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BACKGROUND: Interventional magnetic resonance imaging (MRI) can provide a comprehensive setting for microwave ablation of tumors with real-time monitoring of the energy delivery using MRI-based temperature mapping. The purpose of this study was to quantify the accuracy of three-dimensional (3D) real-time MRI temperature mapping during microwave heating in vitro by comparing MRI thermometry data to reference data measured by fiber-optical thermometry. METHODS: Nine phantom experiments were evaluated in agar-based gel phantoms using an in-room MR-conditional microwave system and MRI thermometry. MRI measurements were performed for 700 s (25 slices; temporal resolution 2 s). The temperature was monitored with two fiber-optical temperature sensors approximately 5 mm and 10 mm distant from the microwave antenna. Temperature curves of the sensors were compared to MRI temperature data of single-voxel regions of interest (ROIs) at the sensor tips; the accuracy of MRI thermometry was assessed as the root-mean-squared (RMS)-averaged temperature difference. Eighteen neighboring voxels around the original ROI were also evaluated and the voxel with the smallest temperature difference was additionally selected for further evaluation. RESULTS: The maximum temperature changes measured by the fiber-optical sensors ranged from 7.3 K to 50.7 K. The median RMS-averaged temperature differences in the originally selected voxels ranged from 1.4 K to 3.4 K. When evaluating the minimum-difference voxel from the neighborhood, the temperature differences ranged from 0.5 K to 0.9 K. The microwave antenna and the MRI-conditional in-room microwave generator did not induce relevant radiofrequency artifacts. CONCLUSION: Accurate 3D real-time MRI temperature mapping during microwave heating with very low RMS-averaged temperature errors below 1 K is feasible in gel phantoms. RELEVANCE STATEMENT: Accurate MRI-based volumetric real-time monitoring of temperature distribution and thermal dose is highly relevant in clinical MRI-based interventions and can be expected to improve local tumor control, as well as procedural safety by extending the limits of thermal (e.g., microwave) ablation of tumors in the liver and in other organs. KEY POINTS: Interventional MRI can provide a comprehensive setting for the microwave ablation of tumors. MRI can monitor the microwave ablation using real-time MRI-based temperature mapping. 3D real-time MRI temperature mapping during microwave heating is feasible. Measured temperature errors were below 1 °C in gel phantoms. The active in-room microwave generator did not induce any relevant radiofrequency artifacts.

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BACKGROUND: We compared ultra-high resolution (UHR), standard resolution (SR), and virtual non-calcium (VNCa) reconstruction for coronary artery stenosis evaluation using photon-counting computed tomography (PC-CT). METHODS: One vessel phantom (4-mm diameter) containing solid calcified lesions with 25% and 50% stenoses inside a thorax phantom with motion simulation underwent PC-CT using UHR (0.2-mm slice thickness) and SR (0.6-mm slice thickness) at heart rates of 60 beats per minute (bpm), 80 bpm, and 100 bpm. A paired t-test or Wilcoxon test with Bonferroni correction was used. RESULTS: For 50% stenosis, differences in percent mean diameter stenosis between UHR and SR at 60 bpm (51.0 vs 60.3), 80 bpm (51.7 vs 59.6), and 100 bpm (53.7 vs 59.0) (p ≤ 0.011), as well as between VNCa and SR at 60 bpm (50.6 vs 60.3), 80 bpm (51.5 vs 59.6), and 100 bpm (53.7 vs 59.0) were significant (p ≤ 0.011), while differences between UHR and VNCa at all heart rates (p ≥ 0.327) were not significant. For 25% stenosis, differences between UHR and SR at 60 bpm (28.0 vs 33.7), 80 bpm (28.4 vs 34.3), and VNCa vs SR at 60 bpm (29.1 vs 33.7) were significant (p ≤ 0.015), while differences for UHR vs SR at 100 bpm (29.9 vs 34.0), as well as for VNCa vs SR at 80 bpm (30.7 vs 34.3) and 100 bpm (33.1 vs 34.0) were not significant (p ≥ 0.028). CONCLUSION: Stenosis quantification accuracy with PC-CT improved using either UHR acquisition or VNCa reconstruction. RELEVANCE STATEMENT: PC-CT offers to scan with UHR mode and the reconstruction of VNCa images both of them could provide improved coronary stenosis quantification at increased heart rates, allowing a more accurate stenosis grading at low and high heart rates compared to SR. KEY POINTS: Evaluation of coronary stenosis with conventional CT is challenging at high heart rates. PC-CT allows for scanning with ECG-gated UHR and SR modes. UHR and VNCa images were compared in a dynamic phantom. UHR improves stenosis quantification up to 100 bpm. VNCa reconstruction improves stenosis evaluation up to 80 bpm.

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OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. MATERIALS AND METHODS: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. RESULTS: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. CONCLUSION: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

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BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS: Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS: DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT: Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS: Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

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OBJECTIVES: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. METHODS: A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS: The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. CONCLUSION: The radiomics features were non-reproducible with significant variability in values among different CT techniques. CLINICAL RELEVANCE STATEMENT: Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. KEY POINTS: CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

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BACKGROUND: Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations. METHODS: A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI). RESULTS: The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94). CONCLUSIONS: SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI. RELEVANCE STATEMENT: This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis. KEY POINTS: • Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.

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BACKGROUND: Dual-energy computed tomography (DECT) is useful for detecting gouty tophi. While iodinated contrast media (ICM) might enhance the detection of monosodium urate crystals (MSU), higher iodine concentrations hamper their detection. Calculating virtual noncontrast (VNC) images might improve the detection of enhancing tophi. The aim of this study was to evaluate MSU detection with VNC images from DECT acquisitions in phantoms, compared against the results with standard DECT reconstructions. METHODS: A grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were scanned with sequential single-source DECT using an ascending order of tube current time product at 80 kVp (16.5-220 mAs) and 135 kVp (2.75-19.25 mAs). VNC images were equivalently reconstructed at 80 and 135 kVp. Two-material decomposition analysis for MSU detection was applied for the VNC and conventional CT images. MSU detection and attenuation values were compared in both modalities. RESULTS: For 0, 0.25, 0.5, 1, and 2% ICM, the average detection indices (DIs) for all MSU concentrations (35-50%) with VNC postprocessing were respectively 25.2, 36.6, 30.9, 38.9, and 45.8% for the grid phantom scans and 11.7, 9.4, 5.5, 24.0, and 25.0% for the porcine phantom scans. In the conventional CT image group, the average DIs were respectively 35.4, 54.3, 45.4, 1.0, and 0.0% for the grid phantom and 19.4, 17.9, 3.0, 0.0, and 0.0% for the porcine phantom scans. CONCLUSIONS: VNC effectively reduces the suppression of information caused by high concentrations of ICM, thereby improving the detection of MSU. RELEVANCE STATEMENT: Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition. KEY POINTS: • Highly concentrated contrast media hinders monosodium urate crystal detection in CT imaging • Virtual noncontrast imaging redetects monosodium urate crystals in high-iodinated contrast media concentrations. • Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.

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BACKGROUND: Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. METHODS: Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers' strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). RESULTS: For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772-1.668) for DM, 1.257 mGy (0.971-1.863) for DBT, 1.280 mGy (0.937-1.878) for LE-CEM, and 0.630 mGy (0.397-0.713) for HE-CEM. Medians CNRs were 14.2 (7.8-20.2) for DM, 4.91 (2.58-7.20) for a single projection in DBT, 11.9 (8.0-18.2) for LE-CEM, and 5.2 (3.6-9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. CONCLUSIONS: The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers' strategies, with potential implications in radiation dose and image quality in clinical settings. RELEVANCE STATEMENT: The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. KEY POINTS: • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the "optimal" exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies.

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BACKGROUND: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. METHODS: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. RESULTS: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. CONCLUSIONS: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. RELEVANCE STATEMENT: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. KEY POINTS: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

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Virtual mono-energetic images (VMI) using dual-layer computed tomography (DLCT) enable substantial contrast medium (CM) reductions. However, the combined impact of patient size, tube voltage, and heart rate (HR) on VMI of coronary CT angiography (CCTA) remains unknown. This phantom study aimed to assess VMI levels achieving comparable contrast-to-noise ratio (CNR) in CCTA at 50% CM dose across varying tube voltages, patient sizes, and HR, compared to the reference protocol (100% CM dose, conventional at 120 kVp). A 5 mm artificial coronary artery with 100% (400 HU) and 50% (200 HU) iodine CM-dose was positioned centrally in an anthropomorphic thorax phantom. Horizontal coronary movement was matched to HR (at 0, < 60, 60-75, > 75 bpm), with varying patient sizes simulated using phantom extension rings. Raw data was acquired using a clinical CCTA protocol at 120 and 140 kVp (five repetitions). VMI images (40-70 keV, 5 keV steps) were then reconstructed; non-overlapping 95% CNR confidence intervals indicated significant differences from the reference. Higher CM-dose, reduced VMI, slower HR, higher tube voltage, and smaller patient sizes demonstrated a trend of higher CNR. Regardless of HR, patient size, and tube voltage, no significant CNR differences were found compared to the reference, with 100% CM dose at 60 keV, or 50% CM dose at 40 keV. DLCT reconstructions at 40 keV from 120 to 140 kVp acquisitions facilitate 50% CM dose reduction for various patient sizes and HR with equivalent CNR to conventional CCTA at 100% CM dose, although clinical validation is needed.

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BACKGROUND: Photon-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets. METHODS: An organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients' left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test. RESULTS: There was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%). CONCLUSIONS: VMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV. RELEVANCE STATEMENT: Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation. KEY POINTS: • Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.

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Background and Objectives: We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Materials and Methods: Three 5 × 5 × 5 mm3 uric acid stones were placed in a physical human phantom in various locations. Three tube voltages (120, 100, and 80 kV) and four current-time products (100, 70, 30, and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with filtered back projection (FBP), statistical iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR). By applying deep learning to each image, we took 12 more scans. Objective image assessments were calculated using the standard deviation of the Hounsfield unit (HU). Subjective image assessments were performed by one radiologist and one urologist. Two radiologists assessed the subjective assessment and found the stone under the absence of information. We used this data to calculate the diagnostic accuracy. Results: Objective image noise was decreased after applying a deep learning tool in all images of FBP, iDose, and IMR. There was no statistical difference between iDose and deep learning-applied FBP images (10.1 ± 11.9, 9.5 ± 18.5 HU, p = 0.583, respectively). At a 100 kV-30 mAs setting, deep learning-applied FBP obtained a similar objective noise in approximately one third of the radiation doses compared to FBP. In radiation doses with settings lower than 100 kV-30 mAs, the subject image assessment (image quality, confidence level, and noise) showed deteriorated scores. Diagnostic accuracy was increased when the deep learning setting was lower than 100 kV-30 mAs, except for at 80 kV-15 mAs. Conclusions: At the setting of 100 kV-30 mAs or higher, deep learning-applied FBP did not differ in image quality compared to IR. At the setting of 100 kV-30 mAs, the radiation dose can decrease by about one third while maintaining objective noise.

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BACKGROUND: To investigate the influence of iodinated contrast medium (ICM) on detection of monosodium urate (MSU) with dual-energy computed tomography (DECT) in two types of phantoms and demonstrate an example patient for clinical illustration. METHODS: Approval is by the institutional review board, and written informed consent was obtained. A grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were prepared and scanned with sequential single-source DECT using established methodology. Ascending orders of tube currents were applied at 80 kVp (16.5 to 220.0 mAs) and 135 kVp (2.75 to 19.25 mAs). Volume and mass measurements were performed using clinical gout software (dual-energy decomposition analysis). Numbers of true-positive and false-positive MSU detections were recorded and compared for different ICM concentrations. We demonstrate a patient with gouty arthritis for clinical illustration. RESULTS: Effects of ICM on MSU detection varied with the amount of iodine. Lower ICM concentrations (0.25 and 0.50%) improved detection of small uric acid concentrations of 35 to 45% in comparison to scans without ICM. However, high ICM concentrations (1 and 2%) almost completely precluded MSU detection for all MSU concentrations investigated. In a patient with gouty arthritis, tophi in the wrist were only detected after intravenous ICM administration. CONCLUSIONS: Exploring multimodal DECT for arthritis imaging, enhancement of ICM influences tophus detection. It can help in visualizing previously undetected MSU depositions but, with too strong enhancement, also obscure tophi. RELEVANCE STATEMENT: Use of iodinated contrast media in dual-energy CT might help in visualizing previously undetected uric acid depositions but, with too strong enhancement, obscure gouty tophi. KEY POINTS: • Iodine significantly influences the uric acid crystal detection in systematic phantom studies. • Lower iodine concentrations improved detection of low and medium uric acid concentrations. • High concentrations of iodine hampered detection of all uric acid concentrations.

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BACKGROUND: Artifacts caused by metal implants are challenging when undertaking computed tomography (CT). Dedicated algorithms have shown promising results although with limitations. Tin filtration (Sn) in combination with high tube voltage also shows promise but with limitations. There is a need to examine these limitations in more detail. The purpose of this study was to investigate the impact of different metal artefact reduction (MAR) algorithms, tin filtration, and ultra-high-resolution (UHR) scanning, alone or in different combinations in both phantom and clinical settings. METHODS: An ethically approved clinical and phantom study was conducted. A modified Catphan® phantom with titanium and stainless-steel inserts was scanned with six different MAR protocols with tube voltage ranging from 80 to 150 kVp. Other scan parameters were kept identical. The differences (∆) in mean HU and standard deviation (SD) in images, with and without metal, were measured and compared. In the clinical study, three independent readers performed visual image quality assessments on eight different protocols using retrospectively acquired images. RESULTS: Iterative MAR had the lowest ∆HU and ∆SD in the phantom study. For images of the forearm, the soft tissue noise for Sn-based 150-kVp UHR protocol with was significantly higher (p = 0.037) than for single-energy MAR protocols. All Sn-based 150-kVp protocols were rated significantly higher (p < 0.046 than the single-energy MAR protocols in the visual assessment. CONCLUSIONS: All Sn-based 150-kVp UHR protocols showed similar objective MAR in the phantom study, and higher objective MAR and significantly improved visual image quality than single-energy MAR. RELEVANCE STATEMENT: Images with less metal artifacts and higher visual image quality may be more clinically optimal in CT examination of musculoskeletal patients with metal implants. KEY POINTS: • Metal artifact reduction algorithms and Sn filter combined with high kVp reduce artifacts. • Metal artifact reduction algorithms introduce new artifacts in certain metals. • Sn-based protocols alone may be considered as low metal artifact protocols.

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Flow-related artifacts have been observed in highly accelerated T1-weighted contrast-enhanced wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid gradient-echo (MPRAGE) imaging and can lead to diagnostic uncertainty. We developed an optimized flow-mitigated Wave-CAIPI MPRAGE acquisition protocol to reduce these artifacts through testing in a custom-built flow phantom. In the phantom experiment, maximal flow artifact reduction was achieved with the combination of flow compensation gradients and radial reordered k-space acquisition and was included in the optimized sequence. Clinical evaluation of the optimized MPRAGE sequence was performed in 64 adult patients, who all underwent contrast-enhanced Wave-CAIPI MPRAGE imaging without flow-compensation and with optimized flow-compensation parameters. All images were evaluated for the presence of flow-related artifacts, signal-to-noise ratio (SNR), gray-white matter contrast, enhancing lesion contrast, and image sharpness on a 3-point Likert scale. In the 64 cases, the optimized flow mitigation protocol reduced flow-related artifacts in 89% and 94% of the cases for raters 1 and 2, respectively. SNR, gray-white matter contrast, enhancing lesion contrast, and image sharpness were rated as equivalent for standard and flow-mitigated Wave-CAIPI MPRAGE in all subjects. The optimized flow mitigation protocol successfully reduced the presence of flow-related artifacts in the majority of cases.Relevance statementAs accelerated MRI using novel encoding schemes become increasingly adopted in clinical practice, our work highlights the need to recognize and develop strategies to minimize the presence of unexpected artifacts and reduction in image quality as potential compromises to achieving short scan times.Key points• Flow-mitigation technique led to an 89-94% decrease in flow-related artifacts.• Image quality, signal-to-noise ratio, enhancing lesion conspicuity, and image sharpness were preserved with the flow mitigation technique.• Flow mitigation reduced diagnostic uncertainty in cases where flow-related artifacts mimicked enhancing lesions.

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PURPOSE: To develop an isotropic three-dimensional (3D) T2 mapping technique for the quantitative assessment of the composition of knee cartilage with high accuracy and precision. METHODS: A T2-prepared water-selective isotropic 3D gradient-echo pulse sequence was used to generate four images at 3 T. These were used for three T2 map reconstructions: standard images with an analytical T2 fit (AnT2Fit); standard images with a dictionary-based T2 fit (DictT2Fit); and patch-based-denoised images with a dictionary-based T2 fit (DenDictT2Fit). The accuracy of the three techniques was first optimized in a phantom study against spin-echo imaging, after which knee cartilage T2 values and coefficients of variation (CoV) were assessed in ten subjects in order to establish accuracy and precision in vivo. Data given as mean ± standard deviation. RESULTS: After optimization in the phantom, whole-knee cartilage T2 values of the healthy volunteers were 26.6 ± 1.6 ms (AnT2Fit), 42.8 ± 1.8 ms (DictT2Fit, p < 0.001 versus AnT2Fit), and 40.4 ± 1.7 ms (DenDictT2Fit, p = 0.009 versus DictT2Fit). The whole-knee T2 CoV reduced from 51.5% ± 5.6% to 30.5 ± 2.4 and finally to 13.1 ± 1.3%, respectively (p < 0.001 between all). The DictT2Fit improved the data reconstruction time: 48.7 ± 11.3 min (AnT2Fit) versus 7.3 ± 0.7 min (DictT2Fit, p < 0.001). Very small focal lesions were observed in maps generated with DenDictT2Fit. CONCLUSIONS: Improved accuracy and precision for isotropic 3D T2 mapping of knee cartilage were demonstrated by using patch-based image denoising and dictionary-based reconstruction. KEY POINTS: • Dictionary T2 fitting improves the accuracy of three-dimensional (3D) knee T2 mapping. • Patch-based denoising results in high precision in 3D knee T2 mapping. • Isotropic 3D knee T2 mapping enables the visualization of small anatomical details.

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BACKGROUND: In this study, stent appearance in a novel silicon-based photon-counting computed tomography (Si-PCCT) prototype was compared with a conventional energy-integrating detector CT (EIDCT) system. METHODS: An ex vivo phantom was created, consisting of a 2% agar-water mixture, in which human-resected and stented arteries were individually embedded. Using similar technique parameters, helical scan data was acquired using a novel prototype Si-PCCT and a conventional EIDCT system at a volumetric CT dose index (CTDIvol) of 9 mGy. Reconstructions were made at 502 and 1502 mm2 field-of-views (FOVs) using a bone kernel and adaptive statistical iterative reconstruction with 0% blending. Using a 5-point Likert scale, reader evaluations were performed on stent appearance, blooming and inter-stent visibility. Quantitative image analysis was performed on stent diameter accuracy, blooming and inter-stent distinction. Qualitative and quantitative differences between Si-PCCT and EIDCT systems were tested with a Wilcoxon signed-rank test and a paired samples t-test, respectively. Inter- and intra-reader agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: Qualitatively, Si-PCCT images were rated higher than EIDCT images at 150-mm FOV, based on stent appearance (p = 0.026) and blooming (p = 0.015), with a moderate inter- (ICC = 0.50) and intra-reader (ICC = 0.60) agreement. Quantitatively, Si-PCCT yielded more accurate diameter measurements (p = 0.001), reduced blooming (p < 0.001) and improved inter-stent distinction (p < 0.001). Similar trends were observed for the images reconstructed at 50-mm FOV. CONCLUSIONS: When compared to EIDCT, the improved spatial resolution of Si-PCCT yields enhanced stent appearance, more accurate diameter measurements, reduced blooming and improved inter-stent distinction. KEY POINTS: • This study evaluated stent appearance in a novel silicon-based photon-counting computed tomography (Si-PCCT) prototype. • Compared to standard CT, Si-PCCT resulted in more accurate stent diameter measurements. • Si-PCCT also reduced blooming artefacts and improved inter-stent visibility.

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BACKGROUND: To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. METHODS: Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF50) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL. RESULTS: The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF50 was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1. CONCLUSIONS: The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

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OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS: The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION: DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS: • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

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Purpose: The objective of this study was to propose a method for developing a clinical phantom to reproduce various diseases that are clinically prevalent in the field of dentistry. This could facilitate diverse clinical research without unnecessarily exposing patients to radiation. Material and Methods: This study utilized a single dry skull, which was visually and radiographically examined to evaluate its condition. Existing lesions on the dry skull were preserved, and other relevant lesions were artificially created as necessary. These lesions were then documented using intraoral radiography and cone-beam computed tomography. Once all pre-existing and reproduced lesions were confirmed by the consensus of 2 oral and maxillofacial radiologists, the skull was embedded in a soft tissue substitute. To validate the process, cone-beam computed tomography scans and panoramic radiographs were obtained of the fabricated phantom. All acquired images were subsequently evaluated. Results: Most lesions could be identified on panoramic radiographs, although some sialoliths and cracked teeth were confirmed only through cone-beam computed tomographic images. A small gap was observed between the epoxy resin and the bone structures. However, 2 oral and maxillofacial radiologists agreed that this space did not meaningfully impact the interpretation process. Conclusion: The newly developed phantom has potential for use as a standardized phantom within the dental field. It may be utilized for a variety of imaging studies, not only for optimization purposes, but also for addressing other experimental issues related to both 2- and 3-dimensional diagnostic radiography.