The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies.
Gadolinium-based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network.The proposed workflow consists of two sequential networks: (1) a retina U-Net, which is first trained to derive semantic features from the non-contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U-Net to take the concatenation of the semantic feature maps and non-contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non-contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five-fold cross-validation and 169 patients for hold-out test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis.The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the hold-out test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five-fold cross-validation and hold-out test showed that the calculated metrics can be significantly enhanced (p-values ≤ 0.002) with the tumor contour supervision in network training.The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non-contrast enhanced MR images when the network training included tumor contours. These results suggest that it may be possible to minimize the use of GBCAs in cranial MR imaging studies.
Purpose In the clinic, computed tomography ( CT ) has evolved into an essential modality for diagnostic imaging by multidetector row CT ( MDCT ) and image guided intervention by cone beam CT ( CBCT ). Recognizing the increasing importance of axial MDCT / CBCT in clinical and preclinical applications, and the existence of CB artifacts in MDCT / CBCT images, we provide a review of CB artifacts’ root causes, rendering mechanisms and morphology, and possible solutions for elimination and/or reduction of the artifacts. Methods By examining the null space in Radon and Fourier domain, the root cause of CB artifacts (i.e., data insufficiency) in axial MDCT / CBCT is analytically investigated, followed by a review of the data sufficiency conditions and the “circle +” source trajectories. The rendering mechanisms and morphology of CB artifacts in axial MDCT / CBCT and their special cases (e.g., half/short scan and full scan with latitudinally displaced detector) are then analyzed, followed by a survey of the potential solutions to suppress the artifacts. The phenomenon of imaged zone indention and its variation over FBP , BPF / DBPF , two‐pass and iterative CB reconstruction algorithms and/or schemes are discussed in detail. Results An interdomain examination of the null space provides an insightful understanding of the root cause of CB artifacts in axial MDCT / CBCT . The decomposition of CB artifacts rendering mechanisms facilitates understanding of the artifacts’ behavior under different conditions and the potential solutions to suppress them. An inspection of the imaged zone intention phenomenon provides guidance on the design and implementation of CB image reconstruction algorithms and schemes for CB artifacts suppression in axial MDCT / CBCT . Conclusions With increasing importance of axial MDCT / CBCT in clinical and preclinical applications, this review article can update the community with in‐depth information and clarification on the latest progress in dealing with CB artifacts and thus increase clinical/preclinical confidence.
While energy-integration spectral CT with the capability of material decomposition has been providing added value to diagnostic CT imaging in the clinic, photon-counting spectral CT is gaining momentum in research and development, with the potential of overcoming more clinically relevant challenges. In practice, the photon-counting spectral CT provides the opportunity for principal component analysis to effectively extract information from the raw data. However, the principal component analysis in spectral CT may suffer from high noise induced by photon starvation, especially in energy bins at the high energy end. Via phantom and small animal studies, we investigate the feasibility of principal component analysis in photon-counting spectral CT and the benefit that can be offered by de-noising with the Content-Oriented Sparse Representation method.
Implemented with energy-integration x-ray detector, dual energy spectral CT has been playing increasingly important roles in the clinic for diagnostic imaging. Encouraged by the clinical value added by dual energy spectral CT in oncological, cardiovascular and neurovascular applications, the technological community is investing more resource and effort on photon-counting spectral CT that is anticipated to outperform its energy-integration counterpart in many advanced clinical applications. Based on the preliminary data of phantom and animal studies acquired using a prototype photoncounting spectral CT system, we revisit the underlying physics, mathematics, dimensionality of material space and selection of basis materials for spectral imaging. Focused on three-material decomposition for virtual monochromatic imaging/analysis, we investigate the feasibility and performance of spectral imaging without contrast agents in photoncounting CT, along with an in-depth analysis of the pros and cons of three-material decomposition over two-material decomposition for spectral imaging.
Purpose: Optimization‐based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi‐cycle cardiovascular imaging, we investigate the performance of optimization‐based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub‐scan I. Another 82 views are sorted out in a similar manner to form sub‐scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub‐scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization‐based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization‐based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization‐based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization‐based image reconstruction can save radiation dose up to 12‐fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH‐12‐1‐0138 (DoD), Sinovision Technologies.
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.
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water (ρe) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient L0-norm minimization, for Z and ρe maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies.
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