Mean CT Number Research Articles

An Algorithm for Automatic Low-contrast Detection System and Its Evaluation for Images with Various Phantom Rotations

The purpose of this study is to develop an algorithm for automatic low-contrast object detection on the ACR 464 CT phantom and to investigate its evaluation for images with various phantom rotations. A software for automatic low-contrast detection was implemented with MATLAB R2013a. An algorithm was based on a template matching method. The reference points for the template matching method was centers of phantom and largest low-contrast object. The centers of the phantom and the largest low-contrast object were calculated using centroid formulae from the segmented phantom and objects with specific threshold values. Region of interests (ROIs) were located at each low-contrast object and background. The mean CT number and noise were calculated from pixel values within each ROI. The contrast-to-noise ratio (CNR) was then calculated based on the contrast between low-contrast object and background. The CNR cut-off is one. Object that has a CNR value more than the cut-off is considered resolved object and object that has a CNR value less than the cut-off is considered unresolved object. The algorithm was evaluated on images of the ACR CT phantom with various rotations of 0°, 22.5°, 45°, and 60°. Statistic evaluation was performed by ANOVA one-way to compare results of mean CT number, noise, and CNR for various rotations. The p-value more than 0.05 indicated that there is no significant difference. The proposed method was successful in placing ROIs at all low-contrast objects and at background for various phantom rotations. The CNR increases with the increase of size of the low-contrast object. The p-values for contrast, noise and CNR were 0.99, indicating that there are no statistically significant differences for various rotations. The minimum resolved object detectability in various rotations was 4 mm. An automatic technique for detecting low-contrast objects is accurate for various rotations.

Correction for partial volume averaging in the quantification of radiopaque nanomaterial-embedded resorbable polymers

Resorbable inferior vena cava (IVC) filters require embedded contrast for image-guided placement and integrity monitoring. We calculated correction factors to account for partial volume averaging of thin nanoparticle (NP)-embedded materials, accounting for object and slice thicknesses, background signal, and nanoparticle concentration. We used phantoms containing polycaprolactone disks embedded with bismuth (Bi) or ytterbium (Yb): 0.4- to 1.2-mm-thick disks of 20 mg ml−1 NPs (thickness phantom), 0.4-mm-thick disks of 0–20 mg ml−1 NPs in 2 mg ml−1 iodine (concentration phantom), and 20 mg ml−1 NPs in 0.4-mm-thick disks in 0–10 mg ml−1 iodine (background phantom). Phantoms were scanned on a dual-source CT with 80, 90, 100, and 150 kVp with tin filtration and reconstructed at 1.0- to 1.5-mm slice thickness with a 0.1-mm interval. Following scanning, disks were processed for inductively coupled plasma optical emission spectrometry (ICP-OES) to determine NP concentration. Mean and maximum CT numbers (HU) of all disks were measured over a 0.5-cm2 area for each kVp. HU was converted to concentration using previously measured calibrations. Concentration measurements were corrected for partial volume averaging by subtracting residual slice background and extrapolating disk thickness to both nominal and measured slice sensitivity profiles (SSP, mm). Slice thickness to agreement (STTA, mm) was calculated by replacing the CT-derived concentrations with ICP-OES measurements and solving for thickness. Slice thickness correction factors improved agreement with ICP-OES for all measured data. Yb corrections resulted in lower STTA than Bi corrections in the concentration phantom (1.01 versus 1.31 STTA/SSP, where 1.0 is perfect agreement), phantoms with varying thickness (1.30 versus 1.87 STTA/SSP), and similar ratio in phantoms with varying background iodine concentration (1.34 versus 1.35 STTA/SSP). All measured concentrations correlated strongly with ICP-OES and all corrections for partial volume averaging increased agreement with ICP-OES concentration, demonstrating potential for monitoring the integrity of thin IVC resorbable filters with CT.

A clinical nomogram for predicting occult lymph node metastasis in patients with non-small-cell lung cancer ≤2 cm.

Sublobar resection has been shown to be feasible for non-small-cell lung cancers (NSCLC) <2 cm in size based on several prospective studies. However, the prognosis of clinical N0 patients who experience an N-stage upgrade after surgery [known as occult lymph node metastasis (OLM)] may be worse. The ability of predict OLM in patients eligible for sublobar resection remains a controversial issue. Patients with NSCLC ≤2 cm in diameter and containing a solid component who underwent surgical treatment at the Affiliated Hospital of Qingdao University were retrospectively enrolled, and 1:1 case matching was performed. The risk factors were identified through logistic regression analyses and theoretical criteria, followed by the development of a nomogram that was evaluated using 200 iterations of 10-fold cross-validation. After case matching, 130 pairs of patients were selected for modelling. According to the multivariable logistic regression analysis, the carcinoembryonic antigen level, consolidation tumour ratio, mean computed tomography number and tumour margin were included in the nomogram. The cross-validated average area under the receiver operating characteristic curve was found to be 0.86. Furthermore, calibration curve and decision curve analyses demonstrated the excellent predictive accuracy and clinical utility of the nomogram respectively. By utilizing accessible characteristics, we developed a nomogram that predicts the probability of OLM in patients with NSCLC ≤2 cm with a solid component. Risk stratification with this nomogram could aid in surgical method decision-making. Not applicable.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. A phantom with two iodine inserts (1 and 2mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4cm for first generation and 8cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50 versus 150keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2cm). With increased collimation (4cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8cm collimation width was comparable to a 4cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45 and 0.33mg/mL for the first and second-generation DLCT, respectively). The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction

BackgroundNeck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. Materials and MethodsFrom 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. ResultsAt each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). ConclusionsThe investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms

PurposeTo characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. MethodsCatphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. ResultsThe five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. ConclusionsThe improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

A simplified low-cost phantom for image quality assessment of dental cone beam computed tomography unit.

A standardised testing protocol for evaluation of a wide range of dental cone beam computed tomography (CBCT) performance and image quality (IQ) parameters is still limited and commercially available testing tool is unaffordable by some centres. This study aims to assess the performance of a low-cost fabricated phantom for image quality assessment (IQA) of digital CBCT unit. A customised polymethyl methacrylate (PMMA) cylindrical phantom was developed for performance evaluation of Planmeca ProMax 3D Mid digital dental CBCT unit. The fabricated phantom consists of four different layers for testing specific IQ parameters such as CT number accuracy and uniformity, noise and CT number linearity. The phantom was scanned using common scanning protocols in clinical routine (90.0 kV, 8.0 mA and 13.6 s). In region-of-interest (ROI) analysis, the mean CT numbers (in Hounsfield unit, HU) and noise for water and air were determined and compared with the reference values (0 HU for water and -1000 HU for air). For linearity test, the correlation between the measured HU of different inserts with their density was studied. The average CT number were -994.1 HU and -2.4 HU, for air and water, respectively and the differences were within the recommended acceptable limit. The linearity test showed a strong positive correlation (R2 = 0.9693) between the measured HU and their densities. The fabricated IQ phantom serves as a simple and affordable testing tool for digital dental CBCT imaging.

Assessment of the effectiveness of Saba shielding with the composition of Cu–Bi in neck CT imaging: a phantom and patient study

The use of computed tomography (CT) is a very well-established medical diagnostic imaging modality, however, the high radiation dose due to this imaging method is a major concern. Therefore, dose reduction methods are necessary, especially for superficial radiosensitive organs like the thyroid. The aim of this study is to construct and assess a CT shield with composition of 90% Cu and 10% Bi (Saba shield) with regard to dose reduction and image quality. The efficiency of the constructed shields for dose reduction was assessed by measuring entrance skin dose (ESD), using thermoluminescence dosimeters placed on an anthropomorphic phantom. Image quality was assessed quantitatively based on image noise and CT number accuracy by drawing regions of interest on CT images of the anthropomorphic phantom. Image quality was further investigated qualitatively in a patient study. Application of the Saba shield and 100% Bi shield with the thickness of one thickness (1T) reduced ESD by 50.2% and 51.7%, respectively, and using a three-fold thickness reduced ESD by 64.6% and 65.1%, respectively. Saba shield with thickness of 1T had no significant change in image noise in the anterior part, and image noise and mean CT number in the posterior part (P > 0.05). The statistical analysis performed did not find any meaningful difference between the study and control groups in image quality assessment of the patient study (P > 0.05). The 1T Saba shield reduced thyroid dose efficiently during neck CT imaging without causing unwanted effects on image quality.

Metal Artifact Reduction in Photon-Counting Detector CT: Quantitative Evaluation of Artifact Reduction Techniques.

With the introduction of clinical photon-counting detector computed tomography (PCD-CT) and its novel reconstruction techniques, a quantitative investigation of different acquisition and reconstruction settings is necessary to optimize clinical acquisition protocols for metal artifact reduction. A multienergy phantom was scanned on a clinical dual-source PCD-CT (NAEOTOM Alpha; Siemens Healthcare GmbH) with 4 different central inserts: water-equivalent plastic, aluminum, steel, and titanium. Acquisitions were performed at 120 kVp and 140 kVp (CTDI vol 10 mGy) and reconstructed as virtual monoenergetic images (VMIs; 110-150 keV), as T3D, and with the standard reconstruction "none" (70 keV VMI) using different reconstruction kernels (Br36, Br56) and with as well as without iterative metal artifact reduction (iMAR). Metal artifacts were quantified, calculating relative percentages of metal artifacts. Mean CT numbers of an adjacent water-equivalent insert and different tissue-equivalent inserts were evaluated, and eccentricity of metal rods was measured. Repeated-measures analysis of variance was performed for statistical analysis. Metal artifacts were most prevalent for the steel insert (12.6% average artifacts), followed by titanium (4.2%) and aluminum (1.0%). The strongest metal artifact reduction was noted for iMAR (with iMAR: 1.4%, without iMAR: 10.5%; P < 0.001) or VMI (VMI: 110 keV 2.6% to 150 keV 3.3%, T3D: 11.0%, and none: 16.0%; P < 0.001) individually, with best results when combining iMAR and VMI at 110 keV (1.2%). Changing acquisition tube potential (120 kV: 6.6%, 140 kV: 5.2%; P = 0.33) or reconstruction kernel (Br36: 5.5%, Br56: 6.4%; P = 0.17) was less effective. Mean CT numbers and standard deviations were significantly affected by iMAR (with iMAR: -3.0 ± 21.5 HU, without iMAR: -8.5 ± 24.3 HU; P < 0.001), VMI (VMI: 110 keV -3.6 ± 21.6 HU to 150 keV -1.4 ± 21.2 HU, T3D: -11.7 ± 23.8 HU, and none: -16.9 ± 29.8 HU; P < 0.001), tube potential (120 kV: -4.7 ± 22.8 HU, 140 kV: -6.8 ± 23.0 HU; P = 0.03), and reconstruction kernel (Br36: -5.5 ± 14.2 HU, Br56: -6.8 ± 23.0 HU; P < 0.001). Both iMAR and VMI improved quantitative CT number accuracy and metal rod eccentricity for the steel rod, but iMAR was of limited effectiveness for the aluminum rod. For metal artifact reduction in PCD-CT, a combination of iMAR and VMI at 110 keV demonstrated the strongest artifact reduction of the evaluated options, whereas the impact of reconstruction kernel and tube potential was limited.

Early detection of hypervascularization in hepatocellular carcinoma (≤2 cm) on hepatic arterial phase with virtual monochromatic imaging: Comparison with low-tube voltage CT.

This study aims to assess the diagnostic value of virtual monochromatic image (VMI) at low keV energy for early detection of small hepatocellular carcinoma (HCC) in hepatic arterial phase compared with low-tube voltage (80 kVp) CT generated from dual-energy CT (DE-CT). A total of 107 patients with 114 hypervascular HCCs (≤2 cm) underwent DE-CT, 140 kVp, blended 120 kVp, and 80 kVp images were generated, as well as 40 and 50 keV. CT numbers of HCCs and the standard deviation as image noise on psoas muscle were measured. The contrast-to-noise ratios (CNR) of HCC were compared among all techniques. Overall image quality and sensitivity for detecting HCC hypervascularity were qualitatively assessed by three readers. The mean CT numbers, CNR, and image noise were highest at 40 keV followed by 50 keV, 80 kVp, blended 120 kVp, and 140 kVp. Significant differences were found in all evaluating endpoints except for mean image noise of 50 keV and 80 kVp. Image quality of 40 keV was the lowest, but still it was considered acceptable for diagnostic purposes. The mean sensitivity for detecting lesion hypervascularity with 40 keV (92%) and 50 keV (84%) was higher than those with 80 kVp (56%). Low keV energy images were superior to 80 kVp in detecting hypervascularization of early HCC.

Results of a multicenter 4D computed tomography quality assurance audit: Evaluating image accuracy and consistency

Background and purpose4D Computed Tomography (4DCT) technology captures the location and movement of tumors and nearby organs at risk over time. In this study, a multi-institutional multi-vendor 4DCT audit was initiated to assess the accuracy of current imaging protocols. Materials and methodsTwelve centers, including thirteen scanners performed a 4DCT acquisition of a dynamic thorax phantom using the institution’s own protocol with the in-house breathing monitoring system. Five regular and three irregular breathing patterns were used. Image acquisition and reconstruction were followed by automated image analysis with our in-house developed 4DCT QA program (QAMotion). CT number accuracy, volume deviation, amplitude deviation, and spatial integrity were assessed per pattern using both the segmented volumes and line profiles. ResultsRegular breathing curves showed relatively accurate results across all institutions, with mean volume and CT number deviations and median amplitude deviation below 2%, 5 HU and 2 mm, respectively. Results obtained for irregular patterns showed more variation across the institutions. Volume and CT number deviations co-occurred with a blurring of the sphere, interpolation, or double-structure artifacts that were confirmed through the line profiles. For some of the irregular patterns, amplitude deviations up to 6 mm were observed. Maximum Intensity Projection (MaxIP) correctly captured the applied motion amplitude with deviations across all institutions within 2 mm except for double amplitude pattern. ConclusionsAll centers invited to participate in the audit responded positively, highlighting the need for a comprehensive yet easy-to-execute 4DCT quality assurance program. The largest variances between the results from one institution to another confirmed that a standardized 4DCT audit is warranted.

A comprehensive quality assurance program for four-dimensional computed tomography in radiotherapy.

This study aimed to develop and validate a comprehensive, reproducible and automatic 4DCT Quality Assurance(QA) workflow (QAMotion) that evaluates image accuracy across various regular and irregular breathing patterns. Volume and amplitude deviations, CT number accuracy, and spatial integrity were used as evaluation metrics. For repeatability tests, tolerances were respected with a mean CT number deviation<10 HU, volume deviation<2% and diameter and amplitude deviation<2mm except for irregular amplitude curves for which an amplitude deviation up to 6mm was measured. QAMotion was able to flag image artefacts for our clinical 4DCT system.

Estimating the differences between inter-operator contrast enhancement in cerebral CT angiography.

Computed tomography (CT) angiography (CTA) is a non-invasive imaging method used to detect arteries and examine various brain diseases. When CTA is performed for follow-up or postoperative evaluation, reproducibility of vessel delineation is required. A reproducible and stable contrast enhancement can be achieved by manipulating the factors affecting it. Previous studies have investigated several factors that alter the contrast enhancement of arteries. However, no reports establishing the effect of different operators on contrast enhancement exist. To assess the differences between inter-operator arterial contrast enhancement in cerebral CTA using Bayesian statistical modeling. Image data were obtained using a multistage sampling method from the cerebral CTA scans of patients who underwent the process between January 2015 and December 2018. Several Bayesian statistical models were developed, and the objective variable was the mean CT number of the bilateral internal carotid arteries after contrast enhancement. The explanatory variables were sex, age, fractional dose (FD), and the operator's information. The posterior distributions of the parameters were computed via Bayesian inference using the Markov chain Monte Carlo (MCMC) method, with the Hamiltonian Monte Carlo method employed as the algorithm. The posterior predictive distributions were computed using the posterior distributions of the parameters. Finally, the differences between inter-operator arterial contrast enhancement on the CT number in cerebral CTA were estimated. The posterior distributions showed that all parameters representing the difference between operators included zero at the 95% credible intervals (CIs). The maximum mean difference between inter-operator CT number in the posterior predictive distribution was only 12.59 Hounsfield units (HUs). The Bayesian statistical modeling results suggest that contrast enhancement of cerebral CTA examination between operator-to-operator differences in postcontrast CT number was small compared to those within-operator differences resulting from factors not considered in the model.

The Quantitative Effect of Noise and Object Diameter on Low-Contrast Detectability of AAPM CT Performance Phantom Images

Parameters for determining computed tomography (CT) image quality include noise and low-contrast detectability. Studies on low-contrast detectability using the AAPM CT performance phantom have several limitations, such as the absence of quantitative information on the effect of noise and object size on low-contrast detectability. In this study, the quantitative effect of noise and object diameter on low-contrast detectability were investigated. Images of the American Association of Physicists in Medicine (AAPM) CT performance phantom model 610 were acquired with a tube voltage of 120 kV and tube currents of 50, 100, 150, and 200 mA. The low-contrast section of the AAPM CT performance phantom model 610 has objects with diameters between 2.5 and 7.5 mm. We analysed the mean CT number, noise level, signal-to noise ratio (SNR), and contrast-to-noise ratio (CNR), acquired using MatLab software. The results obtained indicate that noise and object size affect low-contrast detectability. The CNRs increase linearly with increasing of object diameter with R2 of 0.88, 0.67, 0.75, and 0.83 for tube currents of 50, 100, 150 and 200 mA, respectively.

High-Pitch Multienergy Coronary CT Angiography in Dual-Source Photon-Counting Detector CT Scanner at Low Iodinated Contrast Dose.

The aim of this study was to evaluate the high-helical pitch, multienergy (ME) scanning mode of a clinical dual-source photon-counting detector (PCD) computed tomography (CT) and the benefit of virtual monoenergetic images (VMIs) for low-contrast-dose coronary CT angiography (CTA). High-pitch (3.2) ME coronary CTA was performed in PCD-CT in 27 patients using low contrast dose (30 mL of iohexol 350 mg/mL) and in 26 patients at routine contrast dose (60 mL). Low-energy-threshold 120 kV images (also known as T3D images) and 50 kiloelectron volts (50 keV) and 100 kiloelectron volts (100 keV) VMIs were reconstructed using a 1024 × 1024 matrix and 0.6-mm slices. The CT numbers, noise, and contrast-to-noise ratio (CNR) were measured in the ascending aorta (AA), left main coronary artery (LMCA), and distal left anterior descending (LAD) artery. Confidence in grading luminal stenosis with calcific plaque, noncalcific plaque, and stent was evaluated by 2 independent readers on a 0-100 scale (0 the lowest), and a CAD-RADS score was assigned. Image contrast enhancement, sharpness, noise, artifacts, and overall image quality were rated using a 5-point ordinal scale (1 the lowest). The radiation doses (CTDI) in low- and routine-contrast cohorts were 2.5 ± 0.6 mGy and 3.1 ± 1.7 mGy, respectively ( P = 0.12). At all measured locations, the mean CT number was >300 HU in 120 kV (LMCA 382.9 ± 76.2, distal LAD 341.0 ± 53.9, AA 399.5 ± 76.1) and 50 keV images (LMCA 667.5 ± 139.9, distal LAD 578.1 ± 121.5, AA 700.8 ± 142.5) in the low-contrast cohort, with a 96% increase in CT numbers for 50 keV over 120 kV. The CT numbers were significantly higher ( P < 0.0001) in 50 keV than 120 kV and 100 keV VMI. The CNR was also significantly ( P < 0.0001) higher in 50 keV than 120 kV and 100 keV images in all vessels. Confidence in the assessment of luminal stenosis in the presence of calcific plaque was significantly higher ( P = 0.001) with the addition of 100 keV VMI (median score, 100) than using 50 keV alone (median score, 70) and 120 kV (median score, 70) for reader 1, but no significant differences were seen for reader 2 who had same median scores of 100 for all image types. The confidence in the assessment of luminal stenosis within a stent improved with the use of 100 keV images for both readers (reader 1: median scores for 50 + 100 keV = 100, 50 keV = 82.5, 120 kV = 82.5; reader 2: 50 + 100 keV = 100, 50 keV = 90, 120 kV = 90). There were no significant differences in confidence scores for assessment of luminal stenosis from noncalcific plaques for both readers. The reader-averaged qualitative scores for vascular enhancement and overall image quality were significantly higher for 50 keV VMI than for 120 kV images in both low- and routine-contrast dose cohorts. The image sharpness was nonsignificantly higher at 50 keV VMI than 120 kV images, and the artifact score was comparable for 50 keV VMI and 120 kV images. The noise was higher in 50 keV VMI than in 120 kV images. High-pitch ME PCD-CT mode produced diagnostic quality coronary CTA images at low radiation and iodinated contrast doses. The availability of ME VMIs significantly improved the CNR, overall image quality, and confidence in assessment of luminal stenosis in the presence of calcific plaques and stents, and resulted in change of CAD-RADS categories in 9 patients.

The automated measurement of CT number linearity using an ACR accreditation phantom

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R2, were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R 2 > 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation with R 2 = 0.999, and two Hitachi Eclos scanners had the weakest linear correlation with R 2 < 0.99.

Treatment Response Assessment Using Daily Dual-Energy CT during Pre-Operative Radiation Therapy for Early-Stage Breast Cancer

<h3>Purpose/Objective(s)</h3> We have previously reported that the low energy mono-energetic images (MEI) derived from dual-energy CT (DECT) can amplify the detection of radiation-induced changes in tissue. This work aims to investigate the use of low energy MEI derived from daily DECT to assess tumor response during radiation therapy (RT) of early-stage breast cancer. <h3>Materials/Methods</h3> Daily DECT data acquired during routine CT-guided RT delivery for 35 early-stage breast cancer patients enrolled in an institutional prospective pre-operative accelerated partial breast irradiation trial, along with tumor pathological data collected from the biopsy before RT and the excised tissue from the surgery post RT, were analyzed. All patients were treated with 30 Gy in 5 fractions in two weeks. The DECTs were acquired immediately before the fractional dose delivery using an in-room CT scanner equipped with a sequential DE protocol. MEI images across a range of x-ray energies were reconstructed using an image-based material decomposition. Fractional changes in mean CT number (mCTN) in gross tumor volume (GTV) on MEIs were extracted and the changes from the first to the last fraction were correlated to the tumor cellularity changes from the pathological data collected before and after RT using the Student's T-Test. As a control, the mCTN changes on MEIs in a region far from irradiated volumes were calculated. The results from the MEI data were compared to those obtained from the standard 120 kVp CTs of the same subjects. <h3>Results</h3> The data from 20 out of 35 patients were found to be suitable for this analysis. The greatest enhancement in GTV mCTN change was observed for MEI at 40 keV, the lowest available energy. The average change in GTV mCTN between the first and last fractions of treatments was 2.9 times larger for 40 keV MEIs than that for the standard 120 kVp CTs. For 11 out of the 20 patients, the GTV mCTN changes were greater than 15 HU on the 40 keV MEI and their average change in cellularity was 36.5 ± 10.3%. For the remaining 9 patients, the GTV mCTN changes were less than 15 HU on the 40 keV MEI and the average change in cellularity was 14.4 ± 9.8%. The correlation between change in GTV mCTN and change in cellularity was found to be significant (p=0.02). The average mCTN change in the control region was of 0.2 ± 2.0 HU on the 40 keV for all 20 patients. <h3>Conclusion</h3> Tumor pathological response for early-stage breast cancer treated with pre-operative accelerated partial breast irradiation can be predicted by the change of quantitative features, such as mean CT number, on low energy MEI derived from DECT acquired during the radiation treatment. Compared to the standard CT, the low energy MEIs substantially amplify the changes of mCTN, enhancing the viability of using quantitative CT features as a biomarker for early RT response assessment.

Detection of Kidney Impairment Using Correlation of CT Number and ADC

<h3>Purpose/Objective(s)</h3> It has been reported that a correlation exists between the mean apparent diffusion coefficient (ADC) measured from MRI and the mean CT number (CTN) in kidney. This correlation can be interpreted as both ADC and CTN reflect extracellular volume (ECV) ratio. This study aims to investigate the correlation of ADC and estimated glomerular filtration ratio (eGFR) and the use of ADC-CTN correlation to detect kidney impairment. <h3>Materials/Methods</h3> Data acquired for radiation therapy (RT) planning of twenty-two patients with abdominal/pelvic cancers were analyzed retrospectively in two groups. Group I included sixteen patients with no history of severe kidney disease and Group II had six patients having apparent issues including kidney stones, chronic disease, or tumor on kidney. CT images were acquired during CT simulation using a scanner at 120 kVp and the ADC maps were derived from the diffusion weighted imaging (DWI) acquired immediately before the first fraction of MRI guided RT using a 1.5T MR-Linac approximately 3 weeks following CT simulation. Left and right kidneys excluding renal pelvis were contoured on both the ADC and CT images. The mean values of the ADC and CTN from the voxels within the contours were analyzed using linear regression. The eGFR was calculated based on serum creatinine level measured on the same day as DWI. The correlation between eGFR and mean ADC value of left and right kidneys was evaluated with Pearson test. The linear regression fit was used to correct the ADC values to eliminate the filtration effect to show the ECV effect only. <h3>Results</h3> The CTN and ADC over the Group I patients had an average 29.3 HU and 2.00 µm/s² ranging from 23.0 HU to 38.2 HU and 1.79 µm/s² to 2.16 µm/s², respectively. The eGFR of all patients ranged from 40 to 129 mL/min/1.73 sqm, and had a moderate correlation to ADC (coefficient=0.46, p=0.04). Using the linear fit of ADC-eGFR data, the ADC values were corrected by extrapolating their corresponding eGFR to 0. This correction did not alter the strong correlation between the ADC and CTN (Pearson coefficient = -0.83 with and -0.84 without correction, p<0.0001) but dropped the line of linear fit by 0.2 µm/s². There was no significant difference in eGFR, CTN, or ADC between Group I and II. However, almost all patients in Group II (5 out of 6) had one kidney data residing within the +/- 0.15 µm/s² region of fit line while the other one fell out of the region as an outlier, identified as the actual problematic kidney in the medical record (4 of 5). In two cases kidney stones were not found when the images were taken but in 6-12 months posteriorly. <h3>Conclusion</h3> Our study showed that eGFR affected ADC in a minor way and would not change the strong negative correlation between ADC and CTN. Impaired kidneys tend to fall out of the ADC-CTN correlation line as outliers, which were not identified using eGFR, CTN, or ADC alone. These results indicate that ADC-CTN correlation is a new sensitive method to detect kidney impairment and has a potential application in monitoring radiation toxicity.

Development of a classification method for mild liver fibrosis using non-contrast CT image.

Detection of early-stage liver fibrosis has direct clinical implications on patient management and treatment. The aim of this paper is to develop a non-invasive, cost-effective method for classifying liver disease between "non-fibrosis" (F0) and "fibrosis" (F1-F4), and to evaluate the classification performance quantitatively. Image data from 75 patients who underwent a simultaneous liver biopsy and non-contrast CT examination were used for this study. Non-contrast CT image texture features such as wavelet-based features, standard deviation of variance filter, and mean CT number were calculated in volumes of interest (VOIs) positioned within the liver parenchyma. In addition, a combined feature was calculated using logistic regression with L2-norm regularization to further improve fibrosis detection. Based on the final pathology from the liver biopsy, the patients were labelled either as "non-fibrosis" or "fibrosis". Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUROC), specificity, sensitivity, and accuracy were determined for the algorithm to differentiate between "non-fibrosis" and "fibrosis". The combined feature showed the highest classification performance with an AUROC of 0.86, compared to the wavelet-based feature (AUROC, 0.76), the standard deviation of variance filter (AUROC, 0.65), and mean CT number (AUROC, 0.84). The combined feature's specificity, sensitivity, and accuracy were 0.66, 0.88, and 0.76, respectively, showing the most promising results. A new non-invasive and cost-effective method was developed to classify liver diseases between "non-fibrosis" (F0) and "fibrosis" (F1-F4). The proposed method makes it possible to detect liver fibrosis in asymptomatic patients using non-contrast CT images for better patient management and treatment.

Improved visualization of the wrist at lower radiation dose with photon-counting-detector CT.

To compare the image quality of ultra-high-resolution wrist CTs acquired on photon-counting detector CT versus conventional energy-integrating-detector CT systems. Participants were scanned on a photon-counting-detector CT systemafter clinical energy-integrating detector CTs. Energy-integrating-detector CT scan parameters: comb filter-based ultra-high-resolution mode, 120kV, 250 mAs, Ur70 or Ur73 kernel, 0.4- or 0.6-mm section thickness. Photon-counting-detector CT scan parameters: non-comb-based ultra-high-resolution mode, 120kV, 120 mAs, Br84 kernel, 0.4-mm section thickness. Two musculoskeletal radiologists blinded to CT system, scored specific osseous structures using a 5-point Likert scale (1 to 5). The Wilcoxon rank-sum test was used for statistical analysis of reader scores. Paired t-test was used to compare volume CT dose index, bone CT number, and image noise between CT systems. P-value < 0.05 was considered statistically significant. Twelve wrists (mean participant age 55.3 ± 17.8, 6 females, 6 males) were included. The mean volume CT dose index was lower for photon-counting detector CT (9.6 ± 0.1mGy versus 19.0 ± 6.7mGy, p < .001). Photon-counting-detector CT images had higher Likert scores for visualization of osseous structures (median score = 4, p < 0.001). The mean bone CT number was higher in photon-counting-detector CT images (1946 ± 77 HU versus 1727 ± 49 HU, p < 0.001). Conversely, there was no difference in the mean image noise of the two CT systems (63 ± 6 HU versus 61 ± 6 HU, p = 0.13). Ultra-high-resolution imaging with photon-counting-detector CT depicted wrist structures more clearly than conventional energy-integrating-detector CT despite a 49% radiation dose reduction.