Citations

Background: Chronic kidney disease (CKD), a growing public health issue in the elderly, is associated with increased risk of cognitive impairment. Objective: To investigate the mechanisms through which CKD impacts brain health using longitudinal imaging. Methods: We identified 97 participants (74 CKD and 23 non-CKD) from the BRINK (BRain IN Kidney Disease), a longitudinal study of CKD with two MRI scans (baseline and 3-year follow-up). We measured the associations between baseline and change in kidney disease biomarkers of estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (UACR), considered a measure of microvascular inflammation, and imaging outcomes of cortical thickness and ventricular volume from structural MRI, white matter hyperintensities (WMH) volume from FLAIR images, and fractional anisotropy of the corpus callosum (FACC). Results: There were white matter-specific changes as observed by increased WMH volume and decreased FACC in CKD participants, as well as ventricular volume increase in both CKD and non-CKD groups reflective of aging-related changes. Decline in eGFR was associated with decrease in the FACC, suggesting that subtle early white matter changes due to kidney disease can be captured using DTI. An increase in UACR was associated with increase in ventricular volume. Conclusion: Our results support the role of eGFR as a measure of kidney microvascular disease which is associated with concurrent white matter damage in CKD. Future work is needed to investigate the possible link between endothelial microvascular inflammation (as measured by an increased UACR) and ventricular volume increase.

AbstractObjectiveIn X‐raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisition speed. However, on a typical single‐source helical CT (SSCT) system, the helical pitch p typically cannot exceed 1.5; otherwise, reconstruction artifacts will result from data insufficiency. The purpose of this work is to develop a deep convolutional neural network (CNN) to correct for artifacts caused by an ultra‐fast pitch, which can enable faster acquisition speed than what is currently achievable.MethodsA customized CNN (denoted as ultra‐fast‐pitch network (UFP‐net)) was developed to restore the underlying anatomical structure from the artifact‐corrupted post‐reconstruction data acquired from SSCT with ultra‐fast pitch (i.e., p ≥ 2). UFP‐net employed residual learning to capture the features of image artifacts. UFP‐net further deployed in‐house‐customized functional blocks with spatial‐domain local operators and frequency‐domain non‐local operators, to explore multi‐scale feature representation. Images of contrast‐enhanced patient exams (n = 83) with routine pitch setting (i.e., p < 1) were retrospectively collected, which were used as training and testing datasets. This patient cohort involved CT exams over different scan ranges of anatomy (chest, abdomen, and pelvis) and CT systems (Siemens Definition, Definition Flash, Definition AS+, Siemens Healthcare, Inc.), and the corresponding base CT scanning protocols used consistent settings of major scan parameters (e.g., collimation and pitch). Forward projection of the original images was calculated to synthesize helical CT scans with one regular pitch setting (p = 1) and two ultra‐fast‐pitch setting (p = 2 and 3). All patient images were reconstructed using the standard filtered‐back‐projection (FBP) algorithm. A customized multi‐stage training scheme was developed to incrementally optimize the parameters of UFP‐net, using ultra‐fast‐pitch images as network inputs and regular pitch images as labels. Visual inspection was conducted to evaluate image quality. Structural similarity index (SSIM) and relative root‐mean‐square error (rRMSE) were used as quantitative quality metrics.ResultsThe UFP‐net dramatically improved image quality over standard FBP at both ultra‐fast‐pitch settings. At p = 2, UFP‐net yielded higher mean SSIM (> 0.98) with lower mean rRMSE (< 2.9%), compared to FBP (mean SSIM < 0.93; mean rRMSE > 9.1%). Quantitative metrics at p = 3: UFP‐net—mean SSIM [0.86, 0.94] and mean rRMSE [5.0%, 8.2%]; FBP—mean SSIM [0.36, 0.61] and mean rRMSE [36.0%, 58.6%].ConclusionThe proposed UFP‐net has the potential to enable ultra‐fast data acquisition in clinical CT without sacrificing image quality. This method has demonstrated reasonable generalizability over different body parts when the corresponding CT exams involved consistent base scan parameters.

Abstract Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age &lt;50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.

Objectives: To study the incidence, risk factors, and outcomes of conduction and rhythm disorders in a population-based cohort of patients with systemic sclerosis versus nonsystemic sclerosis comparators. Methods: An incident cohort of patients with systemic sclerosis (1980–2016) from Olmsted County, MN, was compared to age- and sex-matched nonsystemic sclerosis subjects (1:2). Electrocardiograms, Holter electrocardiograms, and a need for cardiac interventions were reviewed to determine the occurrence of any conduction or rhythm abnormalities. Results: Seventy-eight incident systemic sclerosis cases and 156 comparators were identified (mean age 56 years, 91% female). The prevalence of any conduction disorder before systemic sclerosis diagnosis compared to nonsystemic sclerosis subjects was 15% versus 7% ( p = 0.06), and any rhythm disorder was 18% versus 13% ( p = 0.33). During a median follow-up of 10.5 years in patients with systemic sclerosis and 13.0 years in nonsystemic sclerosis comparators, conduction disorders developed in 25 patients with systemic sclerosis with cumulative incidence of 20.5% (95% confidence interval: 12.4%–34.1%) versus 28 nonsystemic sclerosis patients with cumulative incidence of 10.4% (95% confidence interval: 6.2%–17.4%) (hazard ratio: 2.57; 95% confidence interval: 1.48–4.45), while rhythm disorders developed in 27 patients with systemic sclerosis with cumulative incidence of 27.3% (95% confidence interval: 17.9%–41.6%) versus 43 nonsystemic sclerosis patients with cumulative incidence of 18.0% (95% confidence interval: 12.3%–26.4%) (hazard ratio: 1.62; 95% confidence interval: 1.00–2.64). Age, pulmonary hypertension, and smoking were identified as risk factors. Conclusion: Patients with systemic sclerosis have an increased risk of conduction and rhythm disorders both at disease onset and over time, compared to nonsystemic sclerosis patients. These findings warrant increased vigilance and screening for electrocardiogram abnormalities in systemic sclerosis patients with pulmonary hypertension.

Background Due to their easy accessibility, CT scans have been increasingly used for investigation of gastrointestinal (GI) bleeding. Purpose To estimate the performance of a dual-phase, dual-energy (DE) GI bleed CT protocol in patients with overt GI bleeding in clinical practice and examine the added value of portal phase and DE images. Materials and Methods Consecutive patients with GI bleeding underwent a two-phase DE GI bleed CT protocol. Two gastroenterologists established the reference standard. Performance was estimated using clinical CT reports. Three GI radiologists rated confidence in GI bleeding in a subset of 62 examinations, evaluating first mixed kV arterial images, then after examining additional portal venous phase images, and finally after additional DE images (virtual non-contrast and virtual monoenergetic 50 keV images). Results 52 of 176 patients (29.5%) had GI bleeding by the reference standard. The overall sensitivity, specificity, and positive and negative predictive values of the CT GI bleed protocol for detecting GI bleeding were 65.4%, 89.5%, 72.3%, and 86.0%, respectively. In patients with GI bleeding, diagnostic confidence of readers increased after adding portal phase images to arterial phase images ( p = 0.002), without additional benefit from dual energy images. In patients without GI bleeding, confidence in luminal extravasation appropriately decreased after adding portal phase, and subsequently DE images ( p = 0.006, p = 0.018). Conclusion A two-phase DE GI bleed CT protocol had high specificity and negative predictive value in clinical practice. Portal venous phase images improved diagnostic confidence in comparison to arterial phase images alone. Dual-energy images further improved radiologist confidence in the absence of bleeding.

Abstract Background : Non‐motor symptoms are well established phenotypic components of adult‐onset idiopathic, isolated, focal cervical dystonia (AOIFCD). However, improved understanding of their clinical heterogeneity is needed to better target therapeutic intervention. Here, we examine non‐motor phenotypic features to identify possible AOIFCD subgroups. Methods : Participants diagnosed with AOIFCD were recruited via specialist neurology clinics (dystonia wales: n = 114, dystonia coalition: n = 183). Non‐motor assessment included psychiatric symptoms, pain, sleep disturbance, and quality of life, assessed using self‐completed questionnaires or face‐to‐face assessment. Both cohorts were analyzed independently using Cluster, and Bayesian multiple mixed model phenotype analyses to investigate the relationship between non‐motor symptoms and determine evidence of phenotypic subgroups. Results : Independent cluster analysis of the two cohorts suggests two predominant phenotypic subgroups, one consisting of approximately a third of participants in both cohorts, experiencing increased levels of depression, anxiety, sleep impairment, and pain catastrophizing, as well as, decreased quality of life. The Bayesian approach reinforced this with the primary axis, which explained the majority of the variance, in each cohort being associated with psychiatric symptomology, and also sleep impairment and pain catastrophizing in the Dystonia Wales cohort. Conclusions : Non‐motor symptoms accompanying AOIFCD parse into two predominant phenotypic sub‐groups, with differences in psychiatric symptoms, pain catastrophizing, sleep quality, and quality of life. Improved understanding of these symptom groups will enable better targeted pathophysiological investigation and future therapeutic intervention.

Abstract We report a comprehensive evaluation of a full field-of-view (FOV) photon-counting detector (PCD) computed tomography (CT) system using phantoms, and qualitatively assess image quality in patient examples. A whole-body PCD-CT system with 50 cm FOV, 5.76 cm z-detector coverage and two acquisition modes (standard: 144 × 0.4 mm collimation and ultra-high resolution (UHR): 120 × 0.2 mm collimation) was used in this study. Phantoms were scanned to assess image uniformity, CT number accuracy, noise power spectrum, spatial resolution, material decomposition and virtual monoenergetic imaging (VMI) performance. Four patients were scanned on the PCD-CT system with matched or lower radiation dose than their prior clinical CT scans performed using energy-integrating detector (EID) CT, and the potential clinical impact of PCD-CT was qualitatively evaluated. Phantom results showed water CT numbers within ±5 HU, and image uniformity measured between peripheral and central regions-of-interests to be within ±5 HU. For the UHR mode using a dedicated sharp kernel, the cut-off spatial frequency was 40 line-pairs cm −1 , which corresponds to a 125 μ m limiting in-plane spatial resolution. The full-width-at-half-maximum for the section sensitivity profile was 0.33 mm for the smallest slice thickness (0.2 mm) using the UHR mode. Material decomposition in a multi-energy CT phantom showed accurate material classification, with a root-mean-squared-error of 0.3 mg cc −1 for iodine concentrations (2–15 mg cc −1 ) and 14.2 mg cc −1 for hydroxyapatite concentrations (200 and 400 mg cc −1 ). The average percent error for CT numbers corresponding to the iodine concentrations in VMI (40–70 keV) was 2.75%. Patient PCD-CT images demonstrated better delineation of anatomy for chest and temporal bone exams performed with the UHR mode, which allowed the use of very sharp kernels not possible with EID-CT. VMI and virtual non-contrast images generated from a patient head CT angiography exam using the standard acquisition mode demonstrated the multi-energy capability of the PCD-CT system.