Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.     

500 Cases of High-intensity Focused Ultrasound (HIFU) Ablated Uterine Fibroids and Adenomyosis

ObjectiveClinical outcomes of 500 high-intensity focused ultrasound (HIFU)-treated uterine fibroids and adenomyosis are analyzed and presented.Materials and methodsThis is a retrospective cross-sectional analysis from a single tertiary medical center. From April 2015 to October 2018, 546 cases were enrolled for the study. After excluding 46 patients with less than 3 months of follow-up period, there were 404 fibroids, 149 adenomyosis and 53 mixed conditions entered for analysis. The patients’ uterine fibroids and adenomyosis were treated by HIFU according to Chongqing Haifu protocol, with 12 cm diameter transducer of focal length 10–16  cm at 0.8 or 1.6 MHz T2-weight MRI imaging was rendered prior to and 3 month post treatment to assess lesion volume change using non-perfusion volume, which was the primary outcome. Secondary outcomes including quality of life, subjective satisfaction, adverse events and pregnancy rate were determined using self-reported questionnaires. The mean follow up period ranged from 3 to 38 months with an average of 21 months.ResultsThree months after HIFU-treated uterine fibroids and adenomyosis, the lesion size reduced 40.2% and 46.3%, respectively. Symptoms all improved with better quality of life for the fibroid group, while those with adenomyosis or combined diseases benefit the most from pain control. Serum CA125 decreased significantly for all studied groups, and LDH only showed improvement for fibroids group. Number of adverse events is comparable to Chongqing data (approximately 10.2%), with mostly mild and self-resolving conditions. No permanent sequelae or death was documented. Twelve pregnancies are reported in this cohort.ConclusionHIFU is safe and effective in treating uterine fibroids and adenomyosis. The results are reproducible if standardized treatment schedules are followed. It is a promising treatment alternative with the advantages of precision, non-invasiveness, rapid recovery and readiness for pregnancy.     

Loss of function,missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based data sets in quantifying the specific risk of individual variants for disease-related phenotypes.     

Sensory evoked potentials in patients with Rett syndrome through the lens of animal studies: Systematic review

ObjectiveSystematically review the abnormalities in event related potential (ERP) recorded in Rett Syndrome (RTT) patients and animals in search of translational biomarkers of deficits related to the particular neurophysiological processes of known genetic origin (MECP2 mutations).MethodsPubmed, ISI Web of Knowledge and BIORXIV were searched for the relevant articles according to PRISMA standards.ResultsERP components are generally delayed across all sensory modalities both in RTT patients and its animal model, while findings on ERPs amplitude strongly depend on stimulus properties and presentation rate. Studies on RTT animal models uncovered the abnormalities in the excitatory and inhibitory transmission as critical mechanisms underlying the ERPs changes, but showed that even similar ERP alterations in auditory and visual domains have a diverse neural basis. A range of novel approaches has been developed in animal studies bringing along the meaningful neurophysiological interpretation of ERP measures in RTT patients.ConclusionsWhile there is a clear evidence for sensory ERPs abnormalities in RTT, to further advance the field there is a need in a large-scale ERP studies with the functionally-relevant experimental paradigms.SignificanceThe review provides insights into domain-specific neural basis of the ERP abnormalities and promotes clinical application of the ERP measures as the non-invasive functional biomarkers of RTT pathophysiology.