Multi-energy Images Research Articles

Simultaneous iodine and barium imaging with photon-counting CT

Photon-counting computed tomography (PCCT) could potentially offer significant improvements in image quality over conventional CT. This study leverages PCCT technology to achieve simultaneous multi-contrast imaging of barium (Z=56) and iodine (Z=53), addressing the limitations of conventional CT in differentiating materials with similar atomic numbers. Using a bench-top PCCT system with a cadmium zinc telluride (CZT) detector, experiments were conducted with various phantom setups and 1-5\% contrast agent concentrations and in a biological sample. Energy thresholds were adjusted to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed with K-edge subtraction and principal component analysis (PCA) techniques, demonstrating clear differentiation and accurate quantification of contrast agents in phantom combinations and the biological sample. The PCCT system successfully differentiated and quantified barium and iodine, with high correlations ($R^2 \approx 1$) between true and reconstructed concentrations. PCA outperformed K-edge subtraction by providing a superior capability to differentiate barium and iodine, particularly when calcium was present in the scanned object. These findings highlight the potential of PCCT for reliable, detailed imaging in clinical and research applications, particularly for contrast agents with close atomic numbers.

Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study

Rationale and ObjectivesTo evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). MethodsThis study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training(n=114) and internal test set (n=50) in a 7:3 ratio, with center 2 serving as the external test set (n=49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual monoenergetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish ten uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared with the clinical-radiological model to test its diagnostic validity. ResultsThe independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65keV significantly outperformed AUC = 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC = 0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC = 0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. ConclusionsThe combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

Standardizing technical parameters and terms for abdominopelvic photon-counting CT: laying the groundwork for innovation and evidence sharing.

Photon-counting detector CT (PCD-CT) is a new technology that has multiple diagnostic benefits including increased spatial resolution, iodine signal, and radiation dose efficiency, as well as multi-energy imaging capability, but which also has unique challenges in abdominal imaging. The purpose of this work is to summarize key features, technical parameters, and terms, which are common amongst current abdominopelvic PCD-CT systems and to propose standardized terminology (where none exists). In addition, user-selectable protocol parameters are highlighted to facilitate both scientific evaluation and early clinical adoption. Unique features of PCD-CT systems include photon-counting detectors themselves, energy thresholds and bins, and tube potential considerations for preserved spectral separation. Key parameters for describing different PCD-CT systems are reviewed and explained. While PCD-CT can generate multi-energy images like dual-energy CT, there are new types of images such as threshold images, energy bin images, and special spectral images. The standardized terms and concepts herein build upon prior interdisciplinary consensus and have been endorsed by the newly created Society of Abdominal Radiology Photon-counting CT Emerging Technology Commission.

Sparse-View Spectral CT Reconstruction and Material Decomposition Based on Multi-Channel SGM.

In medical applications, the diffusion of contrast agents in tissue can reflect the physiological function of organisms, so it is valuable to quantify the distribution and content of contrast agents in the body over a period. Spectral CT has the advantages of multi-energy projection acquisition and material decomposition, which can quantify K-edge contrast agents. However, multiple repetitive spectral CT scans can cause excessive radiation doses. Sparse-view scanning is commonly used to reduce dose and scan time, but its reconstructed images are usually accompanied by streaking artifacts, which leads to inaccurate quantification of the contrast agents. To solve this problem, an unsupervised sparse-view spectral CT reconstruction and material decomposition algorithm based on the multi-channel score-based generative model (SGM) is proposed in this paper. First, multi-energy images and tissue images are used as multi-channel input data for SGM training. Secondly, the organism is multiply scanned in sparse views, and the trained SGM is utilized to generate multi-energy images and tissue images driven by sparse-view projections. After that, a material decomposition algorithm using tissue images generated by SGM as prior images for solving contrast agent images is established. Finally, the distribution and content of the contrast agents are obtained. The comparison and evaluation of this method are given in this paper, and a series of mouse scanning experiments are carried out to verify the effectiveness of the method.

Technical Note: Assessment of Pulse Pileup on Single-Energy and Multienergy Images From a Clinical Photon-Counting Detector Computed Tomography.

Pulse pileup effects occur when pulses occur so close together that they fall on top of one another, resulting in count loss and errors in energy thresholding. To date, there has been little work systematically detailing the quantitative effects of pulse pileup on material decomposition accuracy for photon-counting detector (PCD) computed tomography (CT). Our aim in this work was to quantify the effects of pulse pileup on single-energy and multienergy CT images, including low-energy bin (BL), high-energy bin (BH), iodine map, and virtual noncontrast images from a commercial PCD-CT. Scans of a 20-cm diameter multienergy CT phantom with 10 solid inserts were acquired at a fixed tube potential as the tube current was varied across the available range. Four types of images (BL, BH, iodine map, and virtual noncontrast) were reconstructed using an iterative reconstruction algorithm at strength 2, a quantitative reconstruction kernel (Qr40), 2-/1-mm slice thickness/increment, and a 210-mm field-of-view. The mean and standard deviation of CT numbers were recorded and the ratios of CT number between BL and BH images were calculated and plotted, along with noise versus tube current and noise × versus tube current. As tube current was increased, the range of variations in CT numbers was less than 13.4 HU for all inserts and image types evaluated. Noise × versus tube current showed a small positive slope equal to a noise increase from 100 mA of 10% at 500 mA and 15% at 900 mA compared with what would be expected if the slope was zero. Minimal impact on single-energy and multienergy CT numbers and noise performance was observed for the evaluated clinical PCD-CT system.

The first mobile photon-counting detector CT: the human images and technical performance study

Objective. The purpose of this study is to assess its human images and its unique capabilities such as the ‘on demand’ higher spatial resolution and multi-spectral imaging of photon-counting-detector (PCD)-CT. Approach. In this study, the FDA 510(k) cleared mobile PCD-CT (OmniTom Elite) was used. To this end, we imaged internationally certified CT phantoms and a human cadaver head to evaluate the feasibility of high resolution (HR) and multi-energy imaging. We also demonstrate the performance of PCD-CT via first-in-human imaging by scanning three human volunteers. Main results. At the 5 mm slice thickness, routinely used in diagnostic head CT, the first human PCD-CT images were diagnostically equivalent to the EID-CT scanner. The HR acquisition mode of PCD-CT achieved a resolution of 11 line-pairs (lp)/cm as compared to 7 lp cm−1 using the same kernel (posterior fossa-kernel) in the standard acquisition mode of EID-CT. For the quantitative multi-energy CT performance, the measured CT numbers in virtual mono-energetic images (VMI) of iodine inserts in the Gammex Multi-Energy CT phantom (model 1492, Sun Nuclear Corporation, USA) matched the manufacturer reference values with mean percent error of 3.25%. Multi-energy decomposition with PCD-CT demonstrated the separation and quantification of iodine, calcium, and water. Significance. PCD-CT can achieve multi-resolution acquisition modes without physically changing the CT detector. It can provide superior spatial resolution compared with the standard acquisition mode the conventional mobile EID-CT. Quantitative spectral capability of PCD-CT can provide accurate, simultaneous multi-energy images for material decomposition and VMI generation using a single exposure.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.

Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

Non-linear fusion of dual-energy projections for XCT imaging of challenging components

The imaging of multi-material components having large changes in attenuation behaviour is widely regarded as a limitation of X-ray computed tomographic techniques. The use of Dual Energy X-ray Computed Tomography (DECT) is common in the field of medical imaging, providing many benefits to imaging and characterisation. However, multienergy methods such as DECT remain underutilised in the industrial sector. In this work, a novel measurement based multi-energy image fusion approach was evaluated using two radiographic image stacks acquired under two distinct polychromatic spectra; this fusion was then compared to the results of the individual high and low-energy datasets as well as their average. The implemented approach was tested using a Titanium (Ti) and Carbon Fibre reinforced Polymer (CFRP) double-step lap joint to evaluate performance. Results indicated improvements to contrast in Dual Energy fused radiographs, aiding the interrogation of complex components in 2D. The translation of these benefits into the Reconstructed CT volume showed promise for isolated material regions. It was established that careful parameter selection was needed to achieve optimum fusion results, as it was found that failure to do so, resulted in contrast suppression in regions of mixed materials. The process proposed here acts as a first foundational step into the fusion of multi-energy industrial X-ray computed tomography (XCT) datasets using standard XCT equipment. Further work will aim at refining and comparing it to other projection weighting functions and measures.

New Composite Contrast Agents Based on Ln and Graphene Matrix for Multi-Energy Computed Tomography.

The subject of the current research study is aimed at the development of novel types of contrast agents (CAs) for multi-energy computed tomography (CT) based on Ln-graphene composites, which include Ln (Ln = La, Nd, and Gd) nanoparticles with a size of 2-3 nm, acting as key contrasting elements, and graphene nanoflakes (GNFs) acting as the matrix. The synthesis and surface modifications of the GNFs and the properties of the new CAs are presented herein. The samples have had their characteristics determined using X-ray photoelectron spectroscopy, X-Ray diffraction, transmission electron microscopy, thermogravimetric analysis, and Raman spectroscopy. Multi-energy CT images of the La-, Nd-, and Gd-based CAs demonstrating their visualization and discriminative properties, as well as the possibility of a quantitative analysis, are presented.

A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials

Many methods have been proposed for multienergy computed tomography (CT) imaging based on traditional CT systems. Usually, either prior knowledge of the X-ray spectra distribution or materials or the segmentation of the projection or reconstructed image is needed. To avoid these requirements, a multienergy CT method is proposed in this paper. A CT image can be seen as a linear combination of energy-dependent components and spatially dependent components. The latter components are the base images, while the former components are the coefficients. A blind decomposition model is constructed to decompose the multivoltage projections to obtain the base images and the energies. Multienergy CT images are computationally synthesized with the base images and the energies. Multivoltage projections can be acquired based on one scan with stepped voltages. X-ray scattering is considered an important factor in imaging errors and appears as a low-frequency signal. The variance is used to describe the low-frequency features and is minimized as the optimized objective function of the decomposition model. The solution of the model uses Karush–Kuhn–Tucker (KKT) conditions. In the experiments, the images reconstructed with the proposed method exhibit weak beam-hardening artifacts. Additionally, the X-ray energies of the different materials represented have small relative errors. Therefore, the reconstructed images have narrow energy intervals. This shows the effectiveness of the proposed method.

Estimating optical parameters of biological tissues with photon-counting micro-CT.

Wavelength-dependent absorption and scattering properties determine the fluorescence photon transport in biological tissues and image resolution of optical molecular tomography. Currently, these parameters are computed from optically measured data. For small animal imaging, estimation of optical parameters is a large-scale optimization problem, which is highly ill-posed. In this paper, we propose a new, to the best of our knowledge, approach to estimate optical parameters of biological tissues with photon-counting micro-computed tomography (micro-CT). From photon-counting x-ray data, multi-energy micro-CT images can be reconstructed to perform multi-organ segmentation and material decomposition in terms of tissue constituents. The concentration and characteristics of major tissue constituents can be utilized to calculate the optical absorption and scattering coefficients of the involved tissues. In our study, we perform numerical simulation, phantom experiments, and in vivo animal studies to calculate the optical parameters using our proposed approach. The results show that our approach can estimate optical parameters of tissues with a relative error of <10%, accurately mapping the optical parameter distributions in a small animal.

Photon-Counting Detector CT: Key Points Radiologists Should Know.

Photon-counting detector (PCD) CT is a new CT technology utilizing a direct conversion X-ray detector, where incident X-ray photon energies are directly recorded as electronical signals. The design of the photon-counting detector itself facilitates improvements in spatial resolution (via smaller detector pixel design) and iodine signal (via count weighting) while still permitting multi-energy imaging. PCD-CT can eliminate electronic noise and reduce artifacts due to the use of energy thresholds. Improved dose efficiency is important for low dose CT and pediatric imaging. The ultra-high spatial resolution of PCD-CT design permits lower dose scanning for all body regions and is particularly helpful in identifying important imaging findings in thoracic and musculoskeletal CT. Improved iodine signal may be helpful for low contrast tasks in abdominal imaging. Virtual monoenergetic images and material classification will assist with numerous diagnostic tasks in abdominal, musculoskeletal, and cardiovascular imaging. Dual-source PCD-CT permits multi-energy CT images of the heart and coronary arteries at high temporal resolution. In this special review article, we review the clinical benefits of this technology across a wide variety of radiological subspecialties.

Feasibility study of portable multi-energy computed tomography with photon-counting detector for preclinical and clinical applications

In this study, preclinical experiments were performed with an in-house developed prototypal photon-counting detector computed tomography (PCD CT) system. The performance of the system was compared with the conventional energy-integrating detector (EID)-based CT, concerning the basic image quality biomarkers and the respective capacities for material separation. The pre- and the post-contrast axial images of a canine brain captured by the PCD CT and EID CT systems were found to be visually similar. Multi-energy images were acquired using the PCD CT system, and machine learning-based material decomposition was performed to segment the white and gray matters for the first time in soft tissue segmentation. Furthermore, to accommodate clinical applications that require high resolution acquisitions, a small, native, high-resolution (HR) detector was implemented on the PCD CT system, and its performance was evaluated based on animal experiments. The HR acquisition mode improved the spatial resolution and delineation of the fine structures in the canine’s nasal turbinates compared to the standard mode. Clinical applications that rely on high-spatial resolution expectedly will also benefit from this resolution-enhancing function. The results demonstrate the potential impact on the brain tissue segmentation, improved detection of the liver tumors, and capacity to reconstruct high-resolution images both preclinically and clinically.

Multi-energy X-ray images fusion method based on fuzzy entropy and sparse representation for complex castings

Single-energy X-ray imaging technique cannot form all areas exposure imaging of non-uniform thickness castings at a time. Hence a multi-energy X-ray image fusion method based on fuzzy entropy and sparse representation (SR) is proposed in this paper to overcome this disadvantage. First, effective information image patches are extracted according to fuzzy entropy, thus avoiding the interference of information missing areas on the features of the castings. Then, we design a fusion strategy based on fuzzy entropy to fuse the sparse coefficient vectors. The experimental results show that the internal structure of complex castings can be completely and clearly presented using the proposed fusion method. In addition, when the proposed dictionary is applied to multi-energy X-ray images of other complex non-uniform thickness castings, the internal structure of these castings can also be presented completely and clearly.

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography.

Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice. We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT. For the data acquisition, the X-ray source is controlled to maintain an energy within a segmental arc, and then switch alternately to another energy level. This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction. To solve this problem, we propose a prior image constraint robust principal component analysis (PIC-RPCA) reconstruction method, which introduces structural similarity and spectral correlation into the reconstruction. A numerical simulation and a real phantom experiment were conducted to demonstrate the efficacy and robustness of the scan scheme and reconstruction method. The results showed that our proposed reconstruction method could have achieved better multi-energy images than other competing methods both quantitatively and qualitatively. Our proposed SSMECT scan with PIC-RPCA reconstruction method could lower kVp switching frequency while achieving satisfactory reconstruction accuracy and image quality.

Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT.

Purpose: We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. Approach: An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches ( ) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images ( ) with the remaining real insert materials that were unseen in network training were used for testing. Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy. Results: Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density ( -value [0.0625, 0.999]) and improved it at lower-density inserts ( ) with overall MAPE: Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, ). Conclusion: In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.

Update on Multienergy CT: Physics, Principles, and Applications.

Multienergy CT involves acquisition of two or more CT measurements with distinct energy spectra. Using the differential attenuation of tissues and materials at different x-ray energies, multienergy CT allows distinction of tissues and materials beyond that possible with conventional CT. Multienergy CT technologies can operate at the source or detector level. Dual-source, rapid tube-voltage switching, and dual-layer detector CT are the most commonly used multienergy CT technologies. Most of the currently available technologies typically use two energy levels, commonly referred to as dual-energy CT. With use of two or more energy bins, photon-counting detector CT can perform multienergy CT beyond current dual-energy CT technologies. Multienergy CT postprocessing can be performed in the projection or image domain using two-material or multimaterial decomposition. The most commonly used multienergy CT images are virtual monoenergetic images (VMIs), iodine maps, virtual noncontrast (VNC) images, and uric acid images. Low-energy VMIs are used to boost contrast signal and enhance lesion conspicuity. High-energy VMIs are used to decrease some artifacts. Iodine maps are used to evaluate perfusion, characterize lesions, and evaluate response to therapy. VNC images are used to characterize lesions and save radiation dose by eliminating true noncontrast images from multiphasic acquisitions. Uric acid images are used for characterization of renal calculi and gout. Online supplemental material is available for this article. ©RSNA, 2020.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80–100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml−1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml−1 (DS-EID-CT) to 2.39 ± 0.05 mg ml−1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

High-dynamic-range X-ray CT imaging method based on energy self-adaptation between scanning angles

High-dynamic-range (HDR) X-ray CT imaging is effective in detecting some complex structures. For previous low-dynamic-range (LDR) imaging detectors, multi-energy LDR image sequence fusion can extend the dynamic range, but the efficiency is decreased. However, with the application of HDR imaging devices, traditional fixed-energy X-ray imaging can cause incongruity within energy, dynamic range, and the equivalent thickness of the workpiece at different projection angles. Then, the projection has a blurred edge, and the CT image quality is poor because of scattering and the inadequate dose. In this paper, a new HDR X-ray CT imaging method with energy self-adaptation between scanning angles for HDR imaging devices is studied. Low-energy prescanning is used to determine the initial scanning energy and obtain the edge contour information with an attenuating effect on scattering. By establishing a mathematical model between the gray level of the projection and the transmission voltage, the transmission energy at each angle is adjusted adaptively. Then, the prescanning and energy self-adaption scanning projections are fused to obtain the complete projection of the complex workpiece. Finally, a conventional reconstruction algorithm is used to reconstruct the HDR CT image. The experimental results show that the proposed imaging method can achieve HDR CT imaging of complex structures with high reconstruction quality, clear edge details, and high completeness.

Quantitative Knee Arthrography in a Large Animal Model of Osteoarthritis Using Photon-Counting Detector CT.

The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT. A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp "V71" kernel and a quantitative "D50" kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant. Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix. We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.