Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Can absolute arterial phase hyperenhancement improve sensitivity of detection of hepatocellular carcinoma in indeterminate nodules on CT?

OBJECTIVES: To determine if quantitative assessment of relative (R) and absolute (A) arterial phase hyperenhancement (APHE) and washout (WO) applied to indeterminate nodules on CT would improve the overall sensitivity of detection of hepatocellular carcinoma (HCC). METHODS: One-hundred and fourteen patients (90 male; mean age, 65 years) with 210 treatment-naïve HCC nodules (190 HCCs, 20 benign) who underwent 4-phase CT were included in this retrospective study. Four radiologists independently assigned a qualitative LR (LI-RADS) category per nodule. LR-3/4 nodules were then quantitatively analyzed by the 4 readers, placing ROIs within nodules and adjacent liver parenchyma. A/R-APHE and WO were calculated, and per-reader sensitivity and specificity updated. Interobserver agreement and AUCs were calculated per reader. RESULTS: Qualitative readers 1-4 categorized 57, 69, 57, and 63 nodules as LR-3/4 respectively with moderate to substantial agreement in LR category (kappa 0.56-0.69, p < 0.0001); their diagnostic performances in the detection of HCC were 80%, 73.2%, 77.4%, and 77.4% sensitivity, and 100%, 95%, 70%, and 100% specificity, respectively. A threshold of ≥ 20 HU for A-APHE increased overall sensitivity of HCC detection by 0.5-3.1% without changing specificity for the subset of nodules APHE - /WO + on qualitative read, with 2, 6, 6, and 1 additional HCC detected by readers 1-4. Relative and various A-WO formulae and thresholds all increased sensitivity, but with a drop in specificity for some/all readers. CONCLUSION: Quantitatively assessed A-APHE showed potential to increase sensitivity and maintain specificity of HCC diagnosis when selectively applied to indeterminate nodules demonstrating WO without subjective APHE. Quantitatively assessed R and A-WO increased sensitivity, however reduced specificity. CLINICAL RELEVANCE STATEMENT: A workflow using selective quantification of absolute arterial enhancement is routinely employed in the CT assessment of renal and adrenal nodules. Quantitatively assessed absolute arterial enhancement is a simple tool which may be used as an adjunct to help increase sensitivity and maintain specificity of HCC diagnosis in indeterminate nodules demonstrating WO without subjective APHE. KEY POINTS: • In indeterminate nodules categorized as LI-RADS 3/4 due to absent subjective arterial phase hyperenhancement, a cut-off for absolute arterial phase hyperenhancement of ≥ 20 HU may increase the overall sensitivity of detection of HCC by 0.5-3.1% without affecting specificity. • Relative and various absolute washout formulae and cut-offs increased sensitivity of HCC detection, but with a drop in specificity for some/all readers.

Sulfonamide Per- and Polyfluoroalkyl Substances Can Impact Microorganisms Used in Aromatic Hydrocarbon and Trichloroethene Bioremediation.

Per- and polyfluoroalkyl substances (PFASs) from aqueous film forming foams (AFFFs) can hinder bioremediation of co-contaminants such as trichloroethene (TCE) and benzene, toluene, ethylbenzene, and xylene (BTEX). Anaerobic dechlorination can require bioaugmentation of Dehalococcoides, and for BTEX, oxygen is often sparged to stimulate in situ aerobic biodegradation. We tested PFAS inhibition to TCE and BTEX bioremediation by exposing an anaerobic TCE-dechlorinating coculture, an aerobic BTEX-degrading enrichment culture, and an anaerobic toluene-degrading enrichment culture to n-dimethyl perfluorohexane sulfonamido amine (AmPr-FHxSA), perfluorohexane sulfonamide (FHxSA), perfluorohexanesulfonic acid (PFHxS), or nonfluorinated surfactant sodium dodecyl sulfate (SDS). The anaerobic TCE-dechlorinating coculture was resistant to individual PFAS exposures but was inhibited by >1000× diluted AFFF. FHxSA and AmPr-FHxSA inhibited the aerobic BTEX-degrading enrichment. The anaerobic toluene-degrading enrichment was not inhibited by AFFF or individual PFASs. Increases in amino acids in the anaerobic TCE-dechlorinating coculture compared to the control indicated stress response, whereas the BTEX culture exhibited lower concentrations of all amino acids upon exposure to most surfactants (both fluorinated and nonfluorinated) compared to the control. These data suggest the main mechanisms of microbial toxicity are related to interactions with cell membrane synthesis as well as protein stress signaling.

Mineralization of a Fully Halogenated Organic Compound by Persulfate under Conditions Relevant to in Situ Reduction and Oxidation: Reduction of Hexachloroethane by Ethanol Addition Followed by Oxidation.

Fully halogenated compounds are difficult to remediate by in situ chemical oxidation (ISCO) because carbon-halogen bonds react very slowly with the species that typically initiate contaminant transformation: sulfate radical (SO4•-) and hydroxyl radical (•OH). To enable the remediation of this class of contaminants by persulfate (S2O82-)-based ISCO, we employed a two-phase process to dehalogenate and oxidize a representative halogenated compound (i.e., hexachloroethane). In the first phase, a relatively high concentration of ethanol (1.8 M) was added, along with concentrations of S2O82- that are typically used for ISCO (i.e., 450 mM). Hexachloroethane underwent rapid dehalogenation when carbon-centered radicals produced by the reaction of ethanol and radicals formed during S2O82- decomposition reacted with carbon-halogen bonds. Unlike conventional ISCO treatment, hexachloroethane transformation and S2O82- decomposition took place on the time scale of days without external heating or base addition. The presence of O2, Cl-, and NO3- delayed the onset of hexachloroethane transformation when low concentrations of S2O82- (10 mM) were used, but these solutes had negligible effects when S2O82- was present at concentrations typical of in situ remediation (450 mM). The second phase of the reaction was initiated after most of the ethanol had been depleted when thermolytic S2O82- decomposition resulted in production of SO4•- that oxidized the partially dehalogenated transformation products. With proper precautions, S2O82--based ISCO with ethanol could be a useful remediation technology for sites contaminated with fully halogenated compounds.

Epidemiology of occupational injuries in construction workers between 2009 and 2018 in South Korea.

BACKGROUND: This study aimed to investigate the characteristics of occupational injuries based on fatality, sex, and classification of occupations among construction workers using workers' compensation (WC) insurance data in South Korea. METHODS: We collected WC insurance data from the Korea Workers' Compensation & Welfare Service for all construction workers between 2009 and 2018. Data from 158,947 accepted claims for occupational injury were extracted, and the demographic features, occupational injury types, and annual trends were analyzed for fatal and nonfatal cases. The annual incidence and mortality trends of occupational injury were estimated using negative binomial regression and Poisson regression models, for injury incidence and mortality respectively. RESULTS: Among a total of 158,947 occupational injury cases, there were 155,772 (98%) nonfatal injuries and 3175 (2%) fatal injuries. For all occupational injuries, Construction Elementary Workers (6th Korean Standard Classification of Occupations (KSCO) 910; 45.7%) was the most frequent occupation, followed by Construction-Related Technical Workers (6th KSCO 772; 39.2%). The most frequent injury type was a fracture, followed by ruptures or lacerations and contusions. The incidence of all occupational injuries increased from 700.36 per 100,000 persons in 2009 to 1,195.98 per 100,000 persons in 2018. Further, deaths from injuries at work followed a significantly increasing annual trend [mortality rate ratio 1.04 (95% CI: 1.03-1.05)] from 2009 to 2018. CONCLUSION: The over two-thirds increased incidence of occupational injuries and significantly increasing mortality trends for occupational injuries during the last 10 years indicate the need for aggressive intervention in occupational safety and health management within the Korean construction industry.

Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs.

This study considers an improved likelihood probability in multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs). MIMO systems using one-bit ADCs are known to exhibit from performance degradation because of inaccurate likelihood probabilities. To overcome this degradation, the proposed method leverages the detected symbols to estimate the true likelihood probability by combining the initial likelihood probability. An optimization problem is formulated to minimize the mean-squared error between the true and combined likelihood probabilities, and a solution is derived using the least-squares method. Simulation results show that the proposed method obtains a signal-to-noise gain of approximately 0.3 dB to achieve a frame error rate of 10-1 compared to conventional methods. This improvement in performance is attributed to the enhanced reliability of the likelihood probability.

Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems.

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

Non-Orthogonal Multiple Access with One-Bit Analog-to-Digital Converters Using Threshold Adaptation.

In digital communication systems featuring high-resolution analog-to-digital converters (ADCs), the utilization of successive interference cancellation and detection can enhance the capacity of a Gaussian multiple access channel (MAC) by combining signals from multiple transmitters in a non-orthogonal manner. Conversely, in systems employing one-bit ADCs, it is exceedingly difficult to eliminate non-orthogonal interference using digital signal processing due to the considerable distortion present in the received signal when employing such ADCs. As a result, the Gaussian MAC does not yield significant capacity gains in such cases. To address this issue, we demonstrate that, under a given deterministic interference, the capacity of a one-bit-quantized channel becomes equivalent to the capacity without interference when an appropriate threshold value is chosen. This finding suggests the potential for indirect interference cancellation in the analog domain, facilitating the proposition of an efficient successive interference cancellation and detection scheme. We analyze the achievable rate of the proposed scheme by deriving the mutual information between the transmitted and received signals at each detection stage. The obtained results indicate that the sum rate of the proposed scheme generally outperforms conventional methods, with the achievable upper bound being twice as high as that of the conventional methods. Additionally, we have developed an optimal transmit power allocation algorithm to maximize the sum rate in fading channels.

C2RL: Convolutional-Contrastive Learning for Reinforcement Learning Based on Self-Pretraining for Strong Augmentation.

Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a self-supervised learning framework with data augmentation in the reinforcement learning architecture can promote generalization to a certain extent. However, excessively large changes in the input images may disturb reinforcement learning. Therefore, we propose a contrastive learning method that can help manage the trade-off relationship between the performance of reinforcement learning and auxiliary tasks against the data augmentation strength. In this framework, strong augmentation does not disturb reinforcement learning and instead maximizes the auxiliary effect for generalization. Results of experiments on the DeepMind Control suite demonstrate that the proposed method effectively uses strong data augmentation and achieves a higher generalization than the existing methods.

Automated Laser-Fiber Coupling Module for Optical-Resolution Photoacoustic Microscopy.

Photoacoustic imaging has emerged as a promising biomedical imaging technique that enables visualization of the optical absorption characteristics of biological tissues in vivo. Among the different photoacoustic imaging system configurations, optical-resolution photoacoustic microscopy stands out by providing high spatial resolution using a tightly focused laser beam, which is typically transmitted through optical fibers. Achieving high-quality images depends significantly on optical fluence, which is directly proportional to the signal-to-noise ratio. Hence, optimizing the laser-fiber coupling is critical. Conventional coupling systems require manual adjustment of the optical path to direct the laser beam into the fiber, which is a repetitive and time-consuming process. In this study, we propose an automated laser-fiber coupling module that optimizes laser delivery and minimizes the need for manual intervention. By incorporating a motor-mounted mirror holder and proportional derivative control, we successfully achieved efficient and robust laser delivery. The performance of the proposed system was evaluated using a leaf-skeleton phantom in vitro and a human finger in vivo, resulting in high-quality photoacoustic images. This innovation has the potential to significantly enhance the quality and efficiency of optical-resolution photoacoustic microscopy.

Patterns of Relapse and Complications of Immunoglobulin G4-Related Disease.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-RD) is a multisystemic fibroinflammatory condition potentially resulting in organ dysfunction. We aimed to evaluate imaging characteristics of disease relapse and complications in this cohort of patients. METHODS: This was a cohort study of IgG4-RD patients imaged between 2010 and 2020. Radiological manifestations of disease activity (remission/stability vs. relapse and complications) were correlated with clinical symptoms. Univariate analyses were performed with χ2 , Fisher exact, and Mann-Whitney U tests. Times to relapse and organ atrophy were studied with Kaplan-Meier analyses. RESULTS: A total of 69 patients had imaging surveillance over a median duration of 47 months. Radiological relapse occurred in 50.7% (35/69) with median time to relapse at 74 months (95% confidence interval, 45-122 months); 42.8% (15/35) of this cohort had different-site relapse with the following recognized primary-secondary patterns: pancreas-hepatobiliary ( p = 0.005), hepatobiliary-pancreas ( p = 0.013), and periaortitis-mesenteric ( p = 0.006). Clinical symptoms were significantly associated with imaging characteristics ( p < 0.001). Abdominal complications were detected in 52.2% (36/69) of patients, mostly solid organ atrophy (97.2% [35/36]). New-onset diabetes was more likely in pancreatic IgG4-RD (n = 51) when accompanied by gland atrophy (4/21 vs. 0/30 nonatrophy, p = 0.024). CONCLUSION: Radiological relapse of IgG4-RD is common over prolonged imaging surveillance and is significantly associated with symptomatic relapse. A multisystem review to detect new/different sites of disease and abdominal complications may help predict future organ dysfunction.

Contrast-enhanced US of the Liver and Kidney: A Problem-solving Modality.

Contrast-enhanced US (CEUS) has an important role as a supplement to CT or MRI in clinical practice. The main established utilizations are in the liver and the kidney. The primary advantages of CEUS compared with contrast-enhanced CT or MRI relate to its superior contrast resolution, real-time continuous scanning, pure intravascular nature, portability, and safety-especially in patients with renal impairment or CT or MRI contrast agent allergy. This article focuses on the use of CEUS in the liver and kidney.

Cooperative DF Protocol for MIMO Systems Using One-Bit ADCs.

This study considers a detection scheme for cooperative multi-input-multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs) in a decode-and-forward (DF) relay protocol. The use of one-bit ADCs is a promising technique for reducing the power consumption, which is necessary for supporting future wireless systems comprising a large number of antennas. However, the use of a large number of antennas remains still limited to mobile devices owing to their size. Cooperative communication using a DF relay can resolve this limitation; however, detection errors at the relay make it difficult to employ cooperative communication directly. This difficulty is more severe in a MIMO system using one-bit ADCs due to its nonlinear nature. To efficiently address the difficulty, this paper proposes a detection scheme that mitigates the error propagation effect. The upper bound of the pairwise error probability (PEP) of one-bit ADCs is first derived in a weighted Hamming distance form. Then, using the derived PEP, the proposed detection for the DF relay protocol is derived as a single weighted Hamming distance. Finally, the complexity of the proposed detection is analyzed in terms of real multiplications. The simulation results show that the proposed detection method efficiently mitigates the error propagation effect but has a relatively low level of complexity when compared to conventional detection methods.

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback.

The spherical-cap approximation of vector quantization (SCVQ) is an analytical model used for the mathematical analysis of multiple-input multiple-output (MIMO) systems with limited feedback. SCVQ closely emulates the distribution of the quantization error induced by the finite-rate quantization of a channel using a simple and analytically tractable approach. However, the conventional SCVQ model is not applicable when antenna-combining schemes such as quantization-based combining (QBC) are considered. Because QBC is an effective antenna-combining method that minimizes channel quantization errors, it can be adopted for various practical MIMO broadcast systems. Nevertheless, evaluating the performance of QBC-based MIMO systems with an explicit codebook can be extremely difficult, depending on the system complexity. To resolve this, this study generalizes the conventional SCVQ to be compatible with the QBC. The proposed generalized version of the SCVQ effectively emulates the quantization error obtained using QBC, while enabling a simple simulation independent of the number of feedback bits and mathematically tractable analysis. We validate the effectiveness of the proposed model by presenting a wireless communication application based on a dense cellular network.

A Low-Complexity Algorithm for a Reinforcement Learning-Based Channel Estimator for MIMO Systems.

This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation.

An Enhanced Affine Projection Algorithm Based on the Adjustment of Input-Vector Number.

An enhanced affine projection algorithm (APA) is proposed to improve the filter performance in aspects of convergence rate and steady-state estimation error, since the adjustment of the input-vector number can be an effective way to increase the convergence rate and to decrease the steady-state estimation error at the same time. In this proposed algorithm, the input-vector number of APA is adjusted reasonably at every iteration by comparing the averages of the accumulated squared errors. Although the conventional APA has the constraint that the input-vector number should be integer, the proposed APA relaxes that integer-constraint through a pseudo-fractional method. Since the input-vector number can be updated at every iteration more precisely based on the pseudo-fractional method, the filter performance of the proposed APA can be improved. According to our simulation results, it is demonstrated that the proposed APA has a smaller steady-state estimation error compared to the existing APA-type filters in various scenarios.

The added value of contrast-enhanced ultrasound in evaluation of indeterminate small solid renal masses and risk stratification of cystic renal lesions.

OBJECTIVES: To investigate accuracy of contrast-enhanced ultrasound (CEUS) to characterize indeterminate small solid renal masses (sSRMs), excluding lipid-rich AMLs, and cystic renal masses (CRMs) according to the proposed Bosniak Classification 2019 MATERIALS AND METHODS: CEUS of pathology-proven CRMs and sSRMs (without definite enhancement or macroscopic fat on CT/MRI), and CRMs with ≥18 months follow-up were retrospectively reviewed. Two radiologists blindly categorized CRMs according to new Bosniak Classification on CT/MRI. On CEUS, two other radiologists evaluated arterial-phase enhancement of sSRMs relative to renal cortex and categorized CRMs following new Bosniak Classification. Fisher's exact/chi-squared test was used to compare categorical variables, and Cohen κ statistics for inter-observer agreement RESULTS: A total of 237 patients had 241 lesions: 161 pathology-proven sSRMs (122 malignant and 39 benign), 29 pathology-proven CRMs, 51 CRMs with adequate follow-up. Arterial-phase enhancement < renal cortex predicted malignancy with specificity of 97.4% (38/39) (CI 85.6-99.9%), and positive predictive value (PPV) of 98.2% (54/55) (CI 90.4-99.9%). Inter-observer kappa was 0.95. In pathology-proven CRMS, sensitivity of CEUS vs CT/MRI was 100% (15/15) (CI 79.6-100%) vs 60% (9/15) (CI 35.8-80.1%) (p value = .002) and negative predictive value (NPV) 100% (2/2) (CI 17.8-100%) vs 25% (2/8 ) (CI 4.4-59.1%) (p value < 0.0001), with similar specificity (50%) and PPV- 88.2% (15/17) (CI 65.7-97.9%) vs 81.8% (9/11) (CI 52.3-96.8%) ( p value = 0.586). Bosniak Classification inter-observer kappa was 0.92 for CEUS vs 0.68 for CT/MRI (p value = 0.009). CONCLUSION: In our cohort, CEUS had high specificity and PPV to diagnose RCC in sSRMs excluding lipid-rich AML. CEUS had significantly higher sensitivity/NPV to diagnose malignancy in CRMs as compared to CT/MRI. KEY POINTS: • Once lipid-rich AML is excluded by the other modalities, sSRM arterial phase hypo-enhancement relative to renal cortex on CEUS yielded high specificity (97.4%) and PPV (98.2%) to diagnose RCC. • When applying the proposed Bosniak Classification 2019, CEUS showed higher sensitivity compared to CT/MRI (100% vs 60%), p value=.0024, in the stratification of cystic renal masses to diagnose malignancy. • CEUS may reduce the number of CT/MRI Bosniak IIF lesions by assigning them to either II or III/IV categories.

Characterization of Indeterminate Liver Lesions on CT and MRI With Contrast-Enhanced Ultrasound: What Is the Evidence?

OBJECTIVE. CT or MRI is most commonly used for characterizing focal hepatic lesions. However, findings on CT and MRI are occasionally indeterminate. Contrast-enhanced ultrasound (CEUS), with its unique characteristics as a purely intravascular contrast agent and real-time evaluation of enhancement, is a useful next step. The purpose of this article is to review the evidence for performing CEUS in the assessment of indeterminate hepatic lesions seen on CT and MRI. CONCLUSION. CEUS is a useful problem-solving tool in the evaluation of liver lesions that are indeterminate on CT and MRI. Uses include detection of arterial phase hyperenhancement; differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma; determination of benign versus malignant tumor thrombus, benign versus neoplastic cystic hepatic lesions, and hepatocellular adenoma versus focal nodular hyperplasia; and monitoring for recurrence in postablative therapies. CEUS can help establish a confident diagnosis and determine the need for further invasive diagnosis or treatment.

Antenna Combining for Interference Limited MIMO Cellular Networks.

This paper considers a downlink cellular network where multi-antenna base stations (BSs) simultaneously serve their associated multi-antenna users. Each BS is distributed according to a homogeneous Poisson point process and uses zero-forcing beamforming for spatial division multiplexing with partial channel state information (CSI). During downlink transmission, each user combines the multiple antenna outputs and quantizes the CSI to feed back to its associated BS. Specifically, this paper focuses on antenna combining at the receiver. Conventional quantization-based combining (QBC) effectively reduces the quantization error; however, inter-cell interference in the cellular networks degrades the QBC gain. This degradation is analyzed using a spherical-cap approximation of vector quantization (SCVQ). From the SCVQ, the ergodic spectral efficiency and the optimal number of feedback bits are investigated, and it is shown that the QBC degrades the gain of the effective channel. To address this problem, an optimization solution is proposed that selects the antenna combining to maximize the spectral efficiency. The solution is also derived by considering the expected beamforming vectors of other cells. It is demonstrated by simulation that the proposed solution outperforms the conventional methods.

Reproducibility of 2 Liver 2-Dimensional Shear Wave Elastographic Techniques in the Fasting and Postprandial States.

OBJECTIVES: The purpose of this study was to compare the reliability and agreement of 2 methods of 2-dimensional (2D) shear wave elastography (SWE) on liver stiffness in healthy volunteers. We also assessed effects of the prandial state and operator experience on measurements. METHODS: Two operators, 1 experienced and 1 novice, independently examined 20 healthy volunteers with 2D SWE on 2 ultrasound machines (Aixplorer [SuperSonic Imagine, Aix-en-Provence, France] and Aplio 500 [Canon Medical Systems Corporation, Otawara, Japan]). Volunteers were scanned 8 times by the operators using both machines in fasting and postprandial states. Agreement was evaluated by a Bland-Altman analysis, and the correlation was assessed by the Pearson correlation and intraclass correlation coefficients (ICCs). An analysis of variance was conducted to determine the contribution of the machine, prandial state, and operator experience to the variability. RESULTS: Agreement assessed by Bland-Altman plots showed no statistically significant difference in measured liver stiffness between the machines (mean difference, -0.8%; 95% confidence interval, -3.7%, 2.1%), with a critical difference of 1.36 kPa. The correlation was good to excellent for both the crude overall Pearson coefficient and the ICC, both measuring 0.88 (95% confidence interval, 0.82, 0.92). Subclass ICCs for the fasting state, postprandial state, novice operator, and experienced operator were 0.89, 0.88, 0.90, and 0.86, respectively. The 2-way mixed effect analysis of variance showed that the volunteers accounted for 86.3% of variation in median liver stiffness, with no statistically significant contribution from operator experience, the prandial state, or the machine (P = .108, .067, and .296, respectively). CONCLUSIONS: Our study showed that the 2D SWE techniques had a high degree of reliability and agreement in measurement of liver stiffness in a healthy population. Operator experience and the prandial state did not impart significant variability to stiffness measurements.