research funded, General Electric Company Research funded, Siemens AG Research funded, Varian Medical Systems, Research funded, Hologic, Inc.

Purpose: This paper concerns the feasibility of x-ray differential phase contrast (DPC) tomosynthesis imaging using a grating-based DPC benchtop experimental system, which is equipped with a commercial digital flat-panel detector and a medical-grade rotating-anode x-ray tube. An extensive system characterization was performed to quantify its imaging performance. Methods: The major components of the benchtop system include a diagnostic x-ray tube with a 1.0 mm nominal focal spot size, a flat-panel detector with 96 μm pixel pitch, a sample stage that rotates within a limited angular span of ±30°, and a Talbot-Lau interferometer with three x-ray gratings. A total of 21 projection views acquired with 3° increments were used to reconstruct three sets of tomosynthetic image volumes, including the conventional absorption contrast tomosynthesis image volume (AC-tomo) reconstructed using the filtered-backprojection (FBP) algorithm with the ramp kernel, the phase contrast tomosynthesis image volume (PC-tomo) reconstructed using FBP with a Hilbert kernel, and the differential phase contrast tomosynthesis image volume (DPC-tomo) reconstructed using the shift-and-add algorithm. Three inhouse physical phantoms containing tissue-surrogate materials were used to characterize the signal linearity, the signal difference-to-noise ratio (SDNR), the three-dimensional noise power spectrum (3D NPS), and the through-plane artifact spread function (ASF). Results: While DPC-tomo highlights edges and interfaces in the image object, PC-tomo removes the differential nature of the DPC projection data and its pixel values are linearly related to the decrement of the real part of the x-ray refractive index. The SDNR values of polyoxymethylene in water and polystyrene in oil are 1.5 and 1.0, respectively, in AC-tomo, and the values were improved to 3.0 and 2.0, respectively, in PC-tomo. PC-tomo and AC-tomo demonstrate equivalent ASF, but their noise characteristics quantified by the 3D NPS were found to be different due to the difference in the tomosynthesis image reconstruction algorithms. Conclusions: It is feasible to simultaneously generate x-ray differential phase contrast, phase contrast, and absorption contrast tomosynthesis images using a grating-based data acquisition setup. The method shows promise in improving the visibility of several low-density materials and therefore merits further investigation.