Spectral Orbits: Combining Spectral Imaging and Non-Circular Orbits for Interventional CBCT.

Cone-beam CT imaging using non-circular orbits has been demonstrated to be effective in reducing artifacts around metal. With the increasing interest in spectral imaging in the interventional suite, there are potential advantages to combine both technologies to yield further image quality benefits. We simulated a neuro-interventional application where imaging around the embolization is challenged by metal artifacts and the differentiation of bleeds and contrast extravasation is difficult with single-energy imaging. The imaging system was simulated with a dual-layer detector and different sinusoidal orbits. Material decomposition used a projection-domain approach followed by a model-based reconstruction of the density line integrals of each basis. The spectral non-circular orbits acquisitions were compared with single-energy circular, single-energy non-circular, and spectral circular orbits. Results using spectral non-circular orbit contain minimal metal artifacts and allow the differentiation of bleeds and contrast extravasation, demonstrating the potential of the combined technologies.

Clinician and patient experiences with shared decision-making to promote daily arm use for individuals with chronic stroke: an exploratory qualitative study.

Purpose: To explore the attitudes and experiences of clinicians and individuals with chronic stroke on the use of shared decision-making (SDM) during upper extremity rehabilitation to improve daily arm use in the home environment. Specifically, we aimed to describe clinician and client perspectives regarding the facilitators and barriers to using SDM within the context of a self-directed upper extremity intervention for individuals living in the community with chronic stroke. Methods: Data were collected within the context of an interventional study examining the feasibility of the Use My Arm-Remote intervention. Focus group interviews were conducted with the clinicians (n = 3) providing the intervention and individual semi-structured interviews with the participants (n = 15) of the study. All interview data were collected after the end of the intervention period. Data were analyzed using thematic analysis. Results: The following themes were identified: (1) Equal partnership; (2) Enhancing clinician confidence; and (3) This is different. Facilitators and barriers were identified within each theme. Key facilitators for clinicians were competence with SDM and patient characteristics; while facilitators for patients were open and trusting relationships with clinicians and personalized experience. Key barriers to SDM for clinicians were lack of expertise in SDM and participant buy in; while patients identified a lack of foundational knowledge of stroke rehabilitation as a potential barrier. Conclusions: Key barriers were analyzed using the consolidated framework for advancing implementation science to interpret results and identify strategies for enhancing the implementation of SDM in a virtual setting. The CFIR-ERIC tool highlighted the need for targeted educational meetings and materials to address the training and educational needs of both clinicians and patients for future iterations of this intervention.

Fourier Diffusion for Sparse CT Reconstruction.

Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.

Surgeon as teacher Scott Warner Woods Memorial Lecture Midwest Surgical Association Mackinac Island, Michigan.

Though a key component of the surgical training paradigm envisioned by Halstead and critical for the generation of future surgeons, teaching surgery is increasingly challenging. The qualities and practices of effective educators have been published and can be acquired through practice. Teaching allows us to connect with our learners and the larger surgical community.

CT Material Decomposition using Spectral Diffusion Posterior Sampling.

In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach combines sophisticated prior knowledge from unsupervised training with a rigorous physical model of the measurements. A faster and more stable variant is proposed that uses a "jumpstarted" process to reduce the number of time steps required in the reverse process and a gradient approximation to reduce the computational cost. Performance is investigated for two spectral CT systems: dual-kVp and dual-layer detector CT. On both systems, DPS achieves high Structure Similarity Index Metric Measure(SSIM) with only 10% of iterations as used in the model-based material decomposition(MBMD). Jumpstarted DPS (JSDPS) further reduces computational time by over 85% and achieves the highest accuracy, the lowest uncertainty, and the lowest computational costs compared to classic DPS and MBMD. The results demonstrate the potential of JSDPS for providing relatively fast and accurate material decomposition based on spectral CT data.

Joint Material Decomposition and Scatter Estimation for Spectral CT.

Accurate scatter correction is essential to obtain highquality reconstructions in computed tomography. While many correction strategies for this longstanding issue have been developed, additional efforts may be required for spectral CT imaging - which is particularly sensitive to unmodeled biases. In this work we explore a joint estimation approach within a one-step model-based material decomposition framework to simultaneously estimate material densities and scatter profiles in spectral CT. The method is applied to simulated phantom data obtained using a parametric additive scatter mode, and compared to the unmodeled scatter scenario. In these preliminary experiments, We find that this joint estimation approach has the potential to significantly reduce artifacts associated with unmodeled scatter and to improve material density estimates.

Clinical and genetic delineation of autosomal recessive and dominant ACTL6B-related developmental brain disorders.

PURPOSE: This study aims to comprehensively delineate the phenotypic spectrum of ACTL6B-related disorders, previously associated with both autosomal recessive and autosomal dominant neurodevelopmental disorders. Molecularly, the role of the nucleolar protein ACTL6B in contributing to the disease has remained unclear. METHODS: We identified 105 affected individuals, including 39 previously reported cases, and systematically analysed detailed clinical and genetic data for all individuals. Additionally, we conducted knockdown experiments in neuronal cells to investigate the role of ACTL6B in ribosome biogenesis. RESULTS: Biallelic variants in ACTL6B are associated with severe-to-profound global developmental delay/intellectual disability (GDD/ID), infantile intractable seizures, absent speech, autistic features, dystonia, and increased lethality. De novo monoallelic variants result in moderate-to-severe GDD/ID, absent speech, and autistic features, while seizures and dystonia were less frequently observed. Dysmorphic facial features and brain abnormalities, including hypoplastic corpus callosum, parenchymal volume loss/atrophy, are common findings in both groups. We reveal that in the nucleolus, ACTL6B plays a crucial role in ribosome biogenesis, in particular in pre-rRNA processing. CONCLUSION: This study provides a comprehensive characterization of the clinical spectrum of both autosomal recessive and dominant forms of ACTL6B-associated disorders. It offers a comparative analysis of their respective phenotypes provides a plausible molecular explanation and suggests their inclusion within the expanding category of 'ribosomopathies'.

SARS-CoV-2 seroprevalence in people living with HIV in South Sudan.

Objectives: The burden of SARS-CoV-2 infection in people living with HIV (PLHIV) in South Sudan is unknown. Methods: We conducted a cross-sectional seroprevalence survey of SARS-CoV-2 immunoglobulin (Ig) G antibodies and other diseases of public health importance (strongyloidiasis, toxoplasmosis) in PLHIV in South Sudan during April 1, 2020-April 30, 2022. We used a multiplex SARS-CoV-2 immunoassay to detect IgG antibodies targeting the SARS-CoV-2 spike, receptor binding domain, and nucelocapsid (N) proteins, and antigens for other pathogens (Strongyloides stercoralis and Toxoplasma gondii). Results: Among 3518 samples tested, seroprevalence of IgG antibodies to SARS-CoV-2 spike protein and receptor binding domain 591 and nucleocapsid ranged from 1.4% (95% confidence interval [CI]: 0.9-2.1%) in April-June 2020 to 53.3% (95% CI: 49.5-57.1%) in January-March 2022. The prevalence of S. stercoralis IgG ranged between 27.3% (95% CI: 23.4-31.5%) in October-December 2021 and 47.2% (95% CI: 37.8-56.8%) in July-September 2021, and, for T. gondii IgG, prevalence ranged from 15.5% (95% CI: 13.3-17.9%) in April-June 2020 to 36.2% (95% CI: 27.4-46.2%) July-September 2021. Conclusions: By early 2022, PLHIV in South Sudan had high rates of SARS-CoV-2 seropositivity. Surveillance of diseases of global health concern in PLHIV is crucial to estimate population-level exposure and inform public health responses.

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Purpose: Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. Approach: We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. Results: The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. Conclusion: This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

Polygenic Scoring for Detection of Ascending Thoracic Aortic Dilation.

BACKGROUND: Ascending thoracic aortic dilation is a complex heritable trait that involves modifiable and nonmodifiable risk factors. Polygenic scores (PGS) are increasingly used to assess risk for complex diseases. The degree to which a PGS can improve aortic diameter prediction in diverse populations is unknown. Presently, we tested whether adding a PGS to clinical prediction algorithms improves performance in a diverse biobank. METHODS: The analytic cohort comprised 6235 Penn Medicine Biobank participants with available echocardiography and clinical data linked to genome-wide genotype data. Linear regression models were used to integrate PGS weights derived from a genome-wide association study of thoracic aortic diameter performed in the UK Biobank and were compared with the performance of the previously published aorta optimized regression for thoracic aneurysm (AORTA) score. RESULTS: Cohort participants had a median age of 61 years (IQR, 53-70) and a mean ascending aortic diameter of 3.36 cm (SD, 0.49). Fifty-five percent were male, and 33% were genetically similar to African reference population. Compared with the AORTA score, which explained 30.6% (95% CI, 29.9%-31.4%) of the variance in aortic diameter, AORTA score+UK Biobank-derived PGS explained 33.1%, (95% CI, 32.3%-33.8%), the reweighted AORTA score explained 32.5% (95% CI, 31.8%-33.2%), and the reweighted AORTA score+UK Biobank-derived PGS explained 34.9% (95% CI, 34.2%-35.6%). When stratified by population, models including the UK Biobank-derived PGS consistently improved upon the clinical AORTA score among individuals genetically similar to European reference population but conferred minimal improvement among individuals genetically similar to African reference population. Comparable performance disparities were observed in models developed to discriminate cases/noncases of thoracic aortic dilation (≥4.0 cm). CONCLUSIONS: We demonstrated that inclusion of a UK Biobank-derived PGS to the AORTA score confers a clinically meaningful improvement in model performance only among individuals genetically similar to European reference population and may exacerbate existing health care disparities.

Interpersonal emotion regulation and depressive symptoms in parent-adolescent dyads: A daily-diary investigation.

Deficient parental extrinsic interpersonal emotion regulation (IER, how people regulate others' emotions) is a known risk factor for adolescent depression. Although IER and depression development are transactional, dyadic processes, previous work has almost exclusively focused on how parental IER is associated with adolescent depression. The association between parental IER and adolescent depression, and the associations between adolescent IER and adolescent and parental depression have received little attention. Moreover, most studies have focused on the regulation of negative but not positive affect. We address these gaps by examining associations between parent and adolescent IER and depressive symptoms using the actor-partner interdependence model framework. For 28 days, 112 parent-adolescent dyads (12-18-year-old adolescents) completed a dyadic daily diary, reporting their own depressive symptoms and IER strategies employed in response to dyad members' positive and negative affect. Our results, based on 5,442 data points, show that the use of positive- and negative-affect-worsening IER is associated with more depression in the regulator (be it parent or adolescent). Surprisingly, parents' use of more negative-affect-improving IER was associated with higher levels of their own and adolescents' depression. Finally, adolescents' use of positive-affect-improving IER was associated with their own decreased depression. Overall, parents (vs. adolescents) used more negative- and positive-affect-improving extrinsic IER, whereas adolescents used more positive-affect-worsening extrinsic IER. Our results highlight the importance of using dyadic designs in studying depression and IER, as well as the need to consider who is regulating, the valence of the affect regulated, and the type of strategy used. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design Optimization of A Triple-Layer Flat-Panel Detector for Three-Material Decomposition.

Spectral radiography and fluoroscopy with multi-layer flat-panel detectors (FPD) is being actively investigated in a range of clinical applications. For applications involving contrast administration, maximal contrast resolution is achieved when overlaying background anatomy is completely removed. This calls for three-material decomposition (soft tissue, bone, and contrast) enabled by measurements in three energy channels. We have previously demonstrated the feasibility of such decomposition using a triple-layer detector. While algorithmic solutions can be adopted to mitigate noise in the material basis images, in this work, we seek to fundamentally improve the conditioning of the problem through optimized system design. Design parameters include source voltage, the thickness of the top two CsI scintillators, and the thickness of two copper interstitial filters. The design objective is to minimize noise in the basis image containing contrast, chosen as gadolinium in this work to improve separation from bone. The optimized design was compared with other designs with unoptimized scintillator thickness and/or without interstitial filtration. Results show that CsI thickness optimization and interstitial filtration can significantly reduce noise in the gadolinium image by 35.7% and 42.7% respectively within a lung ROI, which in turn boosts detectability of small vessels. Gadolinium and bone signals are separated in all cases. Visualization of coronary vessels is enabled by the combining optimized system design and regularization. Results from this work demonstrate that three-material decomposition can be significantly improved with system design optimization. Optimized designs obtained from this work can inform imaging techniques selection and triple-layer detector fabrication for spectral radiography.

Synthesis of the bicyclic butenolide core of pallamolide A: a biomimetic approach.

Pallamolide A is a 7,8-seco-labdane terpenoid possessing a unique bicyclo[2.2.2]octane core and a spiro-butenolide moiety. A biomimetic synthesis of the bicyclic butenolide core over 10 steps is reported, featuring an unexpected autoxidation ring opening, and a vinylogous Mukaiyama aldol reaction which was spontaneously followed by an unusual intramolecular vinylogous aldol reaction to assemble the spiro-butenolide moiety and bicyclic core of pallamolide A.

End-to-end Deep Learning Restoration of GLCM Features from blurred and noisy images.

Radiomics involves the quantitative analysis of medical images to provide useful information for a range of clinical applications including disease diagnosis, treatment assessment, etc. However, the generalizability of radiomics model is often challenged by undesirable variability in radiomics feature values introduced by different scanners and imaging conditions. To address this issue, we developed a novel dual-domain deep learning algorithm to recover ground truth feature values given known blur and noise in the image. The network consists of two U-Nets connected by a differentiable GLCM estimator. The first U-Net restores the image, and the second restores the GLCM. We evaluated the performance of the network on lung CT image patches in terms of both closeness of recovered feature values to the ground truth and accuracy of classification between normal and COVID lungs. Performance was compared with an image restoration-only method and an analytical method developed in previous work. The proposed network outperforms both methods, achieving GLCM with the lowest mean-absolute-error from ground truth. Recovered GLCM feature values from the proposed method, on average, is within 2.19% error to the ground truth. Classification performance using recovered features from the network closely matches the "best case" performance achieved using ground truth feature values. The deep learning method has been shown to be a promising tool for radiomics standardization, paving the way for more reliable and repeatable radiomics models.

Outcomes of a Novel Single Branched Aortic Stent Graft for Treatment of Type B Aortic Dissection.

BACKGROUND: Intervention on Type B dissection frequently requires landing the proximal edge of the stent graft between the left common carotid artery and left subclavian artery (LSA). The Gore® TAG® Thoracic Branch Endoprosthesis (TBE) is a technology which allows LSA preservation with a single internal branch. METHODS: This study was a prospective non-randomized single-arm clinical trial of patients with type B aortic dissection that were treated with the single branched device. Patients with operative indications for acute, chronic or residual Type B dissections that originated distal to the origin of a left subclavian artery suitable for branch graft placement were eligible for the study. Native aortic and surgical graft proximal landing zones were eligible. RESULTS: Among the 132 patients, there were 25 (18.9%) acute type B dissections, 79 (59.8%) of chronic type B dissections and 28 (21.1%) of residual dissections after previous open Type A repair. Percutaneous access was used in 105 (79.5%) patients. Overall, 30-day mortality occurred in 6 patients (4.5%). The overall 30 day stroke rate was 2/132(1.5%) and the one-year freedom from stroke was 96.8%. Device Technical Success and Procedural Success was achieved in 129/132(97.7%) and 110/132(83.3%) of subjects, respectively and there was one instance of loss of side branch patency. There was no persistent antegrade false lumen flow observed. CONCLUSIONS: In this study of a novel branched endograft device to preserve the LSA in patients with type B dissection undergoing TEVAR, we demonstrate acceptable safety and efficacy outcomes at one year.

Assessment of laparoscopic surgery practice, knowledge, and training goals in Liberia: A mixed-methods study among Liberian surgeons.

BACKGROUND: Laparoscopy training remains inaccessible in many low- and middle-income countries, including Liberia. We assessed the availability of laparoscopy and feasibility of implementing a laparoscopic program among Liberian surgeons and trainees. METHODS: This mixed-methods study utilized a 32-item survey and semi-structured interviews on laparoscopic experience, knowledge, desires, barriers, patient perceptions, and training opportunities among surgeons and trainees at Liberia's two main teaching hospitals in March 2023. Data analysis utilized summed knowledge and desirability scores, descriptive statistics, and qualitative content analysis. RESULTS: 31 interns, residents, and consultants participated, comprising 60% of Liberia's surgeons. Laparoscopic training (32%) and experience (16%) was low, with exposure limited to those training outside Liberia (p = 0.001). While laparoscopy knowledge varied (29% low, 55% medium, 16% high), participants expressed high interest in training (100%) and willingness to pay (52%). Interviews revealed four themes: desires for training, patient acceptability, feasibility of technology-based training, and barriers including limited equipment and expert trainers. At the time of survey, the only minimally invasive surgeries ever performed in Liberia were two diagnostic laparoscopies. CONCLUSIONS: This is the first mixed-methods study assessing laparoscopy in Liberia. Our sample, though small, comprised approximately 60% of Liberian surgeons in both rural and urban hospitals. Findings demonstrated limited experience, variable knowledge, and high desires for training, showing feasibility for laparoscopy implementation in Liberia.

Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling.

Many spectral CT applications require accurate material decomposition. Existing material decomposition algorithms are often susceptible to significant noise magnification or, in the case of one-step model-based approaches, hampered by slow convergence rates and large computational requirements. In this work, we proposed a novel framework - spectral diffusion posterior sampling (spectral DPS) - for one-step reconstruction and multi-material decomposition, which combines sophisticated prior information captured by one-time unsupervised learning and an arbitrary analytic physical system model. Spectral DPS is built upon a general DPS framework for nonlinear inverse problems. Several strategies developed in previous work, including jumpstart sampling, Jacobian approximation, and multi-step likelihood updates are applied facilitate stable and accurate decompositions. The effectiveness of spectral DPS was evaluated on a simulated dual-layer and a kV-switching spectral system as well as on a physical cone-beam CT (CBCT) test bench. In simulation studies, spectral DPS improved PSNR by 27.49% to 71.93% over baseline DPS and by 26.53% to 57.30% over MBMD, depending on the the region of interest. In physical phantom study, spectral DPS achieved a <1% error in estimating the mean density in a homogeneous region. Compared with baseline DPS, spectral DPS effectively avoided generating false structures in the homogeneous phantom and reduced the variability around edges. Both simulation and physical phantom studies demonstrated the superior performance of spectral DPS for stable and accurate material decomposition.

Birth outcomes and survival by sex among newborns and children under 2 in the Birhan Cohort: a prospective cohort study in the Amhara Region of Ethiopia.

INTRODUCTION: Despite the progress in reducing child mortality, the rate remains high, particularly in sub-Saharan African countries. Limited data exist on child survival and other birth outcomes by sex. This study compared survival rates and birth outcomes by sex among neonates and children under 2 in Ethiopia. METHODS: Women who gave birth after 28 weeks of gestation and their newborns were included in the analysis. Survival probabilities were estimated for males and females in the neonatal period as well as the 2-year period following birth using Kaplan-Meier curves. HRs and 95% CIs were compared between males and females under 2. Descriptive statistics and χ2 tests were used to determine the sex-disaggregated variation in the birth outcomes of preterm birth, low birth weight (LBW), stillbirth, small for gestational age (SGA) and large for gestational age (LGA). RESULTS: The study included a total of 3904 women and child pairs. The neonatal mortality rate for males (3.4%, 95% CI 2.6% to 4.2%) was higher compared with females (1.7%, 95% CI 1.1% to 2.3%). The hazard of death during the first 28 days of life was approximately two times higher for males compared with females (HR 1.99, 95% CI 1.30 to 3.06) but was not significantly different after this period. While there was a non-significant difference between males and females in the proportion of preterm, LBW and LGA births, we found a significantly higher proportion of stillbirth (2.7% vs 1.3%, p=0.003) and SGA (20.5% vs 15.6%, p<0.001) for males compared with females. CONCLUSIONS: This study identified a significant sex difference in mortality and birth outcomes. We recommend focusing future research on the mechanisms of these sex differences in order to better design intervention programmes to reduce disparities and improve outcomes for neonates.

Outcomes following carotid revascularization in patients with prior ipsilateral carotid artery stenting in the Vascular Quality Initiative.

OBJECTIVE: The outcomes of carotid revascularization in patients with prior carotid artery stenting (CAS) remain understudied. Prior research has not reported the outcomes after transcarotid artery revascularization (TCAR) in patients with previous CAS. In this study, we compared the peri-operative outcomes of TCAR, transfemoral CAS (tfCAS) and carotid endarterectomy (CEA) in patients with prior ipsilateral CAS using the Vascular Quality Iniatitive. METHODS: Using Vascular Quality Initiative data from 2016 to 2023, we identified patients who underwent TCAR, tfCAS, or CEA after prior ipsilateral CAS. We included covariates such as age, race, sex, body mass index, comorbidities (hypertension, diabetes, prior coronary artery disease, prior coronary artery bypass grafting/percutaneous coronary intervention, congestive heart failure, renal dysfunction, smoking, chronic obstructive pulmonary disease, and anemia), symptom status, urgency, ipsilateral stenosis, and contralateral occlusion into a regression model to compute propensity scores for treatment assignment. We then used the propensity scores for inverse probability weighting and weighted logistic regression to compare in-hospital stroke, in-hospital death, stroke/death, postoperative myocardial infarction (MI), stroke/death/MI, 30-day mortality, and cranial nerve injury (CNI) after TCAR, tfCAS, and CEA. We also analyzed trends in the proportions of patients undergoing the three revascularization procedures over time using Cochrane-Armitage trend testing. RESULTS: We identified 2137 patients undergoing revascularization after prior ipsilateral carotid stenting: 668 TCAR patients (31%), 1128 tfCAS patients (53%), and 341 CEA patients (16%). In asymptomatic patients, TCAR was associated with a lower yet not statistically significant in-hospital stroke/death than tfCAS (TCAR vs tfCAS: 0.7% vs 2.0%; adjusted odds ratio [aOR], 0.33; 95% confidence interval [CI], 0.11-1.05; P = .06), and similar odds of stroke/death with CEA (TCAR vs CEA: 0.7% vs 0.9%; aOR, 0.80; 95% CI, 0.16-3.98; P = .8). Compared with CEA, TCAR was associated with lower odds of postoperative MI (0.1% vs 14%; aOR, 0.02; 95% CI, 0.00-0.10; P < .001), stroke/death/MI (0.8% vs 15%; aOR, 0.05; 95% CI, 0.01-0.25; P < .001), and CNI (0.1% vs 3.8%; aOR, 0.04; 95% CI, 0.00-0.30; P = .002) in this patient population. In symptomatic patients, TCAR had an unacceptably elevated in-hospital stroke/death rate of 5.1%, with lower rates of CNI than CEA. We also found an increasing trend in the proportion of patients undergoing TCAR following prior ipsilateral carotid stenting (2016 to 2023: 14% to 41%), with a relative decrease in proportions of tfCAS (61% to 45%) and CEA (25% to 14%) (P < .001). CONCLUSIONS: In asymptomatic patients with prior ipsilateral CAS, TCAR was associated with lower odds of in-hospital stroke/death compared with tfCAS, with comparable stroke/death but lower postoperative MI and CNI rates compared with CEA. In symptomatic patients, TCAR was associated with unacceptably higher in-hospital stroke/death rates. In line with the postprocedure outcomes, there has been a steady increase in the proportion of patients with prior ipsilateral stenting undergoing TCAR over time.

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.