Material-specific Images Research Articles

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100 μm, a thickness of 500 μm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Diagnostic performance and feasibility of dual-layer detector dual-energy CT for characterization of urinary stones in patients of different sizes.

Urinary stones are frequently encountered in urology and are typically identified using non-contrast CT scans. Dual-energy CT (DECT) is a valuable imaging technique that produces material-specific images and allows for precise assessment of stone composition by estimating the effective atomic number (Zeff), a capability not achievable with the conventional single-energy CT's attenuation measurement method. To investigate the diagnostic performance and image quality of dual-layer detector DECT (dlDECT) in characterizing urinary stones in patients of different sizes. All consecutive dlDECT examinations with stone protocol and presence of urinary stones between July 2018 and November 2019 were retrospectively evaluated. Two radiologists independently reviewed 120 kVp and color-overlay Zeff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Zeff values measurement. A total of 739 urinary stones (median size 3.7 mm, range 1-35 mm) were identified on 177 CT examinations from 155 adults (mean age, 57 ± 15 years, 80 men, median weight 82.6 kg, range 42.6-186.9 kg). Using color-overlay Zeff images, the radiologists could subjectively interpret the composition in all stones ≥ 3 mm (n = 491). For stones with available reference standards (n = 74), dlDECT yielded a sensitivity of 80% (95%CI 44-98%) and a specificity of 98% (95%CI 92-100%) in visually discriminating uric acid from non-uric acid stones. Patients weighing > 90 kg and ≤ 90 kg had similar stone characterizability (p = 0.20), with 86% of stones characterized in the > 90 kg group and 87% in the ≤ 90 kg group. All examinations throughout various patients' weights revealed acceptable image quality. A Zeff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Zeff analysis revealed AUCs of 0.78 and 0.91 for differentiating calcium-based stones from other non-uric stones and all stone types, respectively. dlDECT allowed accurate differentiation of uric acid and non-uric acid stones among patients with different body sizes with acceptable image quality. The ability to accurately differentiate uric acid stones from non-uric acid stones using color-overlay Zeff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care.

Potential benefits of photon counting detector computed tomography in pediatric imaging.

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

Detection and characterization of urinary stones using material-specific images derived from contrast-enhanced dual-energy CT urography.

To determine the accuracy of material-specific images derived from contrast-enhanced dual-energy CT urography (DECTU) in detecting and measuring urinary stones in comparison with that of unenhanced images and its utility in calcified stone differentiation. 105 patients with 202 urinary stones (121 had confirmed composition by infrared spectroscopy) underwent triphasic (unenhanced, portal venous (VP) and excretory phase (EP)) DECTU. Material-specific images were derived in VP and EP with calcium-water, calcium-iodine and CaOxalate_Dihydrate (COD)-Hydroxyapatite (HAP) as basis material pairs. Stone number and size were recorded on unenhanced images and VP and EP material-specific images, where stone densities were also measured. Material densities of calcified stones (pure calcium oxalate [pCaO, n = 34], mixed calcium oxalate [mCaO, n = 14], mixed carbonate phosphate [mCaP, n = 70]) were compared and thresholds for differentiating these stones were determined using receiver operating characteristic analysis. All 202 urinary stones were detected on the unenhanced, calcium (water) and calcium (iodine) images in VP. While the detection rate was significantly decreased to 58 and 64% using calcium (water) and calcium (iodine) images in EP, respectively (all p < 0.001). Stone sizes measured on calcium (iodine) images in VP was similar to that of unenhanced images (10.6 vs 10.7 mm, p > 0.05). Significant differences in material densities were found among pCaO, mCaO and mCaP on COD(HAP) images with AUC of 0.72-0.74 for differentiating these stones. Material-specific images in VP derived from DECTU allow reliably detecting and measuring urinary tract stones in comparison with unenhanced images and can identify calcified stones with moderate diagnostic performance to provide potential 33% dose reduction. Material-specific images, especially the calcium (iodine) images in VP allow for reliable detection of urinary stones.Stone size measurement should be performed on the calcium (iodine) images in VP.Material density measurements on COD-HAP (VP) material decomposition images can be used to differentiate among pure calcium oxalate, mixed calcium oxalate and mixed carbonate phosphate stones with AUC of 0.72-0.74.

Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector.

Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter. In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure. The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5cm; copper: 0-0.9mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13cm for acrylic and 0.04mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

Joint Reconstruction and Spectrum Refinement for Photon-Counting-Detector Spectral CT.

Photon-counting detector CT (PCD-CT) is a revolutionary technology in decades in the field of CT. Its potential benefits in lowering noise, dose reduction, and material-specific imaging enable completely new clinical applications. Spectral reconstruction of basis material maps requires knowledge of the x-ray spectrum and the spectral response calibration of the detector. However, spectrum estimation errors caused by inaccurate energy threshold calibration will degrade the accuracy of the reconstructions. Existing spectrum estimation methods are not adequately modeled for bias in energy threshold position. Besides, directly solving a big number of variables of the pixel-wise effective spectra for PCD is an ill-conditioned problem so that stable solution is hardly achievable. In this paper, we assumed the effective spectra variation across the detector mainly comes from the calibration error in the energy threshold positions as well as the intrinsic threshold distribution. We propose a joint reconstruction and spectrum refinement algorithm (JoSR) that introduces an innovative spectrum model based on non-negative matrix factorization (NMF) to significantly reduce the dimension of unknowns so that makes the problem well-conditioned. The polychromatic spectral imaging model and the basis material decomposition method together form an optimization objective. The proximal regularized block coordinate descent algorithm is adopted to deal with the non-convex optimization problem to ensure convergence. Simulation studies and experiments on a laboratory PCD-CT system validated the proposed JoSR method. The results demonstrate its advantages on image quality and quantitative accuracy over other state-of-the-art methods in the field.

Pseudo dual-energy CT-derived iodine mapping using single-energy CT data based on a convolution neural network.

The objectives of this study are: (1) to develop a convolutional neural network model that yields pseudo high-energy CT (CTpseudo_high) from simple image processed low-energy CT (CTlow) images, and (2) to create a pseudo iodine map (IMpseudo) and pseudo virtual non-contrast (VNCpseudo) images for thoracic and abdominal regions. Eighty patients who underwent dual-energy CT (DECT) examinations were enrolled. The data obtained from 55, 5, and 20 patients were used for training, validation, and testing, respectively. The ResUnet model was used for image generation model and was trained using CTlow and high-energy CT (CThigh) images. The proposed model performance was evaluated by calculating the CT values, image noise, mean absolute errors (MAEs), and histogram intersections (HIs). The mean difference in the CT values between CTpseudo_high and CThigh images were less than 6 Hounsfield unit (HU) for all evaluating patients. The image noise of CTpseudo_high was significantly lower than that of CThigh. The mean MAEs was less than 15 HU, and HIs were almost 1.000 for all the patients. The evaluation metrics of IM and VNC exhibited the same tendency as that of the comparison between CTpseudo_high and CThigh images. Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images. We constructed the CNN-based model which can generate pseudo DECT image and DECT-derived material-specific image using only simple image-processed CTlow images for the thoracic and abdominal regions.

IMD-MTFC: Image-Domain Material Decomposition via Material-Image Tensor Factorization and Clustering for Spectral CT

Spectral computed tomography (CT) provides multispectral X-ray information that can be used for quantitative material-specific imaging compared to the conventional CT. However, the low-count photon rate in a single energy bin may lead to highly noisy measurements with compromised material contrast and accuracy. Moreover, the complicated material decomposition process is an ill-posed inverse problem, which is sensitive to noise. In this work, we develop an image-domain material decomposition method via material-image tensor factorization and clustering (IMD-MTFC) for spectral CT to obtain high-precision material-specific images. Specifically, a set of image patches is extracted from the normalized material-specific image tensors decomposed by the direct inversion (DI). Then, each of them is clustered in a given nonlocal neighboring area to explore the nonlocal self-similarity of material-specific images. Furthermore, the low-rank regularized Kronecker-basis-representation tensor factorization is employed to incorporate the sparsity and redundant correlation across the material-specific images. The split-Bregman is employed to optimize the model by dividing it into several subproblems. The performance of our method is validated with numerically simulated mouse projections, physical phantom, and preclinical experiments. The results confirm that the IMD-MTFC method outperforms other state-of-the-art competing methods.

Noise correlation and its impact on the performance of multi-material decomposition-based spectral imaging in photon-counting CT.

It has been known that noise correlation plays an important role in the determination of the performance of spectral imaging based on two-material decomposition (2-MD). To further understand the basics of spectral imaging in photon-counting CT toward optimal design and implementation, we study the noise correlation in multi-MD (m-MD) and its impact on the performance of spectral imaging. We derive the equations that characterize the noise and noise correlation in the material-specific (basis) images in m-MD, followed by a simulation study to verify the derived equations and study the noise correlation's impact on the performance of spectral imaging. Using a specially designed digital phantom, the study of noise correlation runs over the cases of two-, three-, and four-MD (2-MD, 3-MD, and 4-MD). Then, the noise correlation's impact on the performance of spectral imaging in photon-counting CT is investigated, using a modified Shepp-Logan phantom. The results in 2-MD show that, in-line with what has been reported in the literature, the noise correlation coefficient between the material-specific images corresponding to the basis materials approaches -1. The results in m-MD (m≥3) are more complicated and interesting, as the noise correlation coefficients between a pair of the material-specific images alternate between ±1, and so do in the case of 4-MD. The m-MD data show that the noise in virtual monochromatic imaging (a form of spectral imaging) is moderate even though the noises in material-specific (basis) images vary drastically. The observation of noise correlation in 3-MD, 4-MD, and beyond (i.e., m-MD) is informative to the community. The relationship between noise correlation and the performance of spectral imaging revealed in this work may help clinical medical physicists understand the fundamentals of spectral imaging based on MD and optimize the performance of spectral imaging in photon-counting CT and other X-ray imaging modalities.

Projection decomposition via univariate optimization for dual-energy CT.

Dual-energy computed tomography (DECT) acquires two x-ray projection datasets with different x-ray energy spectra, performs material-specific image reconstruction based on the energy-dependent non-linear integral model, and provides more accurate quantification of attenuation coefficients than single energy spectrum CT. In the diagnostic energy range, x-ray energy-dependent attenuation is mainly caused by photoelectric absorption and Compton scattering. Theoretically, these two physical components of the x-ray attenuation mechanism can be determined from two projection datasets with distinct energy spectra. Practically, the solution of the non-linear integral equation is complicated due to spectral uncertainty, detector sensitivity, and data noise. Conventional multivariable optimization methods are prone to local minima. In this paper, we develop a new method for DECT image reconstruction in the projection domain. This method combines an analytic solution of a polynomial equation and a univariate optimization to solve the polychromatic non-linear integral equation. The polynomial equation of an odd order has a unique real solution with sufficient accuracy for image reconstruction, and the univariate optimization can achieve the global optimal solution, allowing accurate and stable projection decomposition for DECT. Numerical and physical phantom experiments are performed to demonstrate the effectiveness of the method in comparison with the state-of-the-art projection decomposition methods. As a result, the univariate optimization method yields a quality improvement of 15% for image reconstruction and substantial reduction of the computational time, as compared to the multivariable optimization methods.

Single-Shot Quantitative X-ray Imaging Using a Primary Modulator and Dual-Layer Detector: Simulation and Phantom Studies.

Conventional x-ray imaging provides little quantitative information due to scatter, beam hardening, and overlaying tissues. A single-shot quantitative x-ray imaging (SSQI) method was previously developed to quantify material-specific densities in x-ray imaging by combining the use of a primary modulator (PM) and dual-layer (DL) detector. The feasibility of this concept was demonstrated with simulations using an iterative patch-based method. In this work, we propose a new algorithm pipeline for SSQI that enables accurate quantification and high computational efficiency. The DL images contain four measurements that are obtained behind the unattenuated and partially attenuated regions of the PM of each layer. Using the low-frequency property of scatter and a pre-calibrated material decomposition (MD), four unknowns (i.e., two scatter images and two material-specific images) are jointly recovered by directly solving four equations given by the four measurements. We tested this algorithm in simulations and further demonstrated its efficacy on chest phantom experiments. Through simulation, we show that the new method for MD is robust against scatter. Its performance improves with smaller PM pitch size and smaller focal spot blur. The RMSE in material-specific images compared to ground truth reduces by 52%-84% versus without scatter correction. For our experimental study, we successfully separated soft tissue and bone. The computational time for processing each view was ~8 s without optimization. The reported results further strengthen the potential of SSQI for widespread adoption, leading to quantitative imaging not only for x-ray imaging but also for real-time image guidance or cone-beam CT.

Spectral propagation-based x-ray phase-contrast computed tomography.

Purpose: Propagation-based x-ray imaging (PBI) is a phase-contrast technique that is employed in high-resolution imaging by introducing some distance between sample and detector. PBI causes characteristic intensity fringes that have to be processed with appropriate phase-retrieval algorithms, which has historically been a difficult task for objects composed of several different materials. Spectral x-ray imaging has been introduced to PBI to overcome this issue and to potentially utilize the spectral nature of the data for material-specific imaging. We aim to explore the potential of spectral PBI in three-dimensional computed tomography (CT) imaging in this work. Approach: We demonstrate phase-retrieval for experimental high-resolution spectral propagation-based CT data of a simple two-component sample, as well as a multimaterial capacitor test sample. Phase-retrieval was performed using an algorithm based on the Alvarez-Macovski model. Virtual monochromatic (VMI) and effective atomic number images were calculated after phase-retrieval. Results: Phase-retrieval results from the spectral data set show a distinct gray-level for each material with no residual phase-contrast fringes. Several representations of the phase-retrieved data are provided. The VMI is used to display an attenuation-equivalent image at a chosen display energy of 80keV, to provide good separation of materials with minimal noise. The effective atomic number image shows the material composition of the sample. Conclusions: Spectral photon-counting detector technology has already been shown to be compatible with spectral PBI, and there is a foreseeable need for robust phase-retrieval in high-resolution, spectral x-ray CT in the future. Our results demonstrate the feasibility of phase-retrieval for spectral PBI CT.

Dual Energy CT Physics-A Primer for the Emergency Radiologist.

Dual energy CT (DECT) refers to the acquisition of CT images at two energy spectra and can provide information about tissue composition beyond that obtainable by conventional CT. The attenuation of a photon beam varies depends on the atomic number and density of the attenuating material and the energy of the incoming photon beam. This differential attenuation of the beam at varying energy levels forms the basis of DECT imaging and enables separation of materials with different atomic numbers but similar CT attenuation. DECT can be used to detect and quantify materials like iodine, calcium, or uric acid. Several post-processing techniques are available to generate virtual non-contrast images, iodine maps, virtual mono-chromatic images, Mixed or weighted images and material specific images. Although initially the concept of dual energy CT was introduced in 1970, it is only over the past two decades that it has been extensively used in clinical practice owing to advances in CT hardware and post-processing capabilities. There are numerous applications of DECT in Emergency radiology including stroke imaging to differentiate intracranial hemorrhage and contrast staining, diagnosis of pulmonary embolism, characterization of incidentally detected renal and adrenal lesions, to reduce beam and metal hardening artifacts, in identification of uric acid renal stones and in the diagnosis of gout. This review article aims to provide the emergency radiologist with an overview of the physics and basic principles of dual energy CT. In addition, we discuss the types of DECT acquisition and post processing techniques including newer advances such as photon-counting CT followed by a brief discussion on the applications of DECT in Emergency radiology.

Photon-counting CT via interleaved/gapped spectral channels: Feasibility and imaging performance.

Compared to energy integration, photon-counting x-ray detection facilitates the spectral channelization (energy binning) in spectral CT and thus offers the opportunity to implement data acquisition via sophisticated schemes, for example, gapping or interleaving in spectral channels. In this article, we report our investigation of the performance of material decomposition based spectral imaging in photon-counting CT implemented in such data acquisition schemes, and their comparison with the benchmark scheme and other schemes without spectral gapping or interleaving. Using a deliberately designed anthropomorphic head phantom that mimics the intracranial soft tissues and bony structures, a simulation study is carried out with the focus on two-material decomposition based spectral imaging in photon-counting computed tomography (CT), under both ideal and realistic detector spectral responses. The projection data are acquired in four spectral channels, and then are sorted to implement the schemes of gapping ((ch1 , ch3 ); (ch2 , ch4 ); (ch1 , ch4 )) and interleaving ((ch1 , ch3 ) + (ch2 , ch4 ); (ch1 , ch4 ) + (ch2 , ch3 ); ((ch1 + ch3 ), (ch2 + ch4 )); ((ch1 + ch4 ), (ch2 + ch3 ))) in spectral channels, in addition to the benchmark scheme ((ch1 + ch2 ), (ch3 + ch4 )) and other conventional schemes (ch1 , ch2 ), (ch2 , ch3 ) and (ch3 , ch4 ), where ''ch'' denotes channel, ''+'' denote addition, and (·,·) the operation of material decomposition and image reconstruction. Using the contrast-to-noise ratio between targeted regions of interest as the figure of merit, we study the performance of spectral imaging (material specific and virtual monochromatic) associated with these spectral channelization schemes. Under ideal detector spectral response, the scheme (ch1 , ch4 ) outperforms the benchmark scheme ((ch1 + ch2 ), (ch3 + ch4 )) and others in gapped and/or interleaved spectral channelization in material specific imaging, while the interleaved scheme (ch1 , ch4 ) + (ch2 , ch3 ) performs the best in virtual monochromatic imaging. Notably, only about half x-ray dose is utilized in the scheme (ch1 , ch4 ) for image formation. Under realistic detector spectral response, the difference in imaging performance over all spectral channelization schemes diminishes, along with degradation in each scheme's individual performance. The results suggest that (i) different strategy in spectral channelization may have to be exercised in material specific imaging and virtual monochromatic imaging, respectively, and (ii) the spectral distortion in realistic detector's response due to charge-sharing, Compton scattering, and fluorescent escaping should be mitigated as much as possible. The spectral channelization schemes and associated imaging performance reported herein are novel and thus informative to the community, which may further the understanding of physical fundamentals and design principles for material decomposition based spectral imaging in photon-counting CT and other x-ray related imaging modalities.

Single-pass metal artifact reduction using a dual-layer flat panel detector.

Metal artifact remains a challenge in cone-beam CT images. Many image domain-based segmentation methods have been proposed for metal artifact reduction (MAR), which require two-pass reconstruction. Such methods first segment metal from a first-pass reconstruction and then forward-project the metal mask to identify them in projections. These methods work well in general but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Single-pass methods that detect metal in single-energy projections have also been developed, but often have imperfect metal detection that leads to residual artifacts. In this work, we develop a MAR method using a dual-layer (DL) flat panel detector, which improves performance for single-pass reconstruction. In this work, we directly detect metal objects in projections using dual-energy (DE) imaging that generates material-specific images (e.g., soft tissue and bone), where the metal stands out in bone images when nonuniform soft tissue background is removed. Metal is detected via simple thresholding, and entropy filtration is further applied to remove false-positive detections. A DL detector provides DE images with superior temporal and spatial registration and was used to perform the task. Scatter correction was first performed on DE raw projections to improve the accuracy of material decomposition. One phantom mimicking a liver biopsy setup and a cadaver head were used to evaluate the metal reduction performance of the proposed method and compared with that of a standard two-pass reconstruction, a previously published sinogram-based method using a Markov random field (MRF) model, and a single-pass projection-domain method using single-energy imaging. The phantom has a liver steering setup placed in a hollow chest phantom, with embedded metal and a biopsy needle crossing the phantom boundary. The cadaver head has dental fillings and a metal tag attached to its surface. The identified metal regions in each projection were corrected by interpolation using surrounding pixels, and the images were reconstructed using filtered backprojection. Our current approach removes metal from the projections, which is robust to FOV truncation during imaging acquisition. In case of FOV truncation, the method outperformed the two-pass reconstruction method. The proposed method using DE renders better accuracy in metal segmentation than the MRF method and single-energy method, which were prone to false-positive errors that cause additional streaks. For the liver steering phantom, the average spatial nonuniformity was reduced from 0.127 in uncorrected images to 0.086 using a standard two-pass reconstruction and to 0.077 using the proposed method. For the cadaver head, the average standard deviation within selected soft tissue regions ( ) was reduced from 209.1 HU in uncorrected images to 69.1 HU using a standard two-pass reconstruction and to 46.8 HU using our proposed method. The proposed method reduced the processing time by 31% as compared with the two-pass method. We proposed a MAR method that directly detects metal in the projection domain using DE imaging, which is robust to truncation and superior to that of single-energy imaging. The method requires only a single-pass reconstruction that substantially reduces processing time compared with the standard two-pass metal reduction method.

Reflection terahertz time-domain spectroscopy for imaging and identifying concealed interfaces in insulated systems.

Polarization-resolved reflection spectroscopy enabled by a custom ultrahigh molecular weight polymer dove prism is used to identify spectral characteristics that manifest in the time domain at terahertz frequencies, which shows promise in combination with terahertz time-domain reference-free reflection imaging at a concealed interface. The method is used to produce 100×100 pixel images of an interface concealed by an ultrahigh molecular weight polymer using TE and TM polarized terahertz fields. The construction of material-specific image filters is guided by a theoretical reflection model by identifying reflection pulse characteristics unique to an interface. Application of these filters to the collected terahertz images distinguishes materials at the interface including the concealing plastic, water, air, glass, and metal that are correctly identified and imaged.

Energy-integrating-detector multi-energy CT: Implementation and a phantom study.

Multi-energy computed tomography (MECT) has a great potential to enable many novel clinical applications such as simultaneous multi-contrast imaging. The purpose of this study was to implement triple-beam MECT on a traditional energy-integrating-detector (EID) CT platform (EID-MECT). This was accomplished by mounting a z-axis split-filter (0.05mm Au, 0.6mm Sn) on Tube A of a dual-source EID CT scanner. With the two split x-ray beams from Tube A and the third beam from Tube B, three beams with different x-ray spectra can be simultaneously acquired. With Tube B operated at 70 or 80kV and Tube A at 120 or 140kV, four different triple-beam configurations were calibrated for MECT measurements: 70/Au120/Sn120, 80/Au120/Sn120, 70/Au140/Sn140, and 80/Au140/Sn140 kV. Iodine (I), gadolinium (Gd), bismuth (Bi) samples, and their mixtures were prepared for 2 three-material-decomposition tasks and 1 four-material-decomposition task. For each task, samples were placed in a water phantom and scanned using each of the four triple-beam configurations. For comparison, the same phantom was also scanned using three other dual-energy CT (DECT) or MECT technologies: twin-beam DECT (TB-DECT), dual-source DECT (DS-DECT), and photon-counting-detector CT (PCD-CT), all with optimal x-ray spectrum settings and at equal volume CT dose index (CTDIvol). The phantom for four-material decomposition (I/Gd/Bi/Water imaging) was scanned using the PCD-CT only (140kV with 25, 50, 75, and 90keV). Image-based material decomposition was performed to acquire material-specific images, on which the mean basis material concentrations and noise levels were measured and compared across all triple-beam configurations in EID-MECT and various DECT/MECT systems. The optimal triple-beam configuration was task-dependent with 70/Au120/Sn120, 70/Au140/Sn140, and 70/Au120/Sn120kV for I/Gd/Water, I/Bi/Water, and I/Gd/Bi/Water material decomposition tasks, respectively. At equal radiation dose level, EID-MECT provided comparable or better quantification accuracy in material-specific images for all three material decomposition tasks, compared to EID-based DECT and PCD-CT systems. In terms of noise level comparison, EID-MECT-derived material-specific images showed lower noise levels than TB-DECT and DS-DECT, but slightly higher than that from PCD-CT in I/Gd/Water imaging. For I/Bi/Water imaging, EID-MECT showed a comparable noise level to DS-DECT, and a much lower noise level than TB-DECT and PCD-CT in all material-specific images. For the four-material decomposition task involving I/Gd/Bi/Water, the bismuth-specific image derived from EID-MECT was slightly noisier, but both iodine- and gadolinium-specific images showed much lower noise levels in comparison to PCD-CT. For the first time, an EID-based MECT system that can simultaneously acquire three x-ray spectra measurements was implemented on a clinical scanner, which demonstrated comparable or better imaging performance than existing DECT and MECT systems.

A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks.

Dual-energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material-specific images are affected by magnified noise and beam-hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge-preserving images. In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high- and low-energy bins are sent to two generators separately, and each generator synthesizes one material-specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft-tissue regions are near the ground truth, and the error of material densities is lower than 3mg/ml. The results from XCAT phantom show that the material-specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning-based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly-Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly-Net, the root-mean-square error (RMSE) of soft-tissue images generated by the DIWGAN decreased by 0.01g/ml, whereas the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the soft-tissue images reached 31.43dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterfly-Net, respectively. Furthermore, the performance of the mouse data indicates the potential of the proposed material decomposition method in real scanned data. A DECT material decomposition method based on deep learning is proposed, and the relationship between reconstructed and material-specific images is mapped by training the DIWGAN model. Results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing noise and beam-hardening artifacts.

Erratum to Medical Physics article "Principles and applications of multienergy CT: Report of AAPM Task Group 291"

In AAPM Report 291, “Principles and applications of multienergy CT: Report of AAPM Task Group 291,”1 scientists from Canon Medical Systems Corporation, GE Healthcare Technologies, Philips Healthcare, and Siemens Healthcare GmbH participated as authors and contributed sections to describe, in general, the technology used in their company’s dual- or multi-energy CT systems. Specific scanner models were sometimes mentioned to provide examples of the overall approach. The general statements made about the approach may not, however, apply to the specific scanner model used as an example. The intermingling of the general and specific statements is regrettable, as it is ready source of potential confusion. Readers should view each section as a description of an overall dual- or multi-energy CT approach, even if a specific scanner model is mentioned as an example. Readers should refer to manufacturer specifications and the published literature to assess the specifications and performance of a specific CT system. A numerical error exists on page e895 in the second paragraph of the limitations section for kV switching. The statement “… so the system software currently prevents the use of gantry rotation times of less than approximately 500 ms, which may limit the quality of cardiac examinations,” implies that a minimum 500 ms rotation time applies to the scanner used as an example in the kV switching setting (GE Discovery CT750 HD). However, this value (500 msec) does not reflect the minimum rotation time on the GE Discovery CT750 HD. Rather, from the earliest publications on the use of this specific CT scanner model for dual-energy cardiac imaging, the minimum rotation time of 350 ms was used.2, 3 The second and third paragraphs in this same section on the limitations of kV switching reiterate the physics principles discussed on page e894, at the top of the second. Namely, as first demonstrated in 1979 by Kelcz et al., decreased spectral separation causes increased noise in decomposed material-specific images.4 In 2009, Primak et al. demonstrated that tube-specific filtration can increase spectral separation and decrease image noise in material decomposed images using a commercial dual-source CT system.5 While spectral separation is indeed an important determinant of the underlying noise properties of the acquired data, noise reduction methods can be applied such that the material decomposition calculations performed using kV switching (or other non-dual-source approaches) can also yield accurate quantification of iodine concentration.6