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.
Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80kVp, low photon energy detector layer and the 140kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.
Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.
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.
The performance of a CT scanner for detectability tasks is difficult to precisely measure. Metrics such as contrast-to-noise ratio, modulation transfer function, and noise power spectrum do not predict detectability in the context of nonlinear reconstruction. We propose to measure detectability using a dense search challenge: a phantom is embedded with hundreds of target objects at random locations, and a human or numerical observer analyzes the reconstruction and reports on suspected locations of all target objects. The reported locations are compared to ground truth to produce a figure of merit, such as area under the curve (AUC), that is sensitive to the acquisition dose and the dose efficiency of the CT scanner. We used simulations to design such a dense search challenge phantom and found that detectability could be measured with precision better than 5%. Test 3D prints using the PixelPrint technique showed the feasibility of this technique.
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.
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.
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.
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.
KEYWORDS: Computed tomography, Lung, Printing, Signal attenuation, Image segmentation, 3D printing, 3D modeling, Spatial resolution, 3D image processing, Manufacturing
Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patientbased lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-ofinterest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.
Speckle-based phase-contrast imaging offers enhanced sensitivity towards weakly-attenuating materials and a simple and cheap setup, but requires accurate tracking of sample-induced speckle pattern modulations. We implemented a convolution neural network for speckle tracking in x-ray phase contrast imaging. The model was trained on simulated speckle patterns generated from a wave-optics simulation and then compared against conventional algorithms. Our solution showed comparable bias, substantially improved root mean squared error and spatial resolution, and the shortest computational time. Thus, our approach enhances the performance of speckle-based phase-contrast imaging.
Purpose: Dual-contrast protocols are a promising clinical multienergy computed tomography (CT) application for focal liver lesion detection and characterization. One avenue to enable multienergy CT is the introduction of photon-counting detectors (PCD). Although clinical translation is highly desired because of the diagnostic benefits of PCDs, it will still be a decade or more before they are broadly available. In our work, we investigated an alternative solution that can be implemented on widely used conventional CT systems (single source and integrating detector) to perform multimaterial spectral decomposition for dual-contrast imaging.
Approach: We propose to slowly alternate the x-ray tube voltage between 3 kVp levels so each kVp level covers a few degrees of gantry rotation. This leads to the challenge of sparsely sampled projection data in each energy level. Performing the material decomposition (MD) in the sinogram domain is not directly possible as the projection images of the three energy levels are not angularly aligned. In order to overcome this challenge, we developed a convolutional neural network (CNN) framework for sparse sinogram completion (SC) and MD. To evaluate the feasibility of the slow kVp switching scheme, simulation studies of an abdominal phantom, which included liver lesions, were conducted.
Results: The line-integral SC network yielded sinograms with a pixel-wise RMSE < 0.05 of the line-integrals compared to the ground truth. This provided acceptable image quality up to a switching angle of 9 deg per kVp. The MD network we developed allowed us to differentiate iodine and gadolinium in the sinogram domain. The average relative quantification errors for iodine and gadolinium were below 10%.
Conclusions: We developed a slow triple kVp switching data acquisition scheme and a CNN-based data processing pipeline. Results from a digital phantom validation illustrate the potential for future applications of dual-contrast agent protocols on practically available single-energy CT systems.
The feasibility of acquiring multi-energy CT data through slow modulation of the kVp as an alternative to photon-counting detectors (PCDs) is currently under exploration. A low kVp-switching rate can be enabled with a conventional CT system but raises challenges due to missing sinogram views. Our previous work used a CNN-based method for sinogram completion by generating full-sampled images from undersampled sinograms, providing an acceptable image quality at a 22°/kVp switching rate. The purpose of this study was to investigate a GAN-based spectral sinogram completion method for enabling a lower kVp switching rate. A Pix2Pix GAN model with paired undersampled sinogram of 45° or 120° projections/kVp and its corresponding full-sampled sinogram was implemented and trained. The completed data was subsequently used to perform sinogram domain material decomposition. Our results on a simulated FORBILD abdomen phantom dataset showed that the GAN-based method can further lower the kVp switching rate to 45° projections/kVp. The proposed GAN-based sinogram completion method facilitates slow-kVp switching acquisitions and thus further relaxes hardware requirements.
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.
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.
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.
Physical density assessments may provide valuable insights for a range of diagnostic purposes in abdominal, pulmonary, and breast imaging. These purposes include differentiating iso-attenuating cysts from lesions, aiding in function tests of viral pneumonia, and quantifying breast cancer risk. Physical density assessments also provide a natural and intuitive measurement of human tissue that may be useful for measuring global body mass distributions and comparing measurement techniques. In this work, we present a spectral physical density map generated from clinical dual-energy computed tomography (DECT) datasets. We utilized available DECT estimates for effective atomic number, monoenergetic attenuation, and electron density as inputs into the Alvarez-Macovski model and the relation between a material’s physical and electron densities. To achieve higher accuracy assessments, these underlying equations were parametrized and fit to published tissue composition and density data that had been supplemented with simulated iodine enhancements. Validating the fits with phantom experiments, we observed measurements that are within ± 0.02 g/ml of their nominal values. Our assessments thus fall inside the margin-of-error for the ground-truth densities declared by the phantom manufacturer. Though we validated our density maps only on a dual-layer DECT implementation, the development and optimization of this new spectral result were independent of any other spectral CT technology. Because of this independence and the high accuracies of the maps, we encourage future clinical trials testing the potential applications of this new result for diagnostic imaging.
Dual Energy CT is a modern imaging technique that is utilized in clinical practice to acquire spectral information for various diagnostic purposes including the identification, classification, and characterization of different liver lesions. It provides additional information that, when compared to the information available from conventional CT datasets, has the potential to benefit existing computer vision techniques by improving their accuracy and reliability. In order to evaluate the additional value of spectral versus conventional datasets when being used as input for machine learning algorithms, we implemented a weakly-supervised Convolutional Neural Network (CNN) that learns liver lesion localization and classification without pixel-level ground truth annotations. We evaluated the lesion classification (healthy, cyst, hypodense metastasis) and localization performance of the network for various conventional and spectral input datasets obtained from the same CT scan. The best results for lesion localization were found for the spectral datasets with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV virtual mono-energetic images, respectively, while lesion localization distances of 10.58 ± 17.65 mm were measured for the conventional dataset. In addition, the 40 keV virtual mono-energetic datasets achieved the highest overall lesion classification accuracy of 0.899 compared to 0.854 measured for the conventional datasets. The enhanced localization and classification results that we observed for spectral CT data demonstrates that combining machine-learning technology with spectral CT information may improve the clinical workflow as well as the diagnostic accuracy.
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