Normal Resolution Research Articles

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Rationale and objectivesRadiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methodsA total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. ResultsThe SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. ConclusionsDeep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

Performance of high-resolution CT for detection and discrimination tasks related to stenotic lesions - A phantom study using model observers.

Accurate detection and grading of atheromatous stenotic lesions within the cardiac, renal, and intracranial vasculature is imperative for early recognition of disease and guiding treatment strategies. In this work, a stenotic lesion phantom was used to compare high resolution and normal resolution modes on the same CT scanner in terms of detection and size discrimination performance. The phantom is comprised of three acrylic cylinders (each 15.0cm in diameter and 1.3cm thick) with a matching array of holes in each module. The outer two modules contain holes that are slightly larger than the corresponding hole in the central module to simulate stenotic narrowing in vasculature. The stack of modules was submerged in an iodine solution simulating contrast-enhanced stenotic lesions with a range of lumen diameters (1.32-10.08mm) and stenosis severity (0%, 50%, 60%, 70%, and 80%). The phantom was imaged on the Canon Aquilion Precision high-resolution CT scanner in high-resolution (HR) mode (0.25mm × 0.50mm detector element size) and normal-resolution (NR) mode (0.50mm × 0.50mm) using 120 kV and two dose levels (14 and 21mGy SSDE) with 30 repeat scans acquired for each combination. Filtered back-projection (FBP) and a hybrid-iterative reconstruction (AIDR) were used with the FC18 kernel, as well as a deep learning algorithm (AiCE) which is only available for HR. A non-prewhitening model observer with an eye filter was implemented to quantify performance for detection and size discrimination tasks in the axial plane. Detection performance improved with increasing diameter, dose, and for AIDR in comparison to FBP for a fixed resolution mode. Performance in the HR mode was generally higher than NR for the smaller lumen diameters (1-5mm) with decreasing differences as the diameter increased. Performance in NR mode surpassed HR mode for lumen diameters greater than ∼4mm and ∼5mm for 14mGy and 21mGy, respectively. AiCE provided consistently higher detection performance compared with AIDR-FC18 (48% higher for a 6mm lumen diameter). Discrimination performance increased with increasing nominal diameter, dose, and for larger differences in stenosis severity. When comparing discrimination performance in HR to NR modes, the largest relative differences occur at the smallest nominal diameters and smallest differences in stenosis severity. The AiCE reconstruction algorithm produced the highest overall discrimination performance values, and these were significantly higher than AIDR-FC18 for nominal diameters of 7.14 and 10.08mm. HR mode outperforms NR for detection up to a specific diameter and the results improve with AiCE and for higher dose levels. For the task of size discrimination, HR mode consistently outperforms NR if AIDR-FC18 is used for dose levels of at least 21mGy, and the results improve with AiCE and for the smallest differences in stenosis severity investigated (50% vs. 60%). High-resolution CT appears to be beneficial for detecting smaller simulated lumen diameters (<5mm) and is generally advantageous for discrimination tasks related to stenotic lesions, which inherently contain information at higher frequencies, given the right reconstruction algorithm and dose level.

Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study.

Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA. Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness. For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05). Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT. • Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).

Performance of Ultra-High-Resolution Computed Tomography in Super High-Resolution Mode at the Routine Radiation Dose: Phantom Study.

Using a chest phantom, we compared the image quality of ultra-high-resolution computed tomography (U-HRCT) images acquired in super high-resolution (SHR) and normal resolution (NR) mode and at the routine radiation dose. The detector size was 0.25 and 0.5 mm, respectively. A chest phantom was scanned on a U-HRCT scanner. The scan parameters were tube voltage 120 kV and volume CT dose index 13.0 mGy, the routine radiation dose for conventional scans. The rotation time was 0.5 s/rot, the number of matrices was 512 in NR and 1024 in SHR mode. For physical evaluation, the modulation transfer function was measured on the spherical simulated nodule, and the noise power spectrum on the cylindrical water phantom. A CT value profile curve was created using an in-house simulated bronchial phantom. For visual evaluation, 3 radiologists and 3 radiology technologists evaluated overall image quality using a 4-grade scale (grade 1, poor; and grade 4, excellent). The 10% of modulation transfer function was 13.5 lp/cm in NR and 14.9 lp/cm in SHR mode ( P <0.01). ƒ peak was 5.6 lp/cm in NR and 8.8 lp/cm in SHR mode ( P <0.01), and the peak of noise power spectrum shifted. On the profile curves, the CT value at the edge changed in NR but not in SHR mode. The overall image quality was grade 3.0 ± 0.7 in SHR and grade 2.0 ± 0.7 in NR mode ( P <0.01). The image quality of SHR mode with U-HRCT was superior to that of NR mode at the routine radiation dose.

Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an advanced noninvasive imaging technology that can measure cerebral blood flow (CBF) quantitatively without a contrast agent injection or radiation exposure. However, because of the weak labeling, conventional ASL images usually suffer from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. Therefore, a method that can simultaneously improve the spatial resolution and SNR isneeded. In this work, we proposed an unsupervised superresolution (SR) method to improve ASL image resolution based on a pyramid of generative adversarial networks (GAN). Through layer-by-layer training, the generators can learn features from the coarsest to the finest. The last layer's generator that contains fine details and textures was used to generate the final SR ASL images. In our proposed framework, the corresponding T1-weighted MR image was supplied as a second-channel input of the generators to provide high-resolution prior information. In addition, a low-pass-filter loss term was included to suppress the noise of the original ASL images. To evaluate the performance of the proposed framework, a simulation study and two real-patient experiments based on the in vivo datasets obtained from three healthy subjects on a 3T MR scanner were conducted, regarding the low-resolution (LR) to normal-resolution (NR) and the NR-to-SR tasks. The proposed method was compared to the nearest neighbor interpolation, trilinear interpolation, third-order B-splines interpolation methods, and deep image prior (DIP) with the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as the quantification metrics. The averaged ASL images acquired with 44 min acquisition time were used as the ground truth for real-patient LR-to-NR study. The ablation studies of low-pass-filter loss term and T1-weighted MR image were performed based on simulation data. For the simulation study, results show that the proposed method achieved significantly higher PSNR ( -value 0.05) and SSIM ( -value 0.05) than the nearest neighbor interpolation, trilinear interpolation, third-order B-splines interpolation, and DIP methods. For the real-patient LR-to-NR experiment, results show that the proposed method can generate high-quality SR ASL images with clearer structure boundaries and low noise levels and has the highest mean PSNR and SSIM. For real-patient NR-to-SR tasks, the structure of the results using the proposed method is sharper and clearer, which are the most similar to the structure of the reference 44 min acquisition image than other methods. The proposed method also shows the ability to remove artifacts in the NR image while superresolution. The ablation study verified that the low-pass-filter loss term and T1-weighted MR image are necessary for the proposedmethod. The proposed unsupervised multiscale GAN framework can simultaneously improve spatial resolution and reduce image noise. Experiment results from simulation data and three healthy subjects show that the proposed method achieves better performance than the nearest neighbor interpolation, the trilinear interpolation, the third-order B-splines interpolation, and DIP methods.

Quantification of airway dimensions using a high-resolution CT scanner: A phantom study.

Small airways with inner diameters less than 2mm are sites of major airflow limitations in patients with chronic obstructive pulmonary disease (COPD) and asthma. The purpose of this study is to investigate the limitations for accurate assessment of small airway dimensions using both high-resolution CT (HRCT) and conventional normal-resolution CT at low dose levels. To model the normal human airways from the 3rd to 20th generations, a cylindrical polyurethane phantom with 14 airway tubes of inner diameters (ID) ranging from 0.3 to 3.4mm and wall thicknesses (WT) ranging from 0.15 to 1.6mm was placed within an Anthropomorphic QRM-Thorax phantom. The Aquilion Precision (Canon Medical Systems Corporation) HRCT scanner was used to acquire images at 80, 100, and 120kV using high resolution mode (HR, 0.25mm×160 detector configuration) and normal-resolution (NR) mode (0.5mm×80 detector configuration). The HR data were reconstructed using a 1024×1024matrix (0.22×0.22×0.25mm voxel size) and the NR data were reconstructed using a 512×512matrix (0.43×0.43×0.50mm). Two reconstruction algorithms (filtered back projection; FBP and an adaptive iterative dose reduction 3D algorithm; AIDR 3D) and three reconstruction kernels (FC30, FC52, and FC56) were investigated. The dose values ranged from 0.2 to 6.2mGy. A refined automated full-width half-maximum (FWHM) method was used for the measurement of airway dimensions, where the density profiles were computed by radial oversampling using a polar coordinate system. Both ID and WT were compared to the known dimensions using a regression model, and the root-mean-square error (RMSE) and average error were computed across all 14 airway tubes. The results indicate that the ID can be measured within a 15% error down to approximately 0.8 and 2.0mm using the HR and NR modes, respectively. The overall RMSE (and average error) of ID measurements for HR and NR were 0.10mm (-0.70%) and 0.31mm (-2.63%), respectively. The RMSE (and average error) of WT measurements using HR and NR were 0.10mm (23.27%) and 0.27mm (53.56%), respectively. The WT measurement using HR yielded a factor of two improvement in accuracy as compared to NR. High-resolution CT can provide more accurate measurements of airway dimensions as compared with NR CT, potentially improving quantitative assessment of pathologies such as COPD and asthma. The HR mode acquired and reconstructed with AIDR3D and the FC52kernel provides most accurate measurement of airway dimensions. Low-dose HR measurements at dose level above 0.9mGy can provide improved accuracy on both inner diameters and wall thicknesses compared to full dose NR airway phantom measurements.

Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner

PurposeTo evaluate the image quality and ability to delineate the small visceral arteries of high-resolution (HR) abdominal CT angiography (CTA) using an ultra-high-resolution computed tomography (UHR CT) scanner.Materials and methodsThirty-seven patients were enrolled who underwent abdominal CTA using a UHR CT scanner. The images were reconstructed with a matrix of 1024 × 1024 and 0.25 mm thickness for HR CTA and with a matrix of 512 × 512 and 0.5 mm thickness for normal resolution (NR) CTA. Maximum CT value, image quality, and delineation of the small arteries were compared between HR CTA and NR CTA.ResultsHR CTA showed significantly higher maximum CT value, higher image quality, and better delineation of the small arteries than did NR CTA (P < .005).ConclusionHR CTA using a UHR CT scanner showed higher image quality than NR CTA and enhanced the delineation of visceral arteries.

Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.

This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner. The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance. The HRDLR visual score was better than the NRHIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001). Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.

Location and direction dependence in the 3D MTF for a high-resolution CT system.

The purpose of this study was to quantify location and direction-dependent variations in the 3D modulation transfer function (MTF) of a high-resolution CT scanner with selectable focal spot sizes and resolution modes. The Aquilion Precision CT scanner (Canon Medical Systems) has selectable 0.25mm or 0.5mm detectors (by binning) in both the axial (x-y) and detector array width (z) directions. For the x-y and z orientations, detectors are configured (x-y)=0.5 mm/(z)=0.5mm for normal resolution (NR), 0.25/0.5mm for high resolution (HR), and 0.25/0.25mm for super high resolution (SHR). Six focal spots (FS1-FS6) range in size from 0.4 (x-y)×0.5mm (z) for FS1 to 1.6×1.4mm for FS6. Phantoms fabricated from spherical objects were positioned at radial distances of 0, 4.0, 7.5, 11.0, 14.5, and 18.5cm. Axial and helical acquisitions were utilized and reconstructed using filtered back projection with the FC18 "Body," FC30 "Bone," and FC81 "Bone Sharp" kernels. The reconstructions were used to measure a 1D slice of the 3D MTF by oversampling the 3D ESF in the axial plane [MTF(fr ); φ=0°)], 45° out of the axial plane [MTF(fr ); φ=45°)], in the longitudinal direction [MTF(fr ); φ=80°)], and along the radial and azimuthal directions within the axial plane. The MTF(fr ); φ=45°) drops to 10% (f10 ) at 1.20, 1.45, and 2.06mm-1 for NR, HR, and SHR, respectively, for a helical acquisition with FS1, FC30, and r=4cm from the isocenter. The MTF(fr ); φ=45°) includes contributions of both the axial-plane MTF (f10 =1.10, 2.04, and 2.01mm-1 ) and the longitudinal MTF (f10 =1.17, 1.18, and 1.82mm-1 ) for the NR, HR, and SHR modes, respectively. For SHR, the axial scan mode showed a 15-25% improvement over helical mode in the longitudinal resolution. Helical pitch, ranging from 0.569 to 1.381, did not appreciably affect the 3D resolution (<2%). The radial MTFs across the axial field of view (FOV) showed dependencies on the focal spot length in z; for example, for SHR with FS2 (0.6×0.6mm), f10 at r=11cm was within 17% of the value at r=4cm, but for SHR with FS3 (0.6×1.3), the reduction in f10 was 46% from 4 to 11cm from the isocenter. The azimuthal MTF also decreased as r increased but less so for longer gantry rotation times and smaller focal spot dimensions in the axial plane. The longitudinal MTF was minimally affected (<11%) by position in the FOV and was principally affected by the focal spot length in the z-dimension. The 3D MTF was measured throughout the FOV of a high-resolution CT scanner, quantifying the advantages of different resolution modes and focal spot sizes on the axial-plane and longitudinal MTF. Reconstruction kernels were shown to impact axial-plane resolution, imparting non-isotropic 3D resolution characteristics. Focal spot size (both in x-y and in z) and gantry rotation time play important roles in preserving the high-resolution characteristics throughout the field of view for this new high-resolution CT scanner technology.

Deep learning-based reconstruction in ultra-high-resolution computed tomography: Can image noise caused by high definition detector and the miniaturization of matrix element size be improved?

This study aimed to assess the noise characteristics of ultra-high-resolution computed tomography (UHRCT) with deep learning-based reconstruction (DLR). Two different diameters of water phantom were scanned with three different resolution acquisition modes. Images were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (hybrid-IR), and DLR. Image noise analysis was performed with noise magnitude, peak frequency (fp) of the noise power spectrum (NPS), and the square root of the area under the curve (√AUCNPS) for the NPS curve. The noise magnitude was up to 3.30 times higher for the FBP acquired in SHR mode than that for the NR mode. The fp values of the FBP were 0.20-0.21, 0.34-0.36, and 0.34-0.37 cycles/mm for normal resolution (NR), high resolution (HR), and super high resolution (SHR) mode, respectively. The fp of hybrid-IR was 0.16-0.19, 0.21-0.26, and 0.23-0.26 cycles/mm for NR, HR, and SHR mode, respectively. The fp of DLR was 0.21-0.32 and 0.22-0.33 cycles/mm for HR and SHR mode, respectively. √AUCNPS showed that the highest value in FBP images of the SHR mode was up to 1.89 times that of the NR mode. DLR in the HR and SHR modes showed high noise reduction while suppressing fp shift with respect to FBP. The new DLR algorithm could be a solution to the noise increase due to the high-definition detector elements and the small reconstruction matrix element size.

Validation of synthesized normal-resolution image data generated from high-resolution acquisitions on a commercial CT scanner.

To validate a normal-resolution (NR) simulation (NRsim) algorithm that uses high-resolution (HR) or super-high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim-generated images and actual NR images. NRsim is intended to allow direct comparison between normal-resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams. The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x-y) and row (z) directions. For NR, each detector element is 0.5mm×0.5mm along the channel and row directions, 0.25mm×0.5mm for HR, and 0.25mm×0.25mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NRHR and NRSHR , respectively) by downsampling the pre-log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three-dimensional noise power spectrum (NPS) was measured in water-equivalent phantoms for standard protocols across a range of size-specific dose estimates (SSDE): head (6.2-29.8mGy), lung (2.2-18.2mGy), and body (5.6-19.4mGy). The MTF and NPS measurements were combined to estimate low-contrast detectability (LCD) using a non-prewhitening model observer with an eye filter for a 5-mm disk with 10HU contrast. All metrics were compared for NR, NRHR , and NRSHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just-noticeable difference in image noise level for human observers is typically <15%. The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2HU of NR. LCD for NRsim was within 15% of NR with the exception of NRSHR for the lung protocol SSDE levels below 3.7mGy with FBP. NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD largely equivalent to scans acquired using an actual NR acquisition. At SSDE levels below ~5mGy for the lung protocol, differences in noise magnitude and LCD for NRSHR were >15% which defines a region where NRsim degrades due to contributions from electronic noise.

Application of a Novel Ultra-High Resolution Multi-Detector CT in Quantitative Imaging of Trabecular Microstructure.

To evaluate the performance of a novel ultra-high resolution multi-detector CT scanner (Canon Aquilion Precision UHR CT), capable of visualizing ~150 μm details, in quantitative assessment of bone microarchitecture. Compared to conventional CT, the spatial resolution of UHR CT begins to approach the size of the trabeculae. This might enable measurements of microstructural correlates of osteoporosis, osteoarthritis, and other bone disease. The UHR CT system features a 160-row x-ray detector with 250×250 μm pixels (measured at isocenter) and a custom-designed x-ray source with a 0.4×0.5 mm focal spot. Visualization of high contrast details down to ~150 μm has been achieved on this device, which is now commercially available for clinical use. To evaluate the performance of UHR CT in quantification of bone microstructure, we imaged a variety of human bone samples (including ulna, radius, and vertebrae) embedded in a ~16 cm diameter plastic cylinder and in an anthropomorphic thorax phantom (QRM-Thorax, QRM Gmbh). Helical UHR CT acquisitions (120 kVp tube voltage) were acquired at scan exposures of 375 mAs - 5 mAs. For comparison, the samples were also imaged using a Normal Resolution (NR) mode available on the scanner, involving 500 μm slice thickness, exposure of 50 mAs, and a focal spot of 0.6×1.3 mm. We obtained micro-CT (μCT) of the bone samples at ~28 μm voxel size as a gold-standard reference. Geometric measurements of bone microstructure were performed in 17 regions-of-interests (ROIs) distributed throughout the bones of the phantoms; image registration was used to place the ROIs at corresponding locations in the UHR CT and NR CT. Trabecular thickness Tb.Th, spacing Tb.Sp, and Bone Volume fraction BvTv were obtained. The UHR and NR imaging protocols were compared terms of correlations to μCT and error of trabecular measurements. The effect of dose on trabecular morphometry was also studied for the UHR CT. Furthermore, we evaluated the sensitivity of texture features of trabecular bone (recently proposed as an alternative to geometric indices of microstructure) to imaging protocol. Image texture evaluation was performed using ~150 regions of interest (ROIs) across all bone samples. Three-dimensional Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) features were extracted for each ROI. We analyzed correlation and concordance correlation coefficient (CCC) of the mean ROI values of texture features obtained using the UHR and NR modes. UHR CT reconstructions of bone samples clearly demonstrated improved visualization of the trabeculae compared to NR CT. UHR CT achieved substantially better correlations for all three metrics of bone microstructure, in particular for BvTv (correlation coefficient of 0.91 for UHR CT compared to 0.84 for NR CT) and TbSp (correlation of 0.74 for UHR CT and 0.047 for NR CT). The error obtained with UHR CT was generally smaller than that of NR CT. For TbSp, the mean deviation from μCT (averaged across all bone samples) was only ~0.07 for UHR CT, compared to 0.25 for NR CT. Analysis of reproducibility of texture features of trabecular bone between UHR CT and NR CT revealed fair correlations (>0.7) for the majority of GLCM features, but relatively poor CCC (e.g. 0.02 for Energy and 0.04 for Entropy). The magnitude of texture metrics is particularly affected by the enhanced spatial resolution of UHR CT. The recently introduced UHR CT achieves improved correlation and reduced error in measurements of trabecular bone microstructure compared to conventional resolution CT. Future development of diagnostic strategies based on textural biomarkers derived from UHR CT will need to account for potential sensitivity of texture features to image resolution.

Physical evaluation of an ultra-high-resolution CT scanner

ObjectivesTo evaluate the technical performance of an ultra-high-resolution CT (UHRCT) system.MethodsThe physico-technical capabilities of a novel commercial UHRCT system were assessed and compared with those of a current-generation multi-detector (MDCT) system. The super-high-resolution (SHR) mode of the system uses 0.25 mm (at isocentre) detector elements (dels) in the in-plane and longitudinal directions, while the high-resolution (HR) mode bins two dels in the longitudinal direction. The normal-resolution (NR) mode bins dels 2 × 2, resulting in a del-size equivalent to that of the MDCT system. In general, standard procedures and phantoms were used to perform these assessments.ResultsThe UHRCT MTF (10% MTF 4.1 lp/mm) is twice as high as that of the MDCT (10% MTF 1.9 lp/mm), which is comparable to the MTF in the NR mode (10% MTF 1.7 lp/mm). The width of the slice sensitivity profile in the SHR mode (FWHM 0.45 mm) is about 60% of that of the MDCT (FWHM 0.77 mm). Uniformity and CT numbers are within the expected range. Noise in the high-resolution modes has a higher magnitude and higher frequency components compared with MDCT. Low-contrast visibility is lower for the NR, HR and SHR modes compared with MDCT, but about a 14%, for NR, and 23%, for HR and SHR, dose increase gives the same results.ConclusionsHR and SHR mode scanning results in double the spatial resolution, with about a 23% increase in dose required to achieve the same low-contrast detectability.Key Points• Resolution on UHRCT is up to twice as high as for the tested MDCT.• With abdominal settings, UHRCT needs higher dose for the same low-contrast detectability as MDCT, but dose is still below achievable levels as defined by current diagnostic reference levels.• The UHRCT system used in normal-resolution mode yields comparable resolution and noise characteristics as the MDCT system.

Improvement of Image Quality in the Axial Section Using High-resolution Scan Mode and Hybrid Iterative Reconstruction in Ultra-high-resolution Computed Tomography

The purpose of this study is to compare the physical characteristics and visibility of high-resolution and conventional images acquired with the same X-ray dose, and to investigate the superiority of super high-resolution imaging. A Catphan phantom was scanned in the normal resolution (NR), high-resolution (HR), and super high-resolution (SHR) modes of ultra-high-resolution computed tomography at 120 kV and 75 mAs. All images were reconstructed into a 5-mm thick image slices with filtered back-projection (FBP) and hybrid image reconstruction (HIR), which included normal and enhanced adaptive iterative dose reduction 3D (AIDR and eAIDR, respectively). The modulation transfer function (MTF) and noise power spectrum (NPS) were measured using the circular edge method and radial frequency method, respectively. The signal-to-noise ratio (SNR) was then calculated. High-contrast resolution and low-contrast detectability were evaluated visually by five radiological technologists. The MTFs of HReAIDR and HRFBP images were higher than those of NRFBP images. However, the NPSs of HReAIDR and HRFBP images were larger than those of NRFBP images. The SNR of HReAIDR images was higher than that of NRFBP and HRFBP images. The scores of high-contrast resolution of HReAIDR, NRFBP, and HRFBP images were 13, 8, and 13 cycles/cm, respectively, and the scores of low-contrast detectability were 5, 5, and 6 mm, respectively. Hence, an improvement in high-contrast resolution of signal more than 400 HU in the axial section can be achieved without increasing the radiation dose and decreasing low-contrast detectability with 10 HU using the HR mode and eAIDR.