Average Computed Tomography Number Research Articles

Characterization of single- and multi-energy CT performance of an oral dark borosilicate contrast media using a clinical photon-counting-detector CT platform.

The feasibility of oral dark contrast media is under exploration in abdominal computed tomography (CT) applications. One of the experimental contrast media in this class is dark borosilicate contrast media (DBCM), which has a CT attenuation lower than that of intra-abdominal fat. To evaluate the performances of DBCM using single- and multi-energy CT imaging on a clinical photon-counting-detector CT (PCD-CT). Five vials, three with iodinated contrast agent (5, 10, and 20mg/mL; Omnipaque 350) and two with DBCM (6% and 12%; Nextrast, Inc.), and one solid-water rod (neutral contrast agent) were inserted into two multi-energy CT phantoms, and scanned on a clinical PCD-CT system (NAEOTOM Alpha) at 90, 120, 140, Sn100, and Sn140 kV (Sn: tin filter) in multi-energy mode. CARE keV IQ level was 180 (CTDIvol: 3.0 and 12.0 mGy for the small and large phantoms, respectively). Low-energy threshold images were reconstructed with a quantitative kernel (Qr40, iterative reconstruction strength 2) and slice thickness/increment of 2.0/2.0mm. Virtual monoenergetic images (VMIs) were reconstructed from 40 to 140keV at 10keV increments. On all images, average CT numbers for each vial/rod were measured using circular region-of-interests and averaged over eight slices. The contrast-to-noise ratio (CNR) of iodine (5mg/mL) against DBCM was calculated and plotted against tube potential and VMI energy level, and compared to the CNR of iodine against water. Similar analyses were performed on iodine maps and VNC images derived from the multi-energy scan at 120kV. With increasing kV or VMI keV, the negative HU of DBCM decreased only slightly, whereas the positive HU of iodine decreased across all contrast concentrations and phantom sizes. CT numbers for DBCM decreased from -178.5±9.6 to -194.4±6.3 HU (small phantom) and from -181.7±15.7 to -192.1±11.9 HU (large phantom) for DBCM-12% from 90 to Sn140 kV; on VMIs, the CT numbers for DBCM decreased minimally from -147.1±15.7 to -185.1±9.2 HU (small phantom) and -158.8±28.6 to -188.9±14.7 HU (large phantom) from 40 to 70keV, but remained stable from 80 to 140keV. The highest iodine CNR against DBCM in low-energy threshold images was seen at 90 or Sn140 kV for the small phantom, whereas all CNR values from low-energy threshold images for the large phantom were comparable. The CNR values of iodine against DBCM computed on VMIs were highest at 40 or 70keV depending on iodine and DBCM concentrations. The CNR values of iodine against DBCM were consistently higher than iodine to water (up to 460% higher dependent on energy level). Further, the CNR of iodine compared to DBCM is less affected by VMI energy level than the identical comparison between iodine and water: CNR values at 140keV were reduced by 46.6% (small phantom) or 42.6% (large phantom) compared to 40keV; CNR values for iodine compared to water were reduced by 86.3% and 83.8% for similar phantom sizes, respectively. Compared to 70keV VMI, the iodine CNR against DBCM was 13%-79% lower on iodine maps and VNC. When evaluated at different tube potentials and VMI energy levels using a clinical PCD-CT system, DBCM showed consistently higher CNR compared to iodine versus water (a neutral contrast).

Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose4: a phantom study at different dose levels

PurposeTo characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging. MethodsCT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose4 (levels 2,5) and PI (‘Soft Tissue’ definition, levels ‘Sharper’,‘Sharp’,‘Standard’,‘Smooth’,‘Smoother’). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d’), CT numbers accuracy and image histograms were analyzed. ResultsThe behavior of the PI algorithm depended strongly on the selected level of reconstruction. The phantom analysis suggested that the PI image noise decreased linearly by varying the level of reconstruction from Sharper to Smoother, expressing a noise reduction up to 80% with respect to FBP. Additionally, the non-uniformity decreased, the histograms became narrower, and d’ values increased as PI reconstruction levels changed from Sharper to Smoother. PI had no significant impact on the average CT number of different contrast objects. The conventional FBP NPS was deeply altered only by Smooth and Smoother levels of reconstruction. Furthermore, spatial resolution was found to be dose- and contrast-dependent, but in each analyzed condition it was greater than or comparable to FBP and iDose4 TTFs. ConclusionsThe PI algorithm can reduce image noise with respect to FBP and iDose4; spatial resolution, CT numbers and image uniformity are generally preserved by the algorithm but changes in NPS for the Smooth and Smoother levels need to be considered in protocols implementation.

The impact of a metal artefact reduction algorithm on treatment planning for patients undergoing radiotherapy of the pelvis.

Metallic implants cause artefacts in computed tomography (CT) images and can introduce significant errors to structure visualisation and dosimetric calculation within the radiotherapy planning process. This study evaluated an orthopaedic metal artefact reduction algorithm and its effect on the CT number, image noise, structure delineation, and treatment dose. Raw CT data were reconstructed using standard filtered back projection and an artefact reduction algorithm to create 'standard' and 'corrected' images. A phantom containing tissue-mimicking inserts and two titanium plugs was imaged. The average CT number was compared to baseline data acquired without metal inserts. Data from 11 pelvic external beam radiotherapy (EBRT) patients with bi- or uni-lateral hip implants were retrospectively analysed. The clinically used treatment plans were re-computed on the corrected images. A prostate-mimicking phantom containing metal 'implants' was imaged, and 11 observers contoured both reconstructions. The artefact reduction algorithm improved the CT number in those areas most affected by metal artefacts and decreased noise by 19% (P=.04) Changes in dose distributions on corrected images compared to those calculated using the current clinical protocol were clinically insignificant. Volumes contoured on the corrected phantom images had larger Dice coefficients than those contoured on the standard images (P=.001), as well as a 36% lower standard deviation in volumes. This study demonstrates that the metal artefact reduction software reduces the error in CT numbers, can improve delineation accuracy, and can reduce inter-observer variability. It has the potential to streamline the planning pathway and improve treatment planning accuracy.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.

PurposeWe have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.MethodsA simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.ResultsThe developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.ConclusionsThe statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.

Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result.

BackgroundArtificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting.MethodsThe study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers’ final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels.ResultsIn total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2–98.0%] and a positive predictive value of 55.6% (95% CI: 49.0–62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist’s decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9–732.5) vs. 141.3 (79.3–380.8) mm3, P<0.001], lower average CT number [−511.0 (−576.5 to −100.5) vs. −191.5 (−487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid.ConclusionsThe AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.

X-ray Computed Tomography as a Tool for Screening Sediment Cores: An Application to the Lagoons of the Po River Delta (Italy)

Lagoon sediments have heterogeneous structure and texture, contain shells and plants and are often highly bioturbated and disturbed by human activities. In such sediments, the selection of representative cores and the choice of a subsampling strategy are important but difficult. In this study, we examine the usefulness of X-ray computed tomography (CT) for inferring sediment features that will help in making optimal decisions prior to core opening (24 cores from seven lagoons). Various algorithms (intensity projections, slice thickness, axial and sagittal images, CT number profiles and volumetric region of interest) are tested to visualise low- and high-density volumes or objects and to quantify the relations between the average volumetric CT number and the bulk density of the sediment matrix. The CT number is related mainly to water content and indirectly to total nitrogen and &lt;16-μm grain-size fraction (model R2 = 0.94). The outliers are attributed to a weak correspondence between the fraction of sediment sampled for water content determination and the volume of sediment matrix used for CT number measurements in highly heterogeneous sediment slices. In conclusion, CT is a powerful tool for the initial screening of cores recovered from heterogeneous lagoon sediments. The adequate use of available algorithms may provide quantitative information on various sediment features, allowing the purposeful selection of cores and subsamples for further investigation.

Characterization of soy-lignin bonded Rhizophora spp. particleboard as substitute phantom material for radiation dosimetric studies – Investigation of CT number, mass attenuation coefficient and effective atomic number

Experimental particleboards are made from Rhizophora spp. wood trunk with three different percentages of lignin and soy flour (0%, 6% and 12%) as adhesives. The objective was to investigate the equivalence of Rhizophora spp. particleboard as phantom material with human soft tissue using Computed Tomography (CT) number. The linear and mass attenuation coefficient of Rhizophora spp. particleboard at low energy range was also explored using X-ray Fluorescence (XRF) configuration technique. Further characterization of the particleboard was performed to determine the effective atomic number, Zeff using Energy Dispersive X-Ray (EDX) method. Adhesive-bonded Rhizophora spp. particleboard showed close similarities with water, based on the average CT numbers, electron density calibration curve and the analysis of CT density profile, compared to the binderless particleboard. The effective atomic number obtained from the study indicated that the attenuation properties of all the particleboards at different percentages of adhesives were almost similar to water. The mass attenuation coefficient calculated from XRF configuration technique showed good agreement with water from XCOM database, suggesting its potential as phantom material for radiation study.

Contextual loss based artifact removal method on CBCT image.

PurposeCone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function.MethodsIn the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT‐CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal‐to‐noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness.ResultsThe proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies.ConclusionsAlthough the CBCT‐CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.

Evaluation of the thermal sensitivity of porcine liver in CT-guided cryoablation: an initial study.

To evaluate computed tomography (CT)-based thermometry in cryoablation, the thermal sensitivity of an ex-vivo porcine liver was determined in an initial study design. The CT-guided cryoablation was performed in three porcine liver samples over a period of 10min. Fiber optic temperature probes were positioned parallel to the shaft of the cryoprobe in an axial slice orientation. During ablation, temperature measurements were performed simultaneously with CT imaging at 5s intervals. On the CT images, the average CT number was calculated for a region of interest of 3×3 pixels just below the tip of each temperature probe. A linear regression analysis was performed using eleven data sets to determine the dependence of the CT number on the temperature. With decreasing temperature, an increasing hypodense area around the tip of the cryoprobe was observed on the CT images and decreasing values of the CT number were determined. Starting at a temperature of-40°C a linear relation between the CT number and the temperature was determined and a thermal sensitivity of 0.95HU/°C (R2 =0.73) was obtained. The thermal sensitivity was used to calculate color-coded temperature maps. The calculated temperature distribution corresponds quantitatively to the increasing hypodense area. A noninvasive CT-based temperature determination during cryoablation in a normal ex vivo porcine liver is feasible. A thermal sensitivity of 0.95HU/°C was determined by linear regression analysis. A color-coded map of the temperature distribution was presented.

Calculating Effective Elastic Properties of Berea Sandstone Using the Segmentation‐Less Method Without Targets

AbstractWe propose a new method to compute elastic properties of rocks from computed tomography (CT) images. A CT volume captures X‐ray attenuation, which is scaled in a CT number unit. Our method improves an existing segmentation‐less method where the CT volume is directly converted to elastic property arrays without using segmentation. A drawback of the existing segmentation‐less method is that it typically requires to scan physical targets of known density along with the sample. Targets map a CT volume into petrophysical property arrays by comparing the CT numbers of the sample with those of the targets. We introduce a segmentation‐less workflow that no longer requires targets but uses pseudotargets that are defined within the sample. Pseudotargets are composed of extrema CT numbers in the CT volume. We assume that regions containing extrema CT numbers represent pristine materials. The average CT numbers of such regions are paired to the pseudotarget densities to create a CT number‐to‐density conversion function. After density‐to‐porosity and porosity‐to‐elastic properties conversion, we simulate wave propagation on two Berea sandstone cores to estimate effective P and S wave velocities. To assess uncertainty, computed velocities are compared to laboratory measurements. We benchmark our technique against a segmentation‐less method, as well as the more commonly used segmentation‐based method. Among the three methods, the segmentation‐less method without targets yields the least absolute velocity error of 4.5%, whereas segmentation and segmentation‐less method yield +29% and +4.7% error, respectively. We conclude that the segmentation‐less method without targets can efficiently compute elastic properties of rocks from CT volumes, serving as a technique to create better subsurface models.

Permeability and Porosity Evolution of Organic-Rich Shales from the Green River Formation as a Result of Maturation

Summary Source rocks (oil shale) were matured artificially via pyrolysis under geologically realistic triaxial stresses using a unique coreholder that is compatible with X-ray computed tomography (CT) scanning. This study focuses on characterization of porosity and permeability as well as the evolution of shale fabric during pyrolysis. Experiments were conducted using 1-in. diameter vertical and horizontal core samples from the Green River Formation. Prior to pyrolysis, the properties of the source rock were characterized [e.g., porosity, bulk density, mineralogy, and total organic carbon (TOC)]. Samples were then heated from room temperature to 350°C under a nitrogen environment to obtain conversion of organic matter (OM) to oil and gas via pyrolysis. Porosity and permeability after maturation were measured. Micro-CT visualization was applied to investigate the fracture network developed throughout the core. Scanning electron microscopy (SEM) images were also used to compare the shale fabric and porosity evolution (pre- to post-maturation) at greater resolution. In-situ observations reveal a decrease in the average CT number (i.e., density) within some volumetric regions of the cores after maturation. In these regions, OM (kerogen and bitumen) was converted into hydrocarbons. Changes in the source rock depend on the original TOC fraction, hydrogen index (HI), and temperature. The permeability prior to pyrolysis for vertical samples is in the undetectable to nanodarcy range. The permeability of all cores increased to the microdarcy range post-maturation. In particular, the permeability of the horizontal sample increased from 0.14 to 50 µd. This improvement in permeability occurred due to the generation of open porosity and fractures (dilation, generation, and/or drainage). Additionally, the porosity after Soxhlet extraction increased proportionally by 20% from pre- to post-pyrolysis depending on pyrolysis time and TOC fraction. Longitudinal deformation depends on the orientation of the sample with respect to the triaxial stress during pyrolysis. The deformation of vertically oriented samples with isostress conditions is larger than that of horizontally oriented samples with isostrain. The measured 3D in-situ porosity distribution indicates that OM has transformed into hydrocarbons by pyrolysis. The development of a fracture and matrix porosity system under stress provides an explanation for transport of hydrocarbon away from its point of origin.

Model Image-Based Metal Artifact Reduction for Computed Tomography.

Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions.

A CT metal artifact reduction algorithm based on sinogram surgery.

Streak artifacts in computed tomography (CT) images caused by metallic objects limit the wider use of CT imaging technologies. There have been various attempts to improve CT images containing streak artifacts; however, most of them generate additional artifacts or do not completely eradicate existing artifacts. In this paper, we propose a novel algorithm which reduces streak artifacts in CT images. Using CT numbers reconstructed from a given sinogram, we extract the metal part M and the surrounding area C with similar CT numbers. By filling in the area C∪M with the evaluated average CT number of C, we obtain a modified CT image. Using forward projection of the modified CT image, we generate a sinogram containing information about the anatomical structure. We undertake sinogram surgery to remove the metallic effects from the sinogram, after which we repeat the procedure. We perform numerical experiments using various simulated phantoms and patient images. For a quantitative analysis, we use the relative l∞ error and the relative l2 error. In simulated phantom experiments, all l∞ errors and l2 errors approach 10% and 1% of the initial errors, respectively. Moreover, for the patient image simulations, all l∞ errors are decreased by a factor of 20 while the l2 errors are decreased less than 5%. We observe that the proposed algorithm effectively reduces the metal artifacts. In this paper, we propose a metal artifact reduction algorithm based on sinogram surgery to reduce metal artifacts without additional artifacts. We also provide empirical convergence of our algorithm.

Metal artifact reduction in CT, a phantom study: subjective and objective evaluation of four commercial metal artifact reduction algorithms when used on three different orthopedic metal implants.

Background Metal implants may introduce severe artifacts in computed tomography (CT) images. Over the last few years dedicated algorithms have been developed in order to reduce metal artifacts in CT images. Purpose To investigate and compare metal artifact reduction algorithms (MARs) from four different CT vendors when imaging three different orthopedic metal implants. Material and Methods Three clinical metal implants were attached to the leg of an anthropomorphic phantom: cobalt-chrome; stainless steel; and titanium. Four commercial MARs were investigated: SmartMAR (GE); O-MAR (Philips); iMAR (Siemens); and SEMAR (Toshiba). The images were evaluated subjectively by three observers and analyzed objectively by calculating the fraction of pixels with CT number above 500 HU in a region of interest around the metal. The average CT number and image noise were also measured. Results Both subjective evaluation and objective analysis showed that MARs reduced metal artifacts and improved the image quality for CT images containing metal implants of steel and cobalt-chrome. When using MARs on titanium, all MARs introduced new visible artifacts. Conclusion The effect of MARs varied between CT vendors and different metal implants used in orthopedic surgery. Both in subjective evaluation and objective analysis the effect of applying MARs was most obvious on steel and cobalt-chrome implants when using SEMAR from Toshiba followed by SmartMAR from GE. However, MARs may also introduce new image artifacts especially when used on titanium implants. Therefore, it is important to reconstruct all CT images containing metal with and without MARs.

Cement stress predictions after anatomic total shoulder arthroplasty are correlated with preoperative glenoid bone quality

We hypothesized that biomechanical parameters typically associated with glenoid implant failure after anatomic total shoulder arthroplasty (aTSA) would be correlated with preoperative glenoid bone quality. We developed an objective automated method to quantify preoperative glenoid bone quality in different volumes of interest (VOIs): cortical bone, subchondral cortical plate, subchondral bone after reaming, subchondral trabecular bone, and successive layers of trabecular bone. Average computed tomography (CT) numbers (in Hounsfield units [HU]) were measured in each VOI from preoperative CT scans. In parallel, we built patient-specific finite element models of simulated aTSAs to predict cement stress, bone-cement interfacial stress, and bone strain around the glenoid implant. CT measurements and finite element predictions were obtained for 20 patients undergoing aTSA for primary glenohumeral osteoarthritis. We tested all linear correlations between preoperative patient characteristics (age, sex, height, weight, glenoid bone quality) and biomechanical predictions (cement stress, bone-cement interfacial stress, bone strain). Average CT numbers gradually decreased from cortical (717 HU) to subchondral and trabecular (362 HU) bone. Peak cement stress (4-10 MPa) was located within the keel hole, above the keel, or behind the glenoid implant backside. Cement stress, bone-cement interfacial stress, and bone strain were strongly negatively correlated with preoperative glenoid bone quality, particularly in VOIs behind the implant backside (subchondral trabecular bone) but also in deeper trabecular VOIs. Our numerical study suggests that preoperative glenoid bone quality is an important parameter to consider in aTSA, which may be associated with aseptic loosening of the glenoid implant. These initial results should now be confronted with clinical and radiologic outcomes.

Effects of carbonation and freeze-thaw cycles on microstructure of concrete

The hydraulic concrete durability under the alternation of freeze-thaw and carbonation has been systematically investigated in this work, where both the micro part and the microscopic characteristics of concrete interface were analyzed based on computed tomography (CT) test and scanning electron microscopy (SEM). Average CT numbers of each section, declined at water-cement ratio of 0.35, increased at 0.45, and changed a little at 0.55. The specimen in the absence of fly ash exhibited less types of hydration products and the surface was observed to be a needle-like ettringite, with a relatively dense overall structure. However, with the increase of fly ash content, pores and micro-cracks of specimen structure increased. Hexagonal flake calcium hydroxide, present in the specimen after the first carbonation, was negligible in the test pieces of the first freezethaw where the main hydration products were ettringite and calcium silicate gel. Regular hexagonal plates of calcium hydroxide exhibited in the interior of the specimen in which charring first occurred but calcium hydroxide rarely existed in the interior of the specimen in which freeze-thaw first occurred.

X‐ray CT‐Derived Soil Characteristics Explain Varying Air, Water, and Solute Transport Properties across a Loamy Field

Core Ideas We used CT‐derived parameters to explain solute, water, and air transport at field scale. CTmatrix was found to be the best parameter to explain solute transport. Limiting macroporosity gave the highest correlations with water and air transport. Combining macroporosity and CTmatrix improved the relationships of water and air flow. The characterization of soil pore space geometry is important for explaining fluxes of air, water, and solutes through soil and understanding soil hydrogeochemical functions. X‐ray computed tomography (CT) can be applied for this characterization, and in this study CT‐derived parameters were used to explain water, air, and solute transport through soil. Forty‐five soil columns (20 by 20 cm) were collected from an agricultural field in Estrup, Denmark, and subsequently scanned using a medical CT scanner. Nonreactive tracer leaching experiments were performed in the laboratory along with measurements of air permeability (Ka) and saturated hydraulic conductivity (Ksat). The CT number of the matrix (CTmatrix), which represents the moist bulk density of the soil matrix, was obtained from the CT scans as the average CT number of the voxels in the grayscale image excluding macropores and stones. The CTmatrix showed the best relationships with the solute transport characteristics, especially the time by which 5% of the applied mass of tritium was leached, known as the 5% arrival time (t0.05). The CT‐derived macroporosity (pores &gt;1.2 mm) was correlated with Ka and log10(Ksat). The correlation improved when the limiting macroporosity (the minimum macroporosity for every 0.6‐mm layer along the soil column) was used, suggesting that soil layers with the narrowest macropore section restricted the flow through the whole soil column. Water, air, and solute transport were related with the CT‐derived parameters by using a best subsets regression analysis. The regression coefficients improved using CTmatrix, limiting macroporosity, and genus density, while the best model for t0.05 used CTmatrix only. The scanning resolution and the time for soil structure development after mechanical activities could be factors that increased the uncertainty of the relationships. Nevertheless, the results confirmed the potential of X‐ray CT visualization techniques for estimating fluxes through soil at the field scale.

Characterization of a lead breast shielding for dose reduction in computed tomography.

ObjectiveSeveral studies have been published regarding the use of bismuth shielding to protect the breast in computed tomography (CT) scans and, up to the writing of this article, only one publication about barium shielding was found. The present study was aimed at characterizing, for the first time, a lead breast shielding.Materials and MethodsThe percentage dose reduction and the influence of the shielding on quantitative imaging parameters were evaluated. Dose measurements were made on a CT equipment with the aid of specific phantoms and radiation detectors. A processing software assisted in the qualitative analysis evaluating variations in average CT number and noise on images.ResultsThe authors observed a reduction in entrance dose by 30% and in CTDIvol by 17%. In all measurements, in agreement with studies in the literature, the utilization of cotton fiber as spacer object reduced significantly the presence of artifacts on the images. All the measurements demonstrated increase in the average CT number and noise on the images with the presence of the shielding.ConclusionAs expected, the data observed with the use of lead shielding were of the same order as those found in the literature about bismuth shielding.

Spatial and dose-response analysis of fibrotic lung changes after stereotactic body radiation therapy

Stereotactic body radiation therapy (SBRT) is becoming the standard of care for early stage nonoperable lung cancers. Accurate dose-response modeling is challenging for SBRT because of the decreased number of clinical toxicity events. As a surrogate for a clinical toxicity endpoint, studies have proposed to use radiographic changes in follow up computed tomography (CT) scans to evaluate lung SBRT normal tissue effects. The purpose of the current study was to use local fibrotic lung regions to spatially and dosimetrically evaluate lung changes in patients that underwent SBRT. Forty seven SBRT patients treated at our institution from 2003 to 2009 were used for the current study. Our patient cohort had a total of 148 follow up CT scans ranging from 3 to 48 months post-therapy. Post-treatment scans were binned into intervals of 3, 6, 12, 18, 24, 30, and 36 months after the completion of treatment. Deformable image registration was used to align the follow up CT scans with the pretreatment CT and dose distribution. Areas of visible fibrotic changes were contoured. The centroid of each gross tumor volume (GTV) and contoured fibrosis volume was calculated and the fibrosis volume location and movement (magnitude and direction) relative to the GTV and 30 Gy isodose centroid were analyzed. To perform a dose-response analysis, each voxel in the fibrosis volume was sorted into 10 Gy dose bins and the average CT number value for each dose bin was calculated. Dose-response curves were generated by plotting the CT number as a function of dose bin and time posttherapy. Both fibrosis and GTV centroids were concentrated in the upper third of the lung. The average radial movement of fibrosis centroids relative to the GTV centroids was 2.6 cm with movement greater than 5 cm occurring in 11% of patients. Evaluating dose-response curves revealed an overall trend of increasing CT number as a function of dose. The authors observed a CT number plateau at doses ranging from 30 to 50 Gy for the 3, 6, and 12 months posttherapy time points. There was no evident plateau for the dose-response curves generated using data from the 18, 24, 30, and 36 months posttherapy time points. Regions of local fibrotic lung changes in patients that underwent SBRT were evaluated spatially and dosimetrically. The authors found that the average fibrosis movement was 2.6 cm with movement greater than 5 cm possible. Evaluating dose-response curves revealed an overall trend of increasing CT number as a function of dose. Furthermore, our dose-response data also suggest that one of the possible explanations of the CT number plateau effect may be the time posttherapy of the acquired data. Understanding normal tissue dose-response is important for reducing toxicity after SBRT, especially in cases where larger tumors are treated. The methods presented in the current work build on prior quantitative studies and further enhance the understanding of normal lung dose-response after SBRT.

Non-destructive detection of internal quality of apple based on CT image.

China is a big producing country of fruits and vegetables,but the export price is quite low due to the lack of suitable grade classification technologies. Domestic academics have been dedicating themselves to ameliorating the detection technology to change this situation.X-ray computed tomography(CT),making use of the specific penetrativity,can acquire an accurate faulted image which contains many internal quality information of fruit. In order to detect the character of apples quickly without damaging the sample simultaneously,a model was established based on the faulted image and some effective information related to the internal quality. Although the average CT number of pulp area in an apple profile has a good linear relationship with the quality,it is of little practical value when popularized.We intend to make CT non-destructive detection method much more useful in the prediction of the apple quality. Firstly,the window/level number of CT image should be unified at an appropriate level,before building the model between CT numbers and gray level values.Secondly,the atactic pulp area should be separated from the faulted image by means of a segmentation algorithm named Otsu,which is an adaptive threshold method.Thirdly,the weighted mean of pixel numbers in this area should be calculated and converted into CT average numbers according to the relationship built before.Finally,the model of the relationship between the CT number and the internal quality in the area was developed.The model can be used to predict and analyze the apple quality. Generally,we had an arbitrary scale with air defined as having a CT number of-1000 HU and water of 0 HU.In our experiment,we detected the CT numbers of apple pulp ranging from-380 HU to 20 HU,which was will corresponded to the internal structure of apple. For the significant influence emerged by the window/level number in the process of converting the DICOM(digital imaging and communications in medicine) image into gray level image in BMP format,the window/level number must be unified.In comparison,we can get the clearest image at 430/-210.Then a regression model was set up between the CT numbers and the gray level values,which showed a good linear relationship with the R2 reaching 0.970 8. In consideration of the results obtained by some image segmentation algorithms,Otsu(maximum between clusters variance method) was put into use.It had different segmentation thresholds by computation,ranging from 71 to 91.At the same time,the weighted mean of pixel number can also be acquired from each gray level image by Otsu,and then the CT average numbers were converted from the pixel numbers. Finally the models between CT average numbers and the apple internal quality parameters were established,showing good linear relationships between CT number and the main under as sugar,titratable acidity and moisture,with the R2 values of 0.8464,0.8233,0.9075,respectively,and the prediction error can be controlled within 5.0%,7.4%,3.8%. It can be concluded that the internal quality of apple can be predicted by the CT faulted image quickly and non-destructively.We also found that the window/level number in the picture format of DICOM would significantly affect the gray level in BMP format,but in a fixed number,the two had a good linear relationship.The pulp area can be well separated from the whole image by Otsu,as well as figuring out the CT average numbers.At last we build three linear regression models between the number and the sugar,the titratable acidity,and the moisture separately,with good related coefficients and low forecast errors.