HU Values Research Articles

Pseudo CT Images Synthesized from CBCT Based on Unsupervised DDPM in Cervical Cancer Radiotherapy

The low image quality and inaccurate HU value of cone beam computed tomography (CBCT) restrict its further application in cervical cancer radiotherapy. A new unsupervised based denoising diffusion probabilistic model (DDPM) was proposed to synthesize pseudo-CT images from CBCT. CBCT and CT images of 120 patients with cervical cancer were selected. The proposed DDPM network with condition and iterative mechanism was used for data training and testing between two image domains. In the training process, the model first obtained coarse pseudo-CT images with Gaussian noise through a diffusion process. Then, with the real CT images as the training target, the noise images were nonlinearly mapped to the domain of the CT images through the inverse diffusion process, and the fine pseudo-CT images were obtained. We repeated the above steps and gradually increased the number of noise diffusion. When the image difference value is less than the threshold, the training of the model is terminated and the pseudo-CT images are output. In the testing stage, each pseudo-CT generated was compared against the real CT image of the same patient based on the metrics of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and normalized mean absolute error (NMAE). In terms of anatomical verification, the PSNR (dB), SSIM (%) and NCC values between the pseudo-CT images obtained based on DDPM and the real CT images were presented as mean (standard deviation), which were 31.92(0.46), 86.69(4.55) and 0.0106(0.002) respectively. Compared with CBCT, the accuracy of the three metric values has been improved 15.6%, 14.2% and 23.3% respectively. For the metric values of the pseudo-CT images obtained based on the U-Net and CycleGAN models, the results synthesized based on the proposed model were paired with T-tests, the p values were all less than 0.05, and the differences were statistically significant. The pseudo-CT images obtained based on the DDPM were close to the real CT images in anatomy. The pseudo-CT images synthesized by the proposed DDPM network have good application prospects in cervical cancer radiotherapy.

Spectral photon-counting CT: Image quality evaluation using a metal-containing bovine bone specimen

PurposeTo find the optimal imaging parameters for a photon-counting detector CT (PCD-CT) and to compare it to an energy-integrating detector CT (EID-CT) in terms of image quality and metal artefact severity using a metal-containing bovine knee specimen. MethodsA bovine knee with a stainless-steel plate and screws was imaged in a whole-body research PCD-CT at 120 kV and 140 kV and in an EID dual-source CT (DSCT) at Sn150 kV and 80/Sn150 kV. PCD-CT virtual monoenergetic 72 and 150 keV images and EID-CT images processed with and without metal artefact reduction algorithms (iMAR) were compared. Four radiologists rated the visualisation of bony structures and metal artefact severity. The Friedman test and Wilcoxon signed-rank test with Bonferroni’s correction were used. P-values of ≤ 0.0001 were considered statistically significant. Distributions of HU values of regions of interest (ROIs) in artefact-affected areas were analysed. ResultsPCD-CT 140 kV 150 keV images received the highest scores and were significantly better than EID-CT Sn150 kV images. PCD-CT 72 keV images were rated significantly lower than all the others. HU-value variation was larger in the 120 kV and the 72 keV images. The ROI analysis revealed no large difference between scanners regarding artefact severity. ConclusionPCD-CT 140 kV 150 keV images of a metal-containing bovine knee specimen provided the best image quality. They were superior to, or as good as, the best EID-CT images; even without the presumed advantage of tin filter and metal artefact reduction algorithms. PCD-CT is a promising method for reducing metal artefacts.

Influence of scanning speed on the microstructure, nanoindentation characteristics and tribological behavior of novel maraging steel coatings by laser cladding

This paper utilized a novel high-Mo maraging steel powder to prepare maraging steel coatings through laser cladding. The study investigated how scanning speed affects the microstructure, phase formation, nanoindentation properties, and wear properties of the resulting laser-clad maraging steel coatings. The experimental results reveal that the maraging steel coating consists of martensite and austenite phases. Moreover, diffraction peaks of Fe2Mo phase were observed at low scanning speeds. As the scanning speed increases, there is a gradual reduction in the austenite content, accompanied by a decrease in the Fe2Mo phase content. Moreover, both martensite and austenite exhibit an increase in lattice distortion. EBSD analysis demonstrates a reduce in grain size as the scanning speed increases, accompanied by the emergence of LAGBs and an increase in dislocation density, as indicated by the rising KAM value. TEM analysis confirms the presence of a Mo-rich precipitated phase, characterized by a dense hexagonal structure consisting of the Fe2Mo intermetallic compound. The Fe2Mo phase displays high hardness, thereby significantly enhancing the second precipitation strengthening effect of the coating. The nanoindentation characteristics of the S2 coating are exceptional, displaying remarkable values for nanohardness, HU, H/E ratio, and H3/E2 ratio. Specifically, the S2 coating exhibits a nanohardness of 4.843 ± 0.104 GPa, accompanied by an HU value of 0.2073 GPa. Additionally, the H/E ratio and H3/E2 ratio are reported as 0.0214 and 0.002218, respectively. Demonstrates excellent mechanical properties of S2 coating. Furthermore, the S2 coating demonstrates outstanding performance in terms of wear resistance and deformation resistance, exhibiting the lowest friction coefficient and specific wear rate, measuring at 0.438 and 1.381 × 10−5 mm3/N•m, respectively. The observed wear mechanisms in the S2 coating include adhesive wear, abrasive wear and oxidative wear. Notably, lower scanning speeds promote the precipitation of Fe2Mo particles, which play a supportive role in the wear process by weakening the tendency of oxidative wear. Overall, the remarkable properties exhibited by the S2 coating, such as nanohardness, wear resistance, and deformation resistance, position it as a promising candidate for various technological applications.

Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology.

In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.

Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging

BackgroundTo present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model.ResultsThe proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively.ConclusionThe cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.

Dosimetric characterization of foam padding with posterior fields in palliative radiation therapy

Patients undergoing external beam radiation therapy for the palliative treatment of painful bony metastases may have difficulty maintaining a still position on a rigid uncovered couch top, both during CT simulation as well as during patient setup, image guidance, and treatment on the linear accelerator. For these patients, a thin foam pad or mattress is sometimes used to mitigate patient discomfort. It was desired to quantify the effect of the padding in cases in which the patient is to be treated supine with posterior beams when the majority of the beam weighting traverses both the couch and the pad. Ion chamber measurements in-phantom were acquired with 6 MV, 10 MV, and 15 MV photon beams. At depths of maximum dose, the pad resulted in a difference of signal collected ≤1%. At the phantom surface, the pad resulted in an increase in signal ranging from 1% to 6.5% for the measured beams. CT data of the pad, both with and without applied pressure, indicated that the pad had average HU values close to air.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy

Background and objectiveCone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. MethodsThe planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. ResultsThe images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). ConclusionsThe proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

Evaluation of the feasibility of a multisource CBCT for maxillofacial imaging

Objective. The aim of this study was to investigate the feasibility of improving the image quality and accuracy of cone beam computed tomography (CBCT) by replacing the conventional wide cone angle x-ray tube with a distributed x-ray source array positioned in the axial direction. Approach. The multisource CBCT (ms-CBCT) design was experimentally simulated using a benchtop scanner with a carbon nanotube x-ray tube and a flat-panel detector. The source was collimated and translated in the axial direction to simulate a source array with a reduced cone angle for each beam. An adjacent scatter ratio subtraction (ASRS) method was implemented for residual scatter reduction. Several phantoms were imaged using the ms-CBCT and conventional CBCT configurations under otherwise similar conditions. The Requirements of the ms-CBCT design on the x-ray source and detector were evaluated. Main results. Compared to the conventional CBCT, the ms-CBCT design with 8 sources and ASRS significantly improved the image quality and accuracy, including: (1) reducing the cupping artifact from 15% to 3.5%; (2) reducing the spatial nonuniformity of the CT Hounsfield unit values from 38.0 to 9.2; (3) improving the contrast-to-noise ratio of the low contrast objects (acrylic and low density polyethylene inserts) against the water-equivalent background by ∼20% and (4) reducing the root-mean-square error of the HU values by 70%, from 420.1 to 124.4. The imaging dose and scanning time used by the current clinical CBCT for maxillofacial imaging can be achieved by current source and detector technologies. Significance. The ms-CBCT design significantly reduces the scatter and improves the image quality and accuracy compared to the conventional CBCT.

Dedicated virtual non-contrast images adapted for liver tissue in clinical photon counting CT improve virtual non-contrast imaging in various organs beyond the liver

PurposePurpose of this study is to re-evaluate the accuracy and diagnostic reliability of virtual non-contrast (VNC) images acquired with the photon-counting computed tomography (PCCT) after an update of the CT scanner software. MethodsFifty-four patients were retrospectively enrolled. VNC images were reconstructed from true non-contrast (TNC) images (VNCn) and contrast-enhanced images in portal venous contrast phase (VNCv). Additionally, a liver-specific VNC (VNCl) was assessed. Quantitative image properties of VNC and TNC images were compared and consistency between VNC images was evaluated. Regions of interest were drawn in the liver, spleen, renal cortex, aorta, muscle and subcutaneous fat. ResultsAttenuation values on all VNC images differed significantly from TNC images in the liver, renal cortex, aorta and fat. A mean offset of <10HU between TNC and all VNC images was found in the liver, spleen and muscle. The comparison of TNC and VNCl images revealed an offset < 10HU in fat. Differences ≤ 10HU between TNC and VNCv and between TNC and VNCl were found in 68%, respectively in 75%. Differences ≤ 15HU were found in 79%, respectively in 92% of all measurements. Differences ≤ 10HU between TNC and VNCn were found in 79% and differences ≤ 15HU in 85%. ConclusionAlthough there are statistically significant differences between HU values measured on TNC and VNC images in certain tissues, the minor offsets measured in liver and spleen suggest a good clinical applicability of VNCv and VNCl images. The significantly lower offset in subcutaneous fat on VNCl images suggests a superiority for measurements in adipose tissues.

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifact reduction (MAR) algorithm has been developed based on a deep learning Generative Adversarial Network called Cycle-MAR, and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology), and CCS-MAR (Combined Clustered Scan-based MAR). Cycle-MAR was trained with a supervised learning scheme using sets of paired clinical CT scans with and without simulated artifacts. It was then evaluated on CT scans with real artifacts of an anthropomorphic phantom, and on sets of clinical CT scans with simulated artifacts which were not used for Cycle-MAR training. Image quality metrics and HU value-based analysis were used to evaluate the performance of Cycle-MAR compared to the other algorithms. The proposed Cycle-MAR network effectively reduces the negative impact of the metal artifacts. For example, the calculated HU value improvement percentage for the cardiac structures in the clinical CT scans was 59.58%, 62.22%, and 72.84% after MDT, CCS-MAR, and Cycle-MAR application, respectively. The application of MAR algorithms reduces the impact of US transducer-induced metal artifacts on CT scans. In comparison to iMAR, O-MAR, MDT, and CCS-MAR, the application of developed Cycle-MAR network on CT scans performs better in reducing these metal artifacts.

Radiomics of spinal muscles: toward a radiological biomarker for allograft rejection in lung transplant

PurposeTo assess the role of muscle composition and radiomics in predicting allograft rejection in lung transplant.Material and methodsThe last available HRCT before surgery of lung transplant candidates referring to our tertiary center from January 2010 to February 2020 was retrospectively examined. Only scans with B30 kernel reconstructions and 1 mm slice thickness were included. One radiologist segmented the spinal muscles of each patient at the level of the 11th dorsal vertebra by an open-source software. The same software was used to extract Hu values and 72 radiomic features of first and second order. Factor analysis was applied to select highly correlating features and then their prognostic value for allograft rejection was investigated by logistic regression analysis (level of significance p < 0.05). In case of significant results, the diagnostic value of the model was computed by ROC curves.ResultsOverall 200 patients had a HRCT prior to the transplant but only 97 matched the inclusion criteria (29 women; mean age 50.4 ± 13 years old). Twenty-one patients showed allograft rejection. The following features were selected by the factor analysis: cluster prominence, Imc2, gray level non-uniformity normalized, median, kurtosis, gray level non-uniformity, and inverse variance. The radiomic-based model including also Hu demonstrated that only the feature Imc2 acts as a predictor of allograft rejection (p = 0.021). The model showed 76.6% accuracy and the Imc2 value of 0.19 demonstrated 81% sensitivity and 64.5% specificity in predicting lung transplant rejection.ConclusionThe radiomic feature Imc2 demonstrated to be a predictor of allograft rejection in lung transplant.

Prediction of Lumbar Pedicle Screw Loosening Using Hounsfield Units in Computed Tomography

One of the most common issues following pedicle screw fixation is pedicle screw loosening. There are, however, few trustworthy methods for predicting screw loosening. The goal of the current study was to identify an efficient technique for using preoperative CT scanning to predict screw loosening in older patients and to offer recommendations for preoperative surgical planning. The current analysis included retrospectively all patients who underwent lumbar pedicle screw fixation for degenerative lumbar diseases in our department between January 2015 and January 2022. Hounsfield units were used to assess each vertebra's attenuation in a CT scan (HU). Postoperative X-ray testing identified screw loosening. Using IBMSPSS 24.00 software, one-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were performed. Over a mean follow-up period of 28.4±11.5 (range 12-44 months) months, screw loosening was noted in 53 of 242 patients (136 male and 106 female, average age 58.7±7.3 years). Gender, BMI, smoking habits, and whether or not a patient had diabetes or spondylolisthesis were not shown to be significantly different among the patients (P>0.05). The difference between the average lumbar vertebral HU values in the screw-loosening group and the control group was significant (P<0.01) at 120.3±31.5HU and 138.6±37.6HU, respectively. The average HU value of L1-L4 exhibited an area under the curve (AUC) of 0.691 (95% CI: 0.614-0.784), according to ROC curve analysis. A HU cut-off value of 122 HU is a likely cut-off point to predict screw loosening with a sensitivity of 70% and a specificity of 58%. The use of screw augmentation techniques can be decided using a prospective CT scan HU value-based prediction. An independent risk factor for screw loosening in an instrumented lumbar vertebra is a cutoff L1-L5 average HU value of 122 HU.

Evaluation of automatic pericardial segmentation methods in computed tomography images

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Fundação para a Ciência e Tecnologia - FCT. Pericardial fat has been associated with the development of many cardiovascular diseases. Non-invasive pericardial fat assessment may be performed through cardiac computed tomography (CCT), with a first step being the segmentation of the pericardial layers, followed by the application of a threshold based on HU values. Given that manual pericardium segmentation is expensive, time-consuming and subject to inter-observer variability, automatic tools have been proposed. The purpose of this study is to compare automatic pericardial segmentation methods to manual segmentation by experts. Two automatic methods are compared: a commercial solution by Siemens and a deep learning based solution. A subset of 190 patients were randomly selected from the EPICHEART (The influence of EPICardial adipose tissue in HEART diseases) study (ClinicalTrials.gov: NCT03280433). All data was anonymized prior to analysis for the purposes of this study. All CCT scans were acquired on a Siemens Somatom Sensation 64 with a slice thickness of 3 mm. Manual pericardial segmentation was performed using 3D Slicer [1] by one of the authors (CS). Segmentation was repeated in a subset of 20 cases by CS and by a second observer (FN) to assess inter- and intra-observer variability. Automatic pericardial segmentation was performed using the Cardiac Risk Assessment tool provided within the Syngo.via software by Siemens Healthcare. A deep learning based segmentation tool was also developed. Publicly available CCT data with available pericardial segmentation was gathered: the CardiacFat dataset [2] which includes 20 patients acquired on two different scanners (Phillips and Siemens); and the OSIC-Konya dataset [3] which includes 87 pulmonary fibrosis patients. The data was used to train a U-Net architecture [4] for pericardial segmentation in 2D (512x512 pixels). The network was trained with a batch size of 2 and a learning rate of 0.0001 using the Dice loss. Excellent intra- and interobserver agreement was obtained with a Dice of 0.908 and 0.873 respectively and a mean absolute distance (MAD) between contours of 2.23 and 3.18mm respectively. The automatic Siemens and U-Net methods obtained however significantly lower performance with a Dice of 0.776 and 0.773 respectively and an MAD of 6.47 and 6.72mm respectively. In conclusion, both automatic solutions showed significantly lower performance in an external dataset when compared to manual pericardium segmentation, which could hinder their applicability in clinical studies to assess pericardial fat. Particularly for deep learning methods, generalization to external datasets is known to be challenging. The use of publicly available data and a vanilla architecture means that this approach can be replicated by any researcher, however, training with additional data and using more advanced architectures could yield improved results and enable the use of automatic deep learning methods in large clinical studies.

Penggunaan Ekstrak Daun Eceng Gondook (Eichornia crassipes) Sebagai Pengawet Terhadap Kualitas dan Kuantitas Telur Ayam Ras

This research was conducted for 3 months in the laboratory of the Department of Animal Husbandry, State University of Gorontalo. This study used an egg sample of 240 eggs of purebred chickens. Eggs were divided into 4 treatments, each treatment was repeated 3 times. A0 = 0% (eggs without treatment), A1 = 10% (eggs soaked at 10% level of water hyacinth leaf extract), A2 = 20% (eggs soaked at 20% level of water hyacinth leaf extract), A3 = 30% (eggs by immersing 30% level of water hyacinth leaf extract). B0 (1 day old egg), B1 (14 day old egg), B2 (21 day old egg), B3 (28 day old egg). The research was conducted by measuring egg weight, air voids, HU value, egg yolk color, and taste. Flavor. Data were analyzed using a completely randomized design (CRD), and further tests were carried out using Duncan's test. The results of this study showed that the effect of tannin content on the level of 20% water hyacinth leaf extract was able to maintain egg quality compared to eggs that were not given treatment (control). Because the tannin solution derived from water hyacinth leaves is able to cover the egg shell pores properly.

Gadolinium K-edge angiography with a spectral photon counting CT in atherosclerotic rabbits

PurposeThe purpose of this study was to investigate the feasibility of gadolinium-K-edge-angiography (angio-Gd-K-edge) with gadolinium-based contrast agents (GBCAs) as obtained with spectral photon counting CT (SPCCT) in atherosclerotic rabbits. Materials and methodsSeven atherosclerotic rabbits underwent angio-SPCCT acquisitions with two GBCAs, with similar intravenous injection protocol. Conventional and angio-Gd-K-edge images were reconstructed with the same parameters. Regions of interest were traced in different locations of the aorta and its branches. Hounsfield unit values, Gd concentrations, signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated and compared. The maximum diameter and the diameter of the aorta in regard to atherosclerotic plaques were measured by two observers. Images were subjectively evaluated regarding vessels’ enhancement, artefacts, border sharpness and overall image quality. ResultsIn the analyzable six rabbits, Gd-K-edge allowed visualization of target vessels and no other structures. HU values and Gd concentrations were greatest in the largest artery (descending aorta, 5.6 ± 0.8 [SD] mm), and lowest in the smallest (renal arteries, 2.1 ± 0.3 mm). While greater for conventional images, CNR and SNR were satisfactory for both images (all P < 0.001). For one observer there were no statistically significant differences in either maximum or plaque-diameters (P = 0.45 and all P > 0.05 in post-hoc analysis, respectively). For the second observer, there were no significant differences for images reconstructed with the same parameters (all P < 0.05). All subjective criteria scored higher for conventional images compared to K-edge (all P < 0.01), with the highest scores for enhancement (4.3–4.4 vs. 3.1–3.4). ConclusionWith SPCCT, angio-Gd-K-edge after injection of GBCAs in atherosclerotic rabbits is feasible and allows for angiography-like visualization of small arteries and for the reliable measurement of their diameters.

Associations between preprocedural carotid artery perivascular fat density and early in-stent restenosis after carotid artery stenting

ObjectivesThis study investigated the association between perivascular fat density (PFD) via preoperative computed tomographic angiography (CTA) and early in-stent restenosis (ISR) after carotid artery stenting (CAS). MethodsWe retrospectively evaluated 248 consecutive patients who had undergone initial CAS and received a preoperative cervical CTA examination between January 2019 and October 2020. The patients were categorized into two according to whether they sustained ISR during the 2 years postoperative follow-up period. Correlations between PFD and ISR were assessed, and multivariate regression for evaluating predictors of ISR was conducted. Receiver operating characteristic (ROC) curves were used to determine the cutoff value for the PFD. ResultsA total of 181 eligible patients (mean age 61.25 ± 10.35 years, 57 male) were enrolled. The ISR group had a higher proportion of closed-cell stents (48.8% versus 27.5%; p = 0.009) and a greater degree of residual stenosis (28[20,33] % versus 20[14.75,30] %; p < 0.001) than the non-ISR group. The ISR group had a higher mean HU value of PFD than the non-ISR group on the operated side (−42.26 ± 6.81 versus −59.66 ± 10.75; p < 0.001). The degree of residual stenosis (OR 1.146, 95%CI 1.071–1.226, p < 0.001) and PFD on the operated side (OR1.353, 95%CI 1.215–1.506, p < 0.001) were significantly associated with the ISR. ConclusionsThe occurrence of the early ISR after CAS is associated with a higher PFD on the operated side. The results indicate that PFD is a promising marker to predict the ISR after CAS.

A nonlinear scaling-based normalized metal artifact reduction to reduce low-frequency artifacts in energy-integrating and photon-counting CT.

Metal within the scan plane can cause severe artifacts when reconstructing X-ray computed tomography (CT) scans. Both in clinical use and recent research, normalized metal artifact reduction (NMAR) has established as the reference method for correcting metal artifacts, but NMAR introduces inconsistencies within the sinogram, which can cause additional low-frequency artifacts after image reconstruction. This paper introduces an extension to NMAR by applying a nonlinear scaling function (NLS-NMAR) to reduce low-frequency artifacts, which get introduced by the reconstruction of interpolation-edge-related sinogram inconsistencies in the normalized sinogram domain. After linear interpolation of the metal trace, an NLS function is applied in the prior-normalized sinogram domain to reduce the impact of the interpolation edges during filtered backprojection. After sinogram denormalization and image reconstruction, the low frequencies of the NLS image are combined with different high frequencies to restore anatomic details. An anthropomorphic dental phantom with removable metal inserts was utilized on two different CT systems to quantitatively assess the artifact reduction performance in terms of HU deviations and the root-mean-square-error within relevant regions of interest. Clinical dental examples were assessed to qualitatively demonstrate the problem of the interpolation-related blooming as well as to demonstrate the performance of the NLS function to reduce respective artifacts. To quantitatively prove HU consistency, HU values were assessed in central ROIs in the clinical cases. In addition, single clinical cases of a hip replacement and pedicle screws in the spine are shown to demonstrate the method's results in other body regions. The NLS-NMAR can minimize the effect of interpolation-related sinogram inconsistencies and thus reduce resulting hyperdense blooming artifacts. In the phantom results, the reconstructions with the NLS-NMAR-corrected low frequencies demonstrate the lowest error. In the qualitative assessment of the clinical data, the NLS-NMAR shows a tremendous enhancement in image quality, also performing best within all assessed images series. The NLS-NMAR provides a small yet effective extension to conventional NMAR by reducing low-frequency hyperdense metal trace-interpolation-related artifacts in computed tomography.

Washout-Computed Tomography Discriminates Pulmonary "Fat-poor" Hamartomas From Neuroendocrine Neoplasms: A Simple Method in the Radiomics Era.

Pulmonary hamartomas (HAs) and neuroendocrine neoplasms (NENs) are often impossible to discriminate using high-resolution computed tomography (CT) as they share morphologic features. This challenge makes differential diagnosis crucial as HAs are invariably benign, whereas NENs must be considered malignant, thus requiring them to be evaluated for surgical excision.Our aim was, therefore, to develop a simple method to discriminate between pulmonary "fat-poor" HAs and NENs using contrast-enhanced CT (CECT). Between September 2015 and December 2021, 95 patients with a histologically proven diagnosis of lung NENs (74) and HAs (21) and who underwent a preoperative CECT scan were initially identified through a review of our pathologic and radiologic databases. Among these, 55 cases (18 HAs and 37 NENs), which have been studied with biphasic CECT, were ultimately selected and reviewed by 3 radiologists with different levels of experience, analyzing their morphologic and enhancement features.The enhancement analysis was performed by placing a region of interest within the lesion in noncontrast (NCp), postcontrast (PCp, 55 to 65s after intravenous contrast injection), and delayed phases (Dp, 180 to 300s). A subgroup of 35 patients who underwent 18FDG-PET/CT was evaluated in a secondary analysis. HU values were significantly different between NENs and HAs in the PCp ( P <0.001). NCp and Dp attenuation values did not show significant differences in the 2 groups. Differences in values of HUs in PCp and Dp allowed to discriminate between NENs and HAs. Wash-out analysis, ΔHU (PCp-Dp), can perfectly discriminate pulmonary "fat-poor" HAs from NENs.

Heterogeneous breast phantom for computed tomography and magnetic resonance imaging.

In this article, a heterogeneous multimodal anthropomorphic breast phantom with carcinoma is introduced to meet the response of the natural breast tissue when imaged using ionizing and non-ionizing machines. The skin, adipose, fibroglandular, pectoral muscle, and carcinoma tissue were mimicked. A T1-weighted breast magnetic resonance image with BI-RADS I tissue segmentation was used for molds creation. The tissue-mimicking materials (TMMs) were tailored in terms of their elemental composition weight fractions and their response to ionization radiation parameters. These are the mass attenuation coefficient (MAC), electron density (ne) and effective atomic number (Zeff). The behaviour of the TMMs, when exposed to a wide range of ionization radiation energy, was investigated analytically and numerically using X-COM. The achieved results showed an excellent agreement with the corresponding properties of the natural breast elemental compositions as reported by the International Commission on Radiation Units and Measurements (ICRU). The MAC of the TMMs and the ICRU-based breast tissue were found to be consistent. The maximum percentage of error in ne and Zeff amounts to only 2.93% and 5.76%, respectively. For non-ionizing imaging, the TMMs were characterized in term of T1 and T2 relaxation times. Using our preclinical MRI unit, the TMMs relaxation times were measured and compared to the natural tissue. The fabricated phantom was validated experimentally using CT, MRI, and Mammographic machines. The achieved images of the TMMs were in alignment with the real tissue in terms of CT HU values and grayscale colors. T1W and T2W images on MRI revealed the expected contrast between TMMs as in natural tissue.

3D-Printed Iodine-Ink CT Phantom for Radiomics Feature Extraction - Advantages and Challenges.

To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's x-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26×19×30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has x-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g. for ω = 0.3942 mm-1 , |f(ω)|original = 0.09 * |fmax |original and |f(ω)|phantom = 0.12 * |fmax |phantom ). 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses. This article is protected by copyright. All rights reserved.