PurposeEvaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system.ApproachA multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.0 mg/ml) was scanned over a range of radiation dose levels (CTDIvol 0.5 to 15.0 mGy) using two tube voltages (120, 140 kVp) and two different source modes (single-, dual-source). To assess the agreement between nominal and measured iodine concentrations, iodine density maps at different iterative reconstruction levels were utilized to calculate root mean square error (RMSE) and generate Bland–Altman plots by grouping radiation dose levels (ultra-low: <1.5; low: 1.5 to 5; medium: 5 to 15 mGy) and iodine concentrations (low: <5; high: 5 to 15 mg/mL).ResultsOverall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, RMSE was stable at 0.31, 0.28, 0.33, and 0.30 mg/ml for tube voltage and source mode combinations. Ultimately, the accuracy of iodine quantification was higher for the phantom without an extension ring (RMSE 0.21 mg/mL) and did not vary across different levels of iterative reconstruction.ConclusionsThe first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.
In recent years, the importance of spectral CT scanners in clinical settings has significantly increased, necessitating the development of phantoms with spectral capabilities. This study introduces a dual-filament 3D printing technique for the fabrication of multi-material phantoms suitable for spectral CT, focusing particularly on creating realistic phantoms with orthopedic implants to mimic metal artifacts. Previously, we developed PixelPrint for creating patient-specific lung phantoms that accurately replicate lung properties through precise attenuation profiles and textures. This research extends PixelPrint's utility by incorporating a dual-filament printing approach, which merges materials such as calcium-doped Polylactic Acid (PLA) and metal-doped PLA, to emulate both soft tissue and bone, as well as orthopedic implants. The PixelPrint dual-filament technique utilizes an interleaved approach for material usage, whereby alternating lines of calcium-doped and metal-doped PLA are laid down. The development of specialized filament extruders and deposition mechanisms in this study allows for controlled layering of materials. The effectiveness of this technique was evaluated using various phantom types, including one with a dual filament orthopedic implant and another based on a human knee CT scan with a medical implant. Spectral CT scanner results demonstrated a high degree of similarity between the phantoms and the original patient scans in terms of texture, density, and the creation of realistic metal artifacts. The PixelPrint technology's ability to produce multi-material, lifelike phantoms present new opportunities for evaluating and developing metal artifact reduction (MAR) algorithms and strategies.
Dual-source photon-counting CT combines the high temporal resolution and high pitch of dual-source CT with the material quantification capabilities of photon-counting CT. It, however, results in cross-scatter that increases in severity with increased patient size and collimation. This cross-scatter must be corrected to ensure the removal of scatter artifacts and improve quantitative accuracy. To evaluate residual cross-scatter of a first-generation dual-source photon-counting CT and the effect of phantom size, collimation, and radiation dose, a phantom was scanned in single- and dual-source modes with and without its extension ring at three collimations and three radiation doses. Virtual monoenergetic images (VMI) at 50 keV, VMI 150 keV, and iodine density maps were reconstructed to determine variation between acquisition parameters in single- and dual-source modes. Additionally, differences relative to single-source acquisitions and to singlesource and small collimation acquisitions were calculated to reflect residual cross-scatter with and without matched collimation. At VMI 50 keV, inserts exhibited accuracy and similar variation between single- and dual-source modes, averaging 5.4 ± 2.6 and 6.2 ± 2.5 HU, respectively, across phantom size, collimation, and radiation dose. Differences relative to single-source measured 5.1 ± 8.5 and 0.4 ± 4.2 HU while differences relative to single-source and small collimation acquisitions were 6.4 ± 10.8 HU and -0.5 ± 3.9 HU for VMI 50 and 150 keV, respectively. This minimal residual cross-scatter increases confidence in the quantitative accuracy of spectral results necessary for clinical applications of dual-source photon-counting CT with motion, such as cardiac imaging.
Cardiovascular disease diagnosis relies heavily on diagnostic imaging. Advancement in computed tomography (CT) technology has particularly improved diagnosis in patients with coronary artery disease. In particular, the improved spatial resolution and iodine quantification capabilities of photon-counting CT (PCCT) have the potential to further improve the diagnostic workflow. Since iodine quantification has become a critical aspect of clinical diagnosis, several studies have been conducted to evaluate its effectiveness and the parameters that may affect it. An additional relationship, the effect of spatial resolution and vessel size on iodine quantification, was examined with a designed phantom. A phantom consisted of six different tubes of changing diameters (2 to 12 mm), along with a cone and an hourglass-shaped tube with diameters from 3 to 8 mm. It was scanned on a PCCT after being filled with an iodine solution. Iodine density maps, VNC, and VMI 70keV were then reconstructed with different fields of view (250 mm, 350 mm, 450 mm). Regions of interest were placed on spectral results along the length of the hourglass. Spectral results were highly accurate for vessels larger than 4 mm in diameter and regions of interest larger than 3 mm. The bias in iodine quantification increases with smaller diameters. Conversely, VNC increased, illustrating a directly proportional relationship between VNC and iodine density. The proposed phantom design allows for future studies that further investigate the relationship between spatial resolution and iodine quantification, especially in clinical workflow for optimizing protocols, implementing new CT technologies, and harmonizing protocols between different CT platforms.
Dual-source photon-counting computed tomography (PCCT) enables a novel ultra-high-resolution (UHR) scanning mode that can provide UHR conventional images (0.2 mm) as well as spectral results (0.4 mm). To evaluate the spatial resolution and quantitative capabilities of the UHR mode, with a focus on thoracic imaging, a PixelPrint lung phantom mimicking interstitial lung disease with honeycombing and iodine rods of different diameters and concentrations directly attached to the phantom were scanned at doses from 1.0 to 7.5 mGy. Virtual monoenergetic images at 50 keV, virtual non-contrast, and iodine density maps at 0.4 and 1 mm slice thickness were reconstructed as well as conventional images at 0.2, 0.4, and 1 mm slice thickness, all with standard lung and quantitative reconstruction kernels (matrix size 512x512). Iodine quantification was performed for the attached rods, and clinically relevant features in the lung phantom were utilized to evaluate spatial resolution. Overall, iodine quantification was stable across radiation dose, reconstruction kernels, and slice thickness with errors of 0.25, 0.20, and 0.40 mg/mL for 1, 2, and 5 mg/mL iodine, respectively. Even the smallest iodine core rod was detected in the extended CT phantom for a higher dose. For the diseased lung region, images at 0.2 mm slice thickness appeared sharper and depicted smaller structures better, even with increased noise in comparison to thicker slices. In conclusion, UHR mode demonstrated high spatial resolution with detection of small features and accurate iodine quantification, which may provide diagnostic advantage to thoracic imaging with more precise and accurate information.
Percutaneous ablation procedures have been increasingly utilized to non-invasively treat tumors, such as hepatocellular carcinoma, by heating tumor cells beyond the lethal threshold. Intraprocedural temperature monitoring via spectral CT thermometry with a sensitivity less than 3 °C can reduce local recurrence rates by ensuring the tumor and its surrounding safety margin reach lethal temperatures. Because temperature sensitivity is reliant on noise, the effect of additional denoising, radiation dose, slice thickness, and iterative reconstruction levels on temperature sensitivity was evaluated on physical density slices utilized to generate temperature maps. Three different denoising algorithms (total variation, bilateral filtering, and non-local means) were applied to input images prior to generating physical density maps. Differences in noise in physical density and temperature sensitivity were calculated for each combination of parameters. All three denoising algorithms did not significantly affect quantification with an average difference of 1 x 10-4 g/mL from standard reconstructions, while generally non-local means denoising performed best with noise decreasing to 2 x 10-4 g/mL. The reduction in noise corresponded to temperature sensitivity decreasing from 15 ± 4 °C with standard reconstructions to 3 ± 2 °C with non-local means denoising at 2 mGy with 2 mm slices. Overall, temperature sensitivity at low radiation doses improved to clinically satisfactory levels with additional denoising. These accurate temperature maps from spectral CT thermometry will enable real-time, non-invasive temperature monitoring to ensure critical structures are not thermally damaged and the entire tumor and safety margin reach the lethal threshold, reducing local recurrences.
Patient-based CT phantoms, with realistic image texture and densities, are essential tools for assessing and verifying CT performance in clinical practice. This study extends our previously presented 3D printing solution (PixelPrint) to patient-based phantoms with soft tissue and bone structures. To expand the Hounsfield Unit (HUs) range, we utilize a stone-based filament. Applying PixelPrint, we converted patient DICOM images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. Two different phantoms were designed to demonstrate the high reproducibility of our approach with micro-CT acquisitions, and to determine the mapping between filament line widths and HU values on a clinical CT system. Moreover, a third phantom based on a clinical cervical spine scan was manufactured and scanned with a clinical spectral CT scanner. CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels. Measured differences between patient and phantom are around 10 HU for bone marrow voxels and around 150 HU for cortical bone. In addition, stone-based filament can accurately represent boney tissue structures across the different x-ray energies, as measured by spectral CT. This study demonstrates the feasibility of our 3D-printed patient-based phantoms to be extended to soft-tissue and bone structure while maintaining accurate organ geometry, image texture, and attenuation profiles for spectral CT.
Efficient removal of solid focal tumors is a major challenge in modern medicine. Percutaneous thermal ablation is a first-line treatment for patients not fit for surgical resection or when the disease burden is low, mainly due to expedited patient recovery times, lower rates of post-operative morbidity, and reduced healthcare costs. While continuously gaining popularity, ~100,000 yearly thermal hepatic ablation procedures are currently performed without actively monitoring temperature distributions, leading to high rates of incomplete ablations, local recurrences, and damage to surrounding structures. Recent advancements in computed tomography (CT), especially spectral CT, provide promising opportunities for lowering these rates. The additional information available with spectral CT can provide the necessary capabilities to achieve accurate, reliable, on-demand, and non-invasive thermometry during ablation procedures. By taking advantage of our newly developed spectral physical density maps and their direct relation with temperature changes, we performed experiments on phantoms and ex vivo tissue to develop, evaluate, optimize, and refine a method for generating thermometry maps from spectral CT scans. Our results validate the accuracy of the spectral physical density model, allowing “whole-organ” mass quantifications that are accurate within one percent, as well as demonstrate an ability to extract temperature changes (linear correlation coefficient of 0.9781) non-invasively and in real-time.
Purpose: to investigate image quality of the ultra-high-resolution (UHR) mode of a dual-source photon-counting CT scanner in visualizing mixed (soft and hard) coronary artery plaques. Materials and methods: We scanned a custom-made phantom with 10 mixed plaques of various sizes and compositions. Each scan was repeated three times. Images were reconstructed with FBP, and model-based quantum iterative reconstruction (QIR). Image quality was investigated by measuring mean CT numbers, noise standard deviation (SD), and by line profiles.
Results: UHR mode provided sharper difference between soft and hard plaques, and the lumen by reducing blooming artifacts. Furthermore, it improved the true CT number of the values by reducing partial volume However, SD of noise increases by a factor of ~8 in FBP reconstructions at thinnest slice thickness (0.2 mm). Quantum iterative reconstruction algorithm reduced image noise x4 of the SR FBP without any apparent loss of spatial resolution.
Conclusion: UHR PCCT improves plaque characterization through improved spatial resolution which results in lower blooming artifacts and partial volume effects. The increase in image noise can be mitigated by using model-based iterative reconstruction algorithms without any loss of spatial resolution. Depending on the imaging task, further noise reduction can be achieved by reconstructing thicker slices. A more detailed investigation with noise power spectrum analysis and observer model studies is warranted.
Cardiac CT is a useful tool for cardiovascular diagnostics that offers different acquisition modes, each with its advantages. The development of direct converting detector technology has resulted in the clinical translation of dual-source photon-counting CT. This takes advantage of the improved image quality at high heart rates from dual-source CT while making available spectral results for more precise material characterization and quantification. To evaluate the stability of spectral results among different acquisition modes and heart rates, a cardiac motion phantom with a rod mimicking a 50% coronary stenosis was scanned with a dual-source photon-counting CT in three different acquisition modes (retrospective dual-source spiral, prospective dual-source step-and-shoot, dual-source flash spiral) and at different heart rates (60, 80, 100 bpm). Dice scores of stenosed regions relative to a static scan, eccentricity of non-stenosed regions, full width half max, and normalized area under the curve of line profiles were calculated for iodine density maps, and virtual mono-energetic images at 40 and 70 keV. Dice scores and eccentricity were consistent and not significantly affected by acquisition mode or heart rate for spectral results. Full width half max and normalized area under the curve similarly illustrated minor differences between acquisition modes and heart rates. The consistency in these metrics demonstrate preserved image structure and allows for the use of spectral results with high confidence. Dual-source photon-counting CT will enable cardiovascular diagnostics with better material characterization and differentiation.
Hepatocellular carcinoma, the fastest rising cause of cancer-related deaths, is commonly treated with percutaneous ablative therapies where tumor cells are destroyed once tissue temperatures reach a lethal threshold. However, high progression and recurrence rates post ablation suggest the need for intraprocedural temperature monitoring to ensure the lethal threshold (>60°C) is reached and a sufficient safety margin is obtained. A previously developed model generates physical density maps from spectral CT data. These spectral physical density quantifications enable thermometry by taking advantage of the thermal volumetric expansion equation that relates the change in temperature to physical density changes. To validate the physical density model, an ex vivo bovine muscle was weighed and scanned on a clinical spectral CT scanner with different scanning parameter combinations (collimation, dose, helical/axial scans). Calculated mass from physical density maps and volume demonstrated high accuracy with a maximum percent error of 0.34% (<1.1 grams for a345 gram sample) and minimal effects of scanning parameters. After validating the accuracy of the physical density maps, the muscle was subjected to heating and cooling while scanning to evaluate the relationship between physical density and temperature. Spectral results were continuously generated to calculate physical density maps at different temperatures. A linear relationship between change in temperature and change in physical density was established with strong correlation (R = 0.9781). The reflection of thermal volumetric expansion in physical density quantifications indicate its potential utility for providing real-time temperature feedback to interventional radiologists during ablative procedures for not only hepatocellular carcinoma, but also other types of malignancies.
Liver lesion detection and characterization presents a longstanding challenge for radiologists. Since liver lesions are mainly characterized from information obtained at both arterial and portal venous circulatory phases, current hepatic Computed tomography (CT) protocols involve intravenous contrast injection and subsequent multiple CT acquisitions. Because detection of lesions by CT often requires further investigation with MRI, improved differentiation CT capabilities are highly desirable. Recently developed imaging protocols for spectral photon-counting CT enable simultaneous mapping of arterial and portal-venous enhancements by injecting two different contrast agents sequentially, allowing robust pixel-to- pixel spatial alignment between the different contrast phases with a reduction of radiation exposure. Here we propose a method that allows to quantitatively and reliably distinguish between two contrast agents in a single dual-energy CT (DECT) acquisition by taking advantage of the unique abilities of modern self-learning algorithms for non-linear mapping, feature extraction, and feature representation. For this purpose, we designed a U-net architecture convolutional neural network (CNN). To overcome training data requirements, we utilizing clinical DECT images to simulate dual-contrast spectral datasets. With the unique network architecture and training datasets, we demonstrate reliable dual-contrast quantifications from DECT. Our results demonstrate an ability to quantify densities of water, iodine and gadolinium, with root mean square errors of 0.2 g/ml, 1.32 mg/ml and 1.04 mg/ml, respectively. While observing some material-cross artifacts, our model demonstrated a high robustness to noise. With the rapid increase in DECT usage, our results pave the way for improved diagnostics and better patient outcome with available hardware implementations.
Pediatric imaging utilizes the quantitative capabilities of CT to guide clinical decision making and treatment, but image quality is heavily affected by variation in patient sizes and the need for lower dose scans. Dual energy CT generates spectral results such as virtual monoenergetic images (VMI), electron density (ED), and effective atomic number (Zeff) that enhance material characterization and quantification. Though it has not been extensively explored, application of DECT to pediatric imaging may allow increased stability in quantitative measures with varying patient size, dose, and tube voltage. To examine the dependency of size, dose, and tube voltage, a phantom with tissue-mimicking inserts was scanned with dual-layer spectral detector CT with different extension rings to simulate different pediatric patient sizes. Each size configuration was subsequently scanned at CTDIvol of 9, 6, and 3 mGy with 100 and 120 kVp to obtain conventional CT and spectral results. Overall, both VMI and ED values were accurately quantified. VMI at 70 keV and 9 mGy demonstrated smaller differences among patient size and kVp compared to conventional images. Low dose dependency relative to 9 mGy was also present for VMI. Similarly, ED and Zeff showed low dependency on patient size, dose, and kVp and maintained material differentiability. Stability of these spectral results with different patient sizes, doses, and tube voltages illustrates the potential application of spectral detector CT to pediatric patients not only to improve the consistency of quantitative measures across patient sizes but also to allow lower doses without impairing quantification.
Coupling computed tomography with positron emission tomography (PET/CT) supplements tracer uptake with anatomical information for localization and improves PET quantification by using CT images for attenuation correction. Iodinated contrast agents in CT scans are used to enhance vascularity, organs, and abnormalities and for characterize lesions. However, current attenuation correction methodologies generate overestimates in standardized uptake values (SUV) in the presence of materials with high atomic numbers such as iodinated contrast agents. Utilizing electron density (ED) from dual energy CT (DECT) may result in less biased attenuation correction as ED is proportional to attenuation at PET emission energy of 511 keV. To evaluate different methods of attenuation correction, five phantom configurations with varying iodine concentrations and constant concentrations of Fluorine-18 were scanned using PET/CT and DECT at similar scanning parameters. Phantom configurations were scanned at CTDIvol 2, 4, 6, and 8 mGy with DECT to evaluate the effect of dose on ED and SUV. For attenuation correction, ED was transformed into attenuation at 511 keV through reported material compositions and ED. SUV demonstrated less biased behavior in the presence of iodinated contrast media with ED-based correction (-1.3% to 1.4%, p=0.271) compared to nominal correction (1.5% to 8.6%, p=0.000). No interaction effect between dose and phantom configuration or effect of dose on SUV was present, which was also reflected in ED stability in different tissue mimics. Use of ED-based attenuation correction from DECT allowed for less biased SUV when increasing concentrations of iodinated contrast agents, indicating quantitative advantages of DECT coupled with PET.
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