An investigation of the visual sampling behaviour of human observers

Visual sampling behavior of human observers for aerospace vehicle design application

Beta-blockers and glioma: a systematic review of preclinical studies and clinical results

Given the median survival of 15 months after diagnosis, novel treatment strategies are needed for glioblastoma. Beta-blockers have been demonstrated to inhibit angiogenesis and tumor cell proliferation in various cancer types. The aim of this study was to systematically review the evidence on the effect of beta-blockers on glioma growth. A systematic literature search was performed in the PubMed, Embase, Google Scholar, Web of Science, and Cochrane Central to identify all relevant studies. Preclinical studies concerning the pharmacodynamic effects of beta-blockers on glioma growth and proliferation were included, as well as clinical studies that studied the effect of beta-blockers on patient outcomes according to PRISMA guidelines. Among the 980 citations, 10 preclinical studies and 1 clinical study were included after title/abstract and full-text screening. The following potential mechanisms were identified: reduction of glioma cell proliferation (n = 9), decrease of glioma cell migration (n = 2), increase of drug sensitivity (n = 1), induction of glioma cell death (n = 1). Beta-blockers affect glioma proliferation by inducing a brief reduction of cAMP and a temporary cell cycle arrest in vitro. Contrasting results were observed concerning glioma cell migration. The identified clinical study did not find an association between beta-blockers and survival in glioma patients. Although preclinical studies provide scarce evidence for the use of beta-blockers in glioma, they identified potential pathways for targeting glioma. Future studies are needed to clarify the effect of beta-blockers on clinical endpoints including survival outcomes in glioma patients to scrutinize the value of beta-blockers in glioma care

Epiglottis reshaping using CO2 laser: A minimally invasive technique and its potent applications

Laryngomalacia (LRM), is the most common laryngeal abnormality of the newborn, caused by a long curled epiglottis, which prolapses posteriorly. Epiglottis prolapse during inspiration (acquired laryngomalacia) is an unusual cause of airway obstruction and a rare cause of obstructive sleep apnea syndrome (OSAS)

Hybrid PET-MR list-mode kernelized expectation maximization reconstruction for quantitative PET images of the carotid arteries

Ordered subsets expectation maximization (OSEM) has been widely used in PET imaging. Although Bayesian algorithms have been shown to perform better, they are still not used in the clinical practice due to the difficulty of choosing appropriate and robust regularization parameters. The recently introduced kernelized expectation maximization (KEM) has shown some promise to work successfully for different applications. Therefore, we propose a list mode hybrid KEM (LM-HKEM) for static reconstructions, which we implemented in the open source Software for Tomographic Image Reconstruction (STIR) library. The proposed algorithm uses both MR and PET update images to create a feature vector for each voxel in the image, which contains the information about the local neighborhood. So as not to over-smooth the reconstructed images a 3×3×3 voxels kernel was used. Three real datasets were acquired with the Siemens mMR: a phantom to validate the algorithm and two patient carotid artery studies to show the possible applications of the method. The reconstructed images are assessed and compared for different algorithms: OSEM, OSEM with median root prior (MRP), KEM and LM-HKEM. The results show better quantification performance for the phantom low count images with around 4% bias compared to 7% for KEM and over 11% for OSEM and OSEM with (MRP). Our results show that the proposed technique can be used to improve quantification at low- count condition and it shows promising performance in terms of stability as for different subsets, with comparable number of events, we used the same parameters values. Emphasis is given on the reconstruction of the carotid artery and the characterization of atherosclerosis

Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation

Mindfulness-based stress reduction for people with multiple sclerosis ? a feasibility randomised controlled trial

Background: Multiple sclerosis (MS) is a stressful condition. Mental health comorbidity is common. Stress can increase the risk of depression, reduce quality of life (QOL), and possibly exacerbate disease activity in MS. Mindfulness-Based Stress Reduction (MBSR) may help, but has been little studied in MS, particularly among more disabled individuals. Methods: The objective of this study was to test the feasibility and likely effectiveness of a standard MBSR course for people with MS. Participant eligibility included: age > 18, any type of MS, an Expanded Disability Status Scale (EDSS) </= 7.0. Participants received either MBSR or wait-list control. Outcome measures were collected at baseline, post-intervention, and three-months later. Primary outcomes were perceived stress and QOL. Secondary outcomes were common MS symptoms, mindfulness, and self-compassion. Results: Fifty participants were recruited and randomised (25 per group). Trial retention and outcome measure completion rates were 90% at post-intervention, and 88% at 3 months. Sixty percent of participants completed the course. Immediately post-MBSR, perceived stress improved with a large effect size (ES 0.93; p < 0.01), compared to very small beneficial effects on QOL (ES 0.17; p = 0.48). Depression (ES 1.35; p < 0.05), positive affect (ES 0.87; p = 0.13), anxiety (ES 0.85; p = 0.05), and self-compassion (ES 0.80; p < 0.01) also improved with large effect sizes. At three-months post-MBSR (study endpoint) improvements in perceived stress were diminished to a small effect size (ES 0.26; p = 0.39), were negligible for QOL (ES 0.08; p = 0.71), but were large for mindfulness (ES 1.13; p < 0.001), positive affect (ES 0.90; p = 0.54), self-compassion (ES 0.83; p < 0.05), anxiety (ES 0.82; p = 0.15), and prospective memory (ES 0.81; p < 0.05). Conclusions: Recruitment, retention, and data collection demonstrate that a RCT of MBSR is feasible for people with MS. Trends towards improved outcomes suggest that a larger definitive RCT may be warranted. However, optimisation changes may be required to render more stable the beneficial treatment effects on stress and depression. Trial registration: ClinicalTrials.gov Identifier NCT02136485; trial registered 1st May 2014

Hybrid PET- and MR-driven attenuation correction for enhanced ¹⁸F-NaF and ¹⁸F-FDG quantification in cardiovascular PET/MR imaging

Background: The standard MR Dixon-based attenuation correction (AC) method in positron emission tomography/magnetic resonance (PET/MR) imaging segments only the air, lung, fat and soft-tissues (4-class), thus neglecting the highly attenuating bone tissues and affecting quantification in bones and adjacent vessels. We sought to address this limitation by utilizing the distinctively high bone uptake rate constant Ki expected from ¹⁸F-Sodium Fluoride (¹⁸F-NaF) to segment bones from PET data and support 5-class hybrid PET/MR-driven AC for ¹⁸F-NaF and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET/MR cardiovascular imaging. Methods: We introduce 5-class Ki/MR-AC for (i) ¹⁸F-NaF studies where the bones are segmented from Patlak Ki images and added as the 5th tissue class to the MR Dixon 4-class AC map. Furthermore, we propose two alternative dual-tracer protocols to permit 5-class Ki/MR-AC for (ii) ¹⁸F-FDG-only data, with a streamlined simultaneous administration of ¹⁸F-FDG and ¹⁸F-NaF at 4:1 ratio (R4:1), or (iii) for ¹⁸F-FDG-only or both ¹⁸F-FDG and ¹⁸F-NaF dual-tracer data, by administering ¹⁸F-NaF 90 minutes after an equal ¹⁸F-FDG dosage (R1:1). The Ki-driven bone segmentation was validated against computed tomography (CT)-based segmentation in rabbits, followed by PET/MR validation on 108 vertebral bone and carotid wall regions in 16 human volunteers with and without prior indication of carotid atherosclerosis disease (CAD). Results: In rabbits, we observed similar (< 1.2% mean difference) vertebral bone ¹⁸F-NaF SUVmean scores when applying 5-class AC with Ki-segmented bone (5-class Ki/CT-AC) vs CT-segmented bone (5-class CT-AC) tissue. Considering the PET data corrected with continuous CT-AC maps as gold-standard, the percentage SUVmean bias was reduced by 17.6% (¹⁸F-NaF) and 15.4% (R4:1) with 5-class Ki/CT-AC vs 4-class CT-AC. In humans without prior CAD indication, we reported 17.7% and 20% higher ¹⁸F-NaF target-to-background ratio (TBR) at carotid bifurcations wall and vertebral bones, respectively, with 5- vs 4-class AC. In the R4:1 human cohort, the mean ¹⁸F-FDG:¹⁸F-NaF TBR increased by 12.2% at carotid bifurcations wall and 19.9% at vertebral bones. For the R1:1 cohort of subjects without CAD indication, mean TBR increased by 15.3% (¹⁸F-FDG) and 15.5% (¹⁸F-NaF) at carotid bifurcations and 21.6% (¹⁸F-FDG) and 22.5% (¹⁸F-NaF) at vertebral bones. Similar TBR enhancements were observed when applying the proposed AC method to human subjects with prior CAD indication. Conclusions: Ki-driven bone segmentation and 5-class hybrid PET/MR-driven AC is feasible and can significantly enhance ¹⁸F-NaF and ¹⁸F-FDG contrast and quantification in bone tissues and carotid walls