Energy Window Optimization for X-Ray K-Edge Tomographic Imaging

Gadolinium-based coronary CT angiography on a clinical photon-counting-detector system: a dynamic circulating phantom study

BACKGROUND

Coronary computed tomography angiography (CCTA) offers non-invasive diagnostics of the coronary arteries. Vessel evaluation requires the administration of intravenous contrast. The purpose of this study was to evaluate the utility of gadolinium-based contrast agent (GBCA) as an alternative to iodinated contrast for CCTA on a first-generation clinical dual-source photon-counting-detector (PCD)-CT system.

METHODS

A dynamic circulating phantom containing a three-dimensional-printed model of the thoracic aorta and the coronary arteries were used to evaluate injection protocols using gadopentetate dimeglumine at 50%, 100%, 150%, and 200% of the maximum approved clinical dose (0.3 mmol/kg). Virtual monoenergetic image (VMI) reconstructions ranging from 40 keV to 100 keV with 5 keV increments were generated on a PCD-CT. Contrast-to-noise ratio (CNR) was calculated from attenuations measured in the aorta and coronary arteries and noise measured in the background tissue. Attenuation of at least 350 HU was deemed as diagnostic.

RESULTS

The highest coronary attenuation (441 ± 23 HU, mean ± standard deviation) and CNR (29.5 ± 1.5) was achieved at 40 keV and at the highest GBCA dose (200%). There was a systematic decline of attenuation and CNR with higher keV reconstructions and lower GBCA doses. Only reconstructions at 40 and 45 keV at 200% and 40 keV at 150% GBCA dose demonstrated sufficient attenuation above 350 HU.

CONCLUSION

Current PCD-CT protocols and settings are unsuitable for the use of GBCA for CCTA at clinically approved doses. Future advances to the PCD-CT system including a 4-threshold mode, as well as multi-material decomposition may add new opportunities for k-edge imaging of GBCA.

RELEVANCE STATEMENT

Patients allergic to iodine-based contrast media and the future of multicontrast CT examinations would benefit greatly from alternative contrast media, but the utility of GBCA for coronary photon-counting-dector-CT angiography remains limited without further optimization of protocols and scanner settings.

KEY POINTS

GBCA-enhanced coronary PCD-CT angiography is not feasible at clinically approved doses. GBCAs have potential applications for the visualization of larger vessels, such as the aorta, on PCD-CT angiography. Higher GBCA doses and lower keV reconstructions achieved higher attenuation values and CNR.

Spectral computed tomography angiography using a gadolinium-based contrast agent for imaging of pathologies of the aorta

OBJECTIVES

Especially patients with aortic aneurysms and multiple computed tomography angiographies (CTA) might show medical conditions which oppose the use of iodine-based contrast agents. CTA using monoenergetic reconstructions from dual layer CT and gadolinium (Gd-)based contrast agents might be a feasible alternative in these patients. Therefore, the purpose of this study was to evaluate the feasibility of clinical spectral CTA with a Gd-based contrast agent in patients with aortic aneurysms.

METHODS

Twenty-one consecutive scans in 15 patients with and without endovascular aneurysm repair showing contraindications for iodine-based contrast agents were examined using clinical routine doses (0.2 mmol/kg) of Gd-based contrast agent with spectral CT. Monoenergetic reconstructions of the spectral data set were computed.

RESULTS

There was a significant increase in the intravascular attenuation of the aorta between pre- and post-contrast images for the MonoE40 images in the thoracic and the abdominal aorta (p < 0.001 for both). Additionally, the ratio between pre- and post-contrast images was significantly higher in the MonoE40 images as compared to the conventional images with a factor of 6.5 ± 4.5 vs. 2.4 ± 0.5 in the thoracic aorta (p = 0.003) and 4.1 ± 1.8 vs. 1.9 ± 0.5 in the abdominal aorta (p < 0.001).

CONCLUSIONS

To conclude, our study showed that Gd-CTA is a valid and reliable alternative for diagnostic imaging of the aorta for clinical applications. Monoenergetic reconstructions of computed tomography angiographies using gadolinium based contrast agents may be a useful alternative in patients with aortic aneurysms and contraindications for iodine based contrast agents.

Dual-Source Photon-Counting Computed Tomography-Part III: Clinical Overview of Vascular Applications beyond Cardiac and Neuro Imaging

Photon-counting computed tomography (PCCT) is an emerging technology that is expected to radically change clinical CT imaging. PCCT offers several advantages over conventional CT, which can be combined to improve and expand the diagnostic possibilities of CT angiography. After a brief description of the PCCT technology and its main advantages we will discuss the new opportunities brought about by PCCT in the field of vascular imaging, while addressing promising future clinical scenarios.

Window-Based Energy Selecting X-ray Imaging and Charge Sharing in Cadmium Zinc Telluride Linear Array Detectors for Contaminant Detection

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (<3 keV) resolution, allow spectral X-ray imaging with interesting image quality improvements. The effects of charge sharing and energy-resolved techniques on contrast-to-noise ratio (CNR) enhancements are investigated. The benefits of a new energy-resolved X-ray imaging approach, termed window-based energy selecting, in the detection of low- and high-density contaminants are also shown.

Nanophosphor-Based Contrast Agents for Spectral X-ray Imaging

Lanthanide-based nanophosphors (NPhs) are herein developed as contrast agents for spectral X-ray imaging, highlighting the chemical, macromolecular and structural differences derived from ligand exchange on computed tomography (CT) and solvent dispersibility. Taking advantage of the ability of spectral X-ray imaging with photon-counting detectors to perform image acquisition, analysis, and processing at different energy windows (bins), enhanced signal of our K-edge materials was derived, improving sensitivity of CT imaging, and differentiation between water, tumor-mimic phantoms, and contrast materials. Our results indicate that the most effective of our oleic acid-stabilized K-edge nanoparticles can achieve 2-4x higher contrast than the examined iodinated molecules, making them suitable for deep tissue imaging of tissues or tumors. On the other hand, ligand exchange yielding poly(acrylic acid)-stabilized K-edge nanoparticles allows for high dispersibility and homogeneity in water, but with a lower contrast due to the high density of the polymer grafted, unless further engineering is probed. This is the first well-defined study that manages to correlate NPh grafting density with CT numbers and water dispersibility, laying the groundwork for the development of the next generation CT-guided diagnostic and/or theranostic materials.

K-edge-based interior tomography

Interior tomography reconstructs a region of interest using truncated projection data, but it is subject to the ill-posedness of interior tomography. With the photon-counting detector, K-edge imaging uses data in the low and high energy bins around the K-edge of a contrast agent, and can faithfully recover true image contrast for improved diagnosis. The purpose of this paper is to reconstruct a region of interest inside a patient assuming the existence of a known K-edge material. In this case, there is a significant difference in x-ray attenuation around the K-edge, but these attenuation coefficients are inter-related to guide updating an intermediate reconstruction until a stopping criterion is satisfied. In our study, new interior tomography algorithms were developed without any major computational overhead, and several phantoms were used to validate the algorithms. The proposed methods are advantageous relative to the existing interior tomography algorithms, because of the available spectral information in the form of a known K-edge material.

Experimental study to optimize configurations of PCD Spectral CT

BACKGROUND

High dose efficiency of photon counting detector based spectral CT (PCD-SCT) and its value in some clinical diagnosis have been well acknowledged. However, it has not been widely adopted in practical use for medical diagnosis and security inspection.

OBJECTIVE

To evaluate the influence on PCD-SCT from multiple aspects including the number of energy channels, k-edge materials, energy thresholding, basis functions in spectral information decomposition, and the combined optimal setting for these parameters and configurations.

METHODS

Basis material decomposition after spatial reconstruction is applied for PCD-SCT. A "one-step" synthesis method, merging decomposition with synthesis, is proposed to obtain virtual monochromatic images. An I-RMSE is computed using the bias part of I-RMSE to describe the difference of a synthesized signal from ground truth and the standard deviation part of I-RMSE to express the noise level. In addition, virtual monochromatic images commonly used in the medical area are also synthesized. Both numerical simulations and practical experiments are conducted for validation.

RESULTS

Results indicated that the I-RMSE for matters significantly reduced with an increased number of energy channels compared with dual-energy channel. The maximum reduction is 6% for triple-, 18% for quadruple-and 24% for quintuple-energy, respectively. However, the improvement is not linear, and also slows down after the number of energy channels reaches a certain number. Contrast agents of high concentration can introduce up to 50% error to surrounding matters. Moreover, different energy partitions influence the total error, which demonstrates the necessity of energy threshold optimization. Last, the optimal basis-material combination varies according to targeted imaging matters and the interested monochromatic energies.

CONCLUSIONS

Gain from more energy channels could be significant with the increase of energy channel number. Introduction of contrast agents in scanned objects will increase overall error in spectral CT imaging. Energy thresholding optimization is beneficial for information recovery. Moreover, the choice of basis materials could also be important to obtain low noise results. With these studies of the effect from various configurations for PCD-SCT, one may optimize the configuration of PCD-SCT accordingly.

Local Smoothness Constrained Nonnegative Matrix Factorization with Nonlinear Convergence Rate for Spectral Decomposition

With the development of the detection technology using multispectra sensors, spectral decomposition (SD) attracts more and more attention in the biomedical signal processing and image processing. In this paper, a local smoothness constrained nonnegative matrix factorization (NMF) with nonlinear convergence rate (NMF-NCR) is proposed to solve SD problem and our contributions are as follows. First, it proves that the gradients of the cost function with respect to each variable matrix are Lipschitz continuous. Then, a proximal function is constructed for optimizing the cost function. As a result, our method can achieve an NCR much faster than the traditional methods. Simulations show the advantage in solving SD of our algorithm over the compared methods.

Model and reconstruction of a K-edge contrast agent distribution with an X-ray photon-counting detector

Contrast-enhanced computed tomography (CECT) helps enhance the visibility for tumor imaging. When a high-Z contrast agent interacts with X-rays across its K-edge, X-ray photoelectric absorption would experience a sudden increment, resulting in a significant difference of the X-ray transmission intensity between the left and right energy windows of the K-edge. Using photon-counting detectors, the X-ray intensity data in the left and right windows of the K-edge can be measured simultaneously. The differential information of the two kinds of intensity data reflects the contrast-agent concentration distribution. K-edge differences between various matters allow opportunities for the identification of contrast agents in biomedical applications. In this paper, a general radon transform is established to link the contrast-agent concentration to X-ray intensity measurement data. An iterative algorithm is proposed to reconstruct a contrast-agent distribution and tissue attenuation background simultaneously. Comprehensive numerical simulations are performed to demonstrate the merits of the proposed method over the existing K-edge imaging methods. Our results show that the proposed method accurately quantifies a distribution of a contrast agent, optimizing the contrast-to-noise ratio at a high dose efficiency.

Spectral CT Reconstruction with Image Sparsity and Spectral Mean

Photon-counting detectors can acquire x-ray intensity data in different energy bins. The signal to noise ratio of resultant raw data in each energy bin is generally low due to the narrow bin width and quantum noise. To address this problem, here we propose an image reconstruction approach for spectral CT to simultaneously reconstructs x-ray attenuation coefficients in all the energy bins. Because the measured spectral data are highly correlated among the x-ray energy bins, the intra-image sparsity and inter-image similarity are important prior acknowledge for image reconstruction. Inspired by this observation, the total variation (TV) and spectral mean (SM) measures are combined to improve the quality of reconstructed images. For this purpose, a linear mapping function is used to minimalize image differences between energy bins. The split Bregman technique is applied to perform image reconstruction. Our numerical and experimental results show that the proposed algorithms outperform competing iterative algorithms in this context.

Feature Learning Based Random Walk for Liver Segmentation

Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods.

High-kVp Assisted Metal Artifact Reduction for X-ray Computed Tomography

In X-ray computed tomography (CT), the presence of metallic parts in patients causes serious artifacts and degrades image quality. Many algorithms were published for metal artifact reduction (MAR) over the past decades with various degrees of success but without a perfect solution. Some MAR algorithms are based on the assumption that metal artifacts are due only to strong beam hardening and may fail in the case of serious photon starvation. Iterative methods handle photon starvation by discarding or underweighting corrupted data, but the results are not always stable and they come with high computational cost. In this paper, we propose a high-kVp-assisted CT scan mode combining a standard CT scan with a few projection views at a high-kVp value to obtain critical projection information near the metal parts. This method only requires minor hardware modifications on a modern CT scanner. Two MAR algorithms are proposed: dual-energy normalized MAR (DNMAR) and high-energy embedded MAR (HEMAR), aiming at situations without and with photon starvation respectively. Simulation results obtained with the CT simulator CatSim demonstrate that the proposed DNMAR and HEMAR methods can eliminate metal artifacts effectively.