Measurement of electron density in dual-energy x-ray CT with monochromatic x rays and evaluation of its accuracy

MB-DECTNet: a model-based unrolling network for accurate 3D dual-energy CT reconstruction from clinically acquired helical scans

Objective.Over the past several decades, dual-energy CT (DECT) imaging has seen significant advancements due to its ability to distinguish between materials. DECT statistical iterative reconstruction (SIR) has exhibited potential for noise reduction and enhanced accuracy. However, its slow convergence and substantial computational demands render the elapsed time for 3D DECT SIR often clinically unacceptable. The objective of this study is to accelerate 3D DECT SIR while maintaining subpercentage or near-subpercentage accuracy.Approach.We incorporate DECT SIR into a deep-learning model-based unrolling network for 3D DECT reconstruction (MB-DECTNet), which can be trained end-to-end. This deep learning-based approach is designed to learn shortcuts between initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet comprises multiple stacked update blocks, each containing a data consistency layer (DC) and a spatial mixer layer, with the DC layer functioning as a one-step update from any traditional iterative algorithm.Main results.The quantitative results indicate that our proposed MB-DECTNet surpasses both the traditional image-domain technique (MB-DECTNet reduces average bias by a factor of 10) and a pure deep learning method (MB-DECTNet reduces average bias by a factor of 8.8), offering the potential for accurate attenuation coefficient estimation, akin to traditional statistical algorithms, but with considerably reduced computational costs. This approach achieves 0.13% bias and 1.92% mean absolute error and reconstructs a full image of a head in less than 12 min. Additionally, we show that the MB-DECTNet output can serve as an initializer for DECT SIR, leading to further improvements in results.Significance.This study presents a model-based deep unrolling network for accurate 3D DECT reconstruction, achieving subpercentage error in estimating virtual monoenergetic images for a full head at 60 and 150 keV in 30 min, representing a 40-fold speedup compared to traditional approaches. These findings have significant implications for accelerating DECT SIR and making it more clinically feasible.

Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction

Objective.Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.Approach.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Main results.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.Significance.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Diagnosis of Pulmonary Embolism in Unenhanced Dual Energy CT Using an Electron Density Image

Dual-energy computed tomography (CT) is a promising tool, providing both anatomical information and material properties. Using spectral information such as iodine mapping and virtual monoenergetic reconstruction, dual-energy CT showed added value over pulmonary CT angiography in the diagnosis of pulmonary embolism. However, the role of non-contrast-enhanced dual energy CT in pulmonary embolism has never been reported. Here, we report a case of acute pulmonary embolism detected on an electron density image from an unenhanced dual-energy CT using a dual-layer detector system.

Proton stopping power prediction based on dual-energy CT-generated virtual monoenergetic images

PURPOSE

The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images.

MATERIALS AND METHODS

A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140 keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated.

RESULTS

The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone.

CONCLUSIONS

The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.

Technical Principles of Dual-Energy Cone Beam Computed Tomography and Clinical Applications for Radiation Therapy

PURPOSE

Medical imaging is an indispensable tool in radiotherapy for dose planning, image guidance and treatment monitoring. Cone beam CT (CBCT) is a low dose imaging technique with high spatial resolution capability as a direct by-product of using flat-panel detectors. However, certain issues such as x-ray scatter, beam hardening and other artifacts limit its utility to the verification of patient positioning using image-guided radiotherapy.

METHODS AND MATERIALS

Dual-energy (DE)-CBCT has recently demonstrated promise as an improved tool for tumor visualization in benchtop applications. It has the potential to improve soft-tissue contrast and reduce artifacts caused by beam hardening and metal. In this review, the practical aspects of developing a DE-CBCT based clinical and technical workflow are presented based on existing DE-CBCT literature and concepts adapted from the well-established library of work in DE-CT. Furthermore, the potential applications of DE-CBCT on its future role in radiotherapy are discussed.

RESULTS AND CONCLUSIONS

Based on current literature and an investigation of future applications, there is a clear potential for DE-CBCT technologies to be incorporated into radiotherapy. The applications of DE-CBCT include (but are not limited to): adaptive radiotherapy, brachytherapy, proton therapy, radiomics and theranostics.

K-edge Subtraction Computed Tomography with a Compact Synchrotron X-ray Source

In clinical diagnosis, X-ray computed tomography (CT) is one of the most important imaging techniques. Yet, this method lacks the ability to differentiate similarly absorbing substances like commonly used iodine contrast agent and calcium which is typically seen in calcifications, kidney stones and bones. K-edge subtraction (KES) imaging can help distinguish these materials by subtracting two CT scans recorded at different X-ray energies. So far, this method mostly relies on monochromatic X-rays produced at large synchrotron facilities. Here, we present the first proof-of-principle experiment of a filter-based KES CT method performed at a compact synchrotron X-ray source based on inverse-Compton scattering, the Munich Compact Light Source (MuCLS). It is shown that iodine contrast agent and calcium can be clearly separated to provide CT volumes only showing one of the two materials. These results demonstrate that KES CT at a compact synchrotron source can become an important tool in pre-clinical research.

Dual-layer spectral computed tomography: measuring relative electron density

Background

X-ray and particle radiation therapy planning requires accurate estimation of local electron density within the patient body to calculate dose delivery to tumour regions. We evaluate the feasibility and accuracy of electron density measurement using dual-layer computed tomography (DLCT), a recently introduced dual-energy CT technique.

Methods

Two calibration phantoms were scanned with DLCT and virtual monoenergetic images (VMIs) at 50 keV and 200 keV were generated. We investigated two approaches to obtain relative electron densities from these VMIs: to fit an analytic interaction cross-sectional model and to empirically calibrate a conversion function with one of the phantoms. Knowledge of the emitted x-ray spectrum was not required for the presented work.

Results

The results from both methods were highly correlated to the nominal values (R > 0.999). Except for the water and lung inserts, the error was within 1.79% (average 1.53%) for the cross-sectional model and 1.61% (average 0.87%) for the calibrated conversion. Different radiation doses did not have a significant influence on the measurement (p = 0.348, 0.167), suggesting that the methods are reproducible. Further, we applied these methods to routine clinical data.

Conclusions

Our study shows a high validity of electron density estimation based on DLCT, which has potential to improve the procedure and accuracy of measuring electron density in clinical practice.

Principles and Clinical Application of Dual-energy Computed Tomography in the Evaluation of Cerebrovascular Disease

Dual-energy computed tomography (DECT) simultaneously acquires images at two X-ray energy levels, at both high- and low-peak voltages (kVp). The material attenuation difference obtained from the two X-ray energies can be processed by software to analyze material decomposition and to create additional image datasets, namely, virtual noncontrast, virtual contrast also known as iodine overlay, and bone/calcium subtraction images. DECT has a vast array of clinical applications in imaging cerebrovascular diseases, which includes: (1) Identification of active extravasation of iodinated contrast in various types of intracranial hemorrhage; (2) differentiation between hemorrhagic transformation and iodine staining in acute ischemic stroke following diagnostic and/or therapeutic catheter angiography; (3) identification of culprit lesions in intra-axial hemorrhage; (4) calcium subtraction from atheromatous plaque for the assessment of plaque morphology and improved quantification of luminal stenosis; (5) bone subtraction to improve the depiction of vascular anatomy with more clarity, especially at the skull base; (6) metal artifact reduction utilizing virtual monoenergetic reconstructions for improved luminal assessment postaneurysm coiling or clipping. We discuss the physical principles of DECT and review the clinical applications of DECT for the evaluation of cerebrovascular diseases.

Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?

OBJECTIVE

Optical and radiologic imaging are commonly used in preclinical research, and research into combined instruments for human applications is showing promise. The purpose of this article is to outline the fundamental limitations and advantages and to review the available systems. The emerging developments and future potential will be summarized.

CONCLUSION

Integration of hybrid systems is now routine at the preclinical level and appears in the form of specialized packages in which performance varies considerably. The synergy is commonly focused on using spatial localization from radiographs to provide structural data for spectroscopy; however, applications also exist in which the spectroscopy informs the use of radiologic imaging. Examples of clinical systems under research and development are shown.