Representative Slices Research Articles

Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in 20 multi-slice MRI datasets acquired with two different coils in 11 subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and PB-U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sørensen-Dice similarity coefficient [DSC] = 0.81) in the representative (central coronal) slices with a large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC = 0.78-0.88 and 0.9, respectively). The proposed deep learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.

An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training.

Image segmentation is critical to lots of medical applications. While deep learning (DL) methods continue to improve performance for many medical image segmentation tasks, data annotation is a big bottleneck to DL-based segmentation because (1) DL models tend to need a large amount of labeled data to train, and (2) it is highly time-consuming and label-intensive to voxel-wise label 3D medical images. Significantly reducing annotation effort while attaining good performance of DL segmentation models remains a major challenge. In our preliminary experiments, we observe that, using partially labeled datasets, there is indeed a large performance gap with respect to using fully annotated training datasets. In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation. We achieve this by (i) selecting representative slices in 3D images that minimize data redundancy and save annotation effort, and (ii) self-training with pseudo-labels automatically generated from the base-models trained using the selected annotated slices. Extensive experiments using two public datasets (the HVSMR 2016 Challenge dataset and mouse piriform cortex dataset) show that our framework yields competitive segmentation results comparing with state-of-the-art DL methods using less than ~ 20% of annotated data.

Apparent Diffusion Coefficient of Diffusion Weighted Imaging have Strong Correlation with the Malignancy Grading of Intracranial Tumor

Background and purpose: Accurate imaging diagnosis before surgery is fundamental to determine whether a tumor is benign or malignant. Diagnosis only from structural MRI is not specific. However, the diffusivity of the benign and malignant lesion is different and could be measured by ADC. The Apparent Diffusion Coefficient (ADC) values are calculated automatically by application software and displayed as a parametric map that reflects the degree of diffusion of water molecules through different tissues. The purpose of this study was to analyze the correlation between Diffusion-Weighted Magnetic Resonance Imaging (DWI) and Apparent Diffusion Coefficient (ADC) values with histopathology grading of brain tumors. Material and methods: Pathology reports of intracranial tumor material were correlated with diffusivity of the tumor, and the ADC values, performed within 2 years of post-operative histopathology study. A freehand closed ROI out-lined the whole visible tumor on the most representative slice of ADC maps defined as the slice with the maximum diameter of the solid neoplastic component. ADC values were taken from 2 averages of 2 ROIs. Spearman Correlation test was used to assess the correlation between ADC values with the malignancy grading of the tumor. Result: Low-grade malignant tumors show facilitated diffusion with high ADC values, and high-grade malignant tumors show restricted diffusion with low ADC values. The mean ADC value of Grade I tumor is 1.0799 ± 0.2356 x 10−3 mm2/sec and the mean ADC value of Grade IV tumor is 0.6798 ± 0.23729 x 10−3 mm2/sec. The Spearman correlation test between ADC value and histopathology result shown r = -0.536 and p=0.000 and the test result for ADC value and DWI shown r=-0.706 and p=0.000 (Sig. 0.05). Conclusion: There was strong correlation between DWI and ADC values with histopathology grading of intracranial tumors.

Correlation of Placental Magnetic Resonance Imaging With Histopathologic Diagnosis: Detection of Aberrations in Structure and Water Diffusivity.

Noninvasive methods to identify placental pathologic conditions are being sought in order to recognize these conditions at an earlier stage leading to improved clinical interventions and perinatal outcomes. The objective of this study was to examine fixed tissue slices of placenta by T2- and diffusion-weighted magnetic resonance imaging (MRI) and correlate the images with placental pathologic findings defined by routine gross and histologic examination. Four formalin-fixed placentas with significant placental pathology (maternal vascular malperfusion, chronic villitis of unknown etiology, and massive perivillous fibrin deposition) and 2 histologically normal placentas were evaluated by high-resolution MRI. Representative placental slices were selected (2 cm long and 10 mm wide) and rehydrated. Imaging was performed on a Bruker Avance 14.1 T microimager. Diffusion-weighted images were acquired from 16 slices using slice thickness 0.5 mm and in-plane resolution approximately 100 µm × 100 µm. T2 maps were obtained from the same slices. T2 relaxation time and apparent diffusion coefficient (ADC) were acquired from representative regions of interest and compared between normal and diseased placentas. In T2- and diffusion-weighted images, the placental microstructure differed subjectively between diseased and normal placentas. Furthermore, diseased placentas showed statistically significantly longer mean T2 relaxation times and generally higher mean ADC. Diffusion- and T2-weighted MRI can potentially be used to detect significant placental pathology by using T2 relaxation time and ADC as markers of altered placental microstructure.

Exchange traded fund risk management and resiliency

The rapid growth of exchange traded funds (ETFs) has raised questions about possible risks facing ETF investors. ETF is a sophisticated daily-dealing open-ended collective investment vehicle (CIV), combining properties of an investment fund and a capital markets instrument and requiring an advanced end-to-end risk management framework across the ETF market structure. This paper recommends ETF risk management best practices developed to meet fiduciary and regulatory responsibilities. The risks associated with ETFs and authorised participants (APs), which serve an important role in the ETF ecosystem, should be monitored and managed prudently and holistically throughout the product life cycle. Investment and liquidity risk arise in portfolio construction and parts of primary market creation and redemption process. Baskets are representative slices of the fund that can introduce unintended investment and liquidity risks if not managed appropriately. Liquidity risk also extends to the secondary market. Counterparty risk arises mostly through primary market activity and securities lending, but it can also be present in portfolios through usage of derivatives and other instruments with counterparty risk exposures. A robust and comprehensive ETF risk management framework should incorporate investment, liquidity, and counterparty risk monitoring with escalation, oversight and strong governance. These are the building blocks of a robust risk management framework, which should also protect ETF investors during periods of market stress. A number of events over the past several years have tested the resiliency of ETFs in volatile markets. The ability of ETF shares to trade on the secondary market without ever having to transact in the underlying security holdings provides a ‘release valve’ or ‘shock absorber’ for directional trading pressure.

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC). This study retrospectively collected 163 patients with pathologically proven small renal mass, including 118 RCC and 45 AMLwvf patients. Target region of interest (ROI) delineation, followed by texture feature extraction, was performed on a representative slice with the largest lesion area on each phase of the four-phase CT images. Fifteen concatenations of the four-phasic features were fed into 224 classification models (built with 8 classifiers and 28 feature selection methods), classification performances of the 3360 resultant discriminative models were compared, and the top-ranked features were analyzed. Image features extracted from the unenhanced phase (UP) CT image demonstrated dominant classification performances over features from other three phases. The two discriminative models "SVM + t_score" and "SVM + relief" achieved the highest classification AUC of 0.90. The 10 top-ranked features from UP included 1 shape feature, 5 first-order statistics features, and 4 texture features, where the shape feature and the first-order statistics features showed superior discriminative capabilities in differentiating RCC vs. AMLwvf through the t-SNE visualization. Image features extracted from UP are sufficient to generate accurate differentiation between AMLwvf and RCC using machine learning-based classification model. • Radiomics extracted from unenhanced CT are sufficient to accurately differentiate angiomyolipoma without visible fat and renal cell carcinoma using machine learning-based classification model. • The highest discriminative models achieved an AUC of 0.90 and were based on the analysis of unenhanced CT, alone or in association with images obtained at the nephrographic phase. • Features related to shape and to histogram analysis (first-order statistics) showed superior discrimination compared with gray-level distribution of the image (second-order statistics, commonly called texture features).

Comments on: A comparative study of time aggregation techniques in relation to power capacity-expansion modeling

Relevant optimization problems dealing with power generation and distribution require that the system be analyzed on an hour-to-hour scale. The resulting mathematical models are frequently computationally intractable due to the very high number of variables and constraints. A popular method for reducing the problem size is to aggregate the time domain by selecting appropriate representative time slices. This study proposes: (i) a survey of the main aggregation techniques developed for power capacity-expansion modeling, and (ii) new aggregation methods and a computational comparison of the various approaches.

A comparative study of time aggregation techniques in relation to power capacity expansion modeling

In this paper, we studied the aggregation techniques for power capacity expansion problems. Combining a growing demand for green energy with a hard constraint on demand satisfaction causes system flexibility to be a major challenge in designing a stable energy system. To determine both the need for flexibility and which technologies that could satisfy these needs at minimum cost, the system should be analyzed on an hour-by-hour scale for a long period of time. This often leads to computationally intractable problems. One way of getting more tractable models is to aggregate the time domain. Many different aggregation techniques have been developed, all with a common goal of selecting representative time slices to be used instead of the full time scale, gaining a problem size reduction in the number of variables and/or constraints. The art of aggregation is to balance the computational difficulty against the solution quality, making validation of the techniques crucial. We propose new aggregation techniques and compare these to each other and to a selection of aggregation techniques from the literature. We validate the aggregated problems against the non-aggregated problems and look into the sensitivity of the performance of the aggregation techniques to different data sets and to the selection of different element types. Our analysis shows that aggregation techniques can be used to achieve very good solutions in a short amount of time, and that simple aggregation techniques achieve good performance similar to that of techniques with higher complexity. Even though the aggregation techniques in this paper are applied to power capacity expansion models, the methodology can be used for other problems with similar time dependence, and we believe that results in agreement with the results seen here, would be achieved.

Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain

Climate change impacts on winter wheat yield and water-use were assessed with the AquaCrop model for different representative concentration pathway (RCP) scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) and time slices (2040s, 2060s, 2080s) using ensemble projections from 10 general circulation models (GCM) for a site in the North China Plain. To test the role of crop and soil management practices under climate change, simulations with deficit irrigation (DI) and mulching were also performed. DI was defined as a practice where soil water content was restored to field capacity after depleting 150% of readily available water (RAW). The chosen mulching practice had a fixed capacity to reduce evaporation by 60%. Simulated outputs were compared with the baseline simulations (baseline period: 1984–2015). The results indicate that yield and water use efficiency (WUE) of wheat will increase under all RCP scenarios and time slices. Analysis revealed that winter wheat in the NCP would benefit from both CO2 fertilization and warming. Warming will shorten the growing cycle but largely due to curbing of the overwintering period. Due to accelerated growth/development, flowering and maturity will be advanced compared to the baseline period. Warming will also ease the low-temperature stress on biomass production. The seasonal evapotranspiration will reduce mainly due to the shorter growing cycle and the CO2-induced reduction in transpiration. Increase in yield and WUE is expected even under DI, while mulching can further increase WUE. Projections using individual GCMs indicated that the variability in crop production would be higher for the high-end scenario (RCP 8.5) than for RCP 2.6. These findings imply that in general, wheat in the NCP is less vulnerable to climate change than in other parts of the world. In addition, the expected increase in the yield and WUE of wheat may positively affect current depletion rates of groundwater for irrigation; however, more work is needed to quantify this. In future, it will be possible to introduce short duration wheat and long duration maize (second crop in rotation) cultivars.

Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network

Most often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV2 ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897.

2D modelling: A Monte Carlo approach for assessing heterogeneous beta dose rates in luminescence and ESR dating: Paper ΙΙ, application to igneous rocks

Accurate evaluations of beta dose rates are crucial in luminescence and electron spin resonance (ESR) thermochronology. Most igneous rocks are heterogeneous and have complex mineralogical structures that render them unsuitable for the conventional methods of beta dose rate calculations based on infinite matrix assumptions. The recently developed software DosiVox-2D provides a Monte-Carlo approach for calculating beta dose rates in realistic heterogeneous geometries. In this paper, we present 2D simulations of uniform and layered igneous rocks. For the modelling, mineral distribution maps were obtained by QEM-EDS (quantitative evaluation of minerals using energy dispersive spectroscopy) and radionuclide concentrations in minerals were determined by laser ablation (LA) ICP-MS. The results show that the skewness of beta dose rate distributions in quartz reduces as the K concentration in a rock increases, and the estimation of the beta dose rate can be critically influenced by the U and Th concentrations and distributions. • QEMSCAN ® provides realistic 2D mineral mapping for heterogeneous rocks. • A single set of radionuclide concentrations cannot be representative for some U and Th enriched minerals. • Selections of representative slices, mapping resolution and size are essential to obtain reliable beta dose rate. • Beta dose rate calculation can be critically influenced by the U and Th concentrations and distributions. • DosiVox-2D enables to estimate beta dose rate in single-grain level.

18F-PSMA-1007 PET/CT at 60 and 120 minutes in patients with prostate cancer: biodistribution, tumour detection and activity kinetics.

PSMA-targeted PET in patients with prostate cancer (PCa) has a significant impact on treatment decisions. By far the most frequently used PSMA ligand is 68Ga-labelled PSMA-11. However, due to the availability of larger amounts of activity, 18F-labelled PSMA ligands are of major interest. The aim of the present study was to evaluate the biodistribution and performance of the novel 18F-labelled ligand PSMA-1007 at two different time points. This retrospective analysis included 40 consecutive patients (mean age 68.7 ± 8.1 years) referred for PSMA PET/CT. 18F-PSMA-1007 PET/CT was performed for localization of biochemical relapse, primary staging or therapy follow-up. Circular regions of interest were placed on representative slices of the liver, spleen, kidney, abdominal aortic blood pool, bone marrow (fourth lumbar vertebral body), urinary bladder and gluteus muscle at 60 and 120 min after injection. In malignant lesions the maximum standardized uptake (SUVmax) was measured within volumes of interest at both time points. All SUVs at 60 min were compared with those at 120 min after injection. The activity in the blood pool, urinary bladder and gluteus muscle was very low and decreased significantly over time (P < 0.001). Uptake in the liver, spleen and kidney showed a significant increase over time and uptake in the bone marrow remained stable. Overall, 135 PCa lesions were detected at 60 min and 136 lesions at 120 min after injection. The median SUVmax increased significantly (P < 0.001) from 10.98 to 15.51 between 60 and 120 min. PCa lesions show a significant increase in 18F-PSMA-1007 uptake at 120 min compared with 60 min after injection. In addition, accumulation of the tracer in the urinary bladder was very low leading to improved contrast of adjacent PCa lesions. Increasing accumulation in the liver may limit the sensitivity of the tracer in detecting liver metastases.

An experimental study of natural convection in a distribution transformer slice model

The aim of this work is to obtain experimental information on the thermal and fluid dynamics of a distribution transformer. The transformer of interest is of the oil natural – air natural type and our interest is centered in the natural circulation that is produced in the oil volume. An experimental device was built to simulate a representative slice of the transformer and a purpose built PIV setup was used to obtain the flow velocity field. The device overall dimensions and the refrigerating oil are the same as those of commercial transformers of 1000 KVA (Tubos Trans Electric S.A. 1000 KVA Standard Distribution Transformer) Natural circulation is produced by a heating element simulating the heat produced by copper losses in the transformer. A double glass window allows visual access for flow visualization and PIV capture. The slice comprises two fins and the air side is bounded by two isolating boards, forming three air channels. Two reference powers were tested for the experiment: The power corresponding to design operating conditions and a lower power to observe the sensibility to power variations. The result reported in this work shows features of the flow pattern that give important information on the sort of turbulent mixing to be expected. The transient evolution was measured and the thermal characteristic time of the system was estimated. It was found that the convection during the start-up transient is significantly stronger than during steady state operation.

Abstract PD2-10: Radiomic phenotypes of tumor heterogeneity from pre-operative DCE-MRI independently predict breast cancer recurrence after 10-year follow-up from primary invasive diagnosis

Abstract Purpose: To investigate whether pre-operative MRI phenotypes of tumor heterogeneity have independent prognostic value and determine their associations with recurrence-free-survival (RFS) using 10-year follow-up after primary invasive breast cancer diagnosis. Materials and Methods: The DCE-MRI images of 94 women diagnosed with primary invasive breast cancer, who had complete histopathologic and 10-year follow up data available, were chosen from a previously conducted multimodality clinical trial cohort (2002-2006). For each woman, the signal enhancement ratio (SER) map was calculated for the most representative slice of the primary lesion. Radiomic features (including first order histogram, run-length, structural, and co-occurrence matrix features) and morphologic measures (perimeter, area, ellipticity, and convexity) were extracted and summarized over four quadrants of the tumor. To identify intrinsic phenotypes of tumor heterogeneity, unsupervised hierarchical clustering was applied to the extracted feature vectors after z-score normalization, where cluster cutoffs were determined using Consensus Clustering and the SigClust method. The normalized feature vectors were further averaged to generate a composite heterogeneity score for each tumor. Differences across phenotypes by recurrence, established prognostic factors (ER, PR, HER2, Clinical Stage, Ki67%), and heterogeneity score were assessed using Chi-square and Kruskal- Wallis tests. Kaplan-Meier curves were used to display survival probabilities across phenotypes, adjusting for time-to-event data. Heterogeneity phenotype assignments were added to a baseline Cox proportional hazards model with established prognostic factors to predict RFS. The log-likelihood test was used to assess goodness-of-fit and model discriminative capacity was evaluated using the c-statistic. Results: Our sample included 14 recurrences(15%). Unsupervised clustering identified three intrinsic phenotypes that ranged from low to high heterogeneity (p&amp;lt;0.001). The most heterogeneous phenotype contained all recurrences (p&amp;lt;0.001). Clinical stage was also different among phenotypes (p=0.05). The augmented model incorporating heterogeneity phenotypes had higher discriminatory capacity (c-statistic=0.86), compared to the baseline model including only established histopathologic prognostic factors (c-statistic=0.79). Baseline ModelBaseline Model + MRI Tumor Heterogeneity Phenotype* p= 0.15, c= 0.79p&amp;lt;0.001, c=0.86ParameterHazard RatioHazard RatioEstrogen Receptor (ER)1.001.00Progesterone Receptor (PR)0.990.99HER2 Receptor (Her2)0.650.72Ki67 (%)1.000.99Clinical Stage2.551.94Heterogeneity phenotype p&amp;lt;0.001*Log-likelihood ratio between models p-value &amp;lt;0.001 Conclusion: Intrinsic phenotypes of tumor heterogeneity in pre-operative DCE-MRI of primary invasive breast cancer may provide additional prognostic information to established histopathologic factors. Independent validation is needed to determine phenotype reproducibility and their independent associations to RFS when also including additional molecular/histopathologic prognostic factors. Citation Format: Chitalia RD, Rowland J, McDonald E, Pantalone L, Cohen E, Gastounioti A, Thomas K, Batiste R, Feldman M, Schnall MD, Conant EF, Kontos D. Radiomic phenotypes of tumor heterogeneity from pre-operative DCE-MRI independently predict breast cancer recurrence after 10-year follow-up from primary invasive diagnosis [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD2-10.

Schisandrin B Ameliorates Myocardial Ischemia/Reperfusion Injury Through Attenuation of Endoplasmic Reticulum Stress-Induced Apoptosis.

Schisandrin B (Sch B), an active composition isolated from the fruit of Schisandra chinensis, has been proved to possess antiinflammatory, antioxidant and anti-endoplasmic reticulum (ER) stress effects in many rodent tissues. However, the exact mechanism of cardioprotective effect of Sch B still needs more study. Here, we detected the effects of Sch B on myocardial ischemia/reperfusion (I/R) injury rats. I/R injury model in this study was established by left anterior descending coronary artery ligation for 40min followed by 1h of reperfusion. Male healthy rats were randomly divided into five groups: the sham, I/R, Sch B (20mg/kg) + I/R, and Sch B (40mg/kg) + I/R, Sch B (80mg/kg) + I/R, with 10 rats in each group. We showed that Sch B treatment significantly protected against myocardial I/R injury, as demonstrated by the decrease in the percentage of infarct formation assessed by 2,3,5-triphenyl tetrazolium chloride (TTC) staining in representative heart tissue slices, comparing with the I/R control group. The levels of creatine kinase (CK), lactate dehydrogenase (LDH), malondialdehyde (MDA), and total superoxide dismutase (T-SOD) were tested. The ER stress-related proteins such as C/EBP homologous protein (CHOP), activating transcription factor 6 (ATF6), and (PKR)-like ER kinase (PERK) were further measured by western blot, and their messenger RNA levels were measured by real-time PCR. The apoptosis of heart tissue cells was also tested through the expressions of caspase-9, caspase-3, Bcl-2, and Bax proteins. Collectively, these results revealed that Sch B exerts protection role on myocardial I/R injury through decreasing oxidative reaction, suppressing ATF6 and PERK pathway, and attenuating ER stress-induced apoptosis.

Impact of advanced modeled iterative reconstruction on interreader agreement in coronary artery measurements

ObjectivesTo evaluate the influence of advanced modeled iterative reconstruction (ADMIRE) on coronary artery computed tomography angiography (cCTA) measurements in comparison to filtered back projection (FBP). Material and methodsPhantom scans and coronary CTA studies of 27 patients were acquired with a third generation dual-source CT scanner. Images were reconstructed using FBP and ADMIRE. Phantom measurements were used as reference standard. In patient studies, representative axial slices of each coronary artery segment without (n=308) and with coronary plaques (n=40) were assessed in identical positions for comparison of FBP and ADMIRE reconstructions. Image analyses included quality assessment, phantom and coronary artery measurements, plaque analysis, and interreader agreement of two independent and blinded readers. ResultsMean image noise was lower on ADMIRE reconstructions with 31.3±9.9 HU compared to 55.9±15.7 HU on FBP reconstructions (p<0.001). Measurement precision and interreader agreement of both observers were assessed satisfactorily on phantom images in comparison to the full width half maximum method. In patients, correlation of lumen diameters of both observers improved using ADMIRE with a Pearson’s r=0.987 (95% confidence interval [CI], 0.983–0.989; p<0.001) compared to FBP images with r=0.939 (95% CI, 0.924–0.951; p<0.001). Applying ADMIRE, agreement of both observers for lumen diameter measurements significantly increased (p<0.001). This was also observed for the degree of stenosis (p<0.001) with r=0.560 using FBP (95% CI, 0.301–0.742) and with r=0.818 using ADMIRE (95% CI, 0.680–0.900). Plaque density measurements correlated closely with a Pearson’s r of 0.951 in FBP (95% CI, 0.909–0.974) and 0.967 in ADMIRE (95% CI, 0.939–0.983). ConclusionsAdvanced modeled iterative reconstruction significantly improves coronary artery assessment in coronary CTA in comparison to FBP by improved image quality due to image noise removal. This renders improved interobserver agreement for coronary lumen diameter and degree of stenosis measurements without influencing mean plaque attenuation.

Improved workflow for quantifying left ventricular function via cardiorespiratory-resolved analysis of free-breathing MR real-time cines.

To evaluate the feasibility of a proposed cardiorespiratory-resolved analysis in left ventricular (LV) function quantification from real-time cines in a cohort of cardiac patients. Electrocardiograph (ECG)-free free-breathing real-time cine imaging based on the balanced steady-state free precession technique was performed on short-axis slices of 20 cardiac patients at 3T. K-means cluster segmentation was used to delineate the endocardial contours, from which the LV centroid and cavity area were determined. Respiratory and cardiac signals were respectively resolved from the filtered LV centroid displacement and time-varied LV cavity area to identify end-expiratory end-diastolic (ED) and end-systolic (ES) images. The obtained LV cavity areas and derived volumetric function indices, including ED volume (EDV), ES volume (ESV), stroke volume (SV), and ejection fraction (EF), were compared with those measured from manual analysis using two-tailed paired Student's t-tests, linear regression analyses, and Bland-Altman plots. Interobserver variability was calculated. The LV cavity area was strongly correlated between the proposed and conventional manual methods (r > 0.87) for three representative slices at the base, middle ventricle, and apex. The average differences between the two methods were 0.66 ± 3.22 mL for EDV, -0.02 ± 2.68 mL for ESV, 0.67 ± 3.73 mL for SV, and 0.17 ± 2.30% for EF. All paired measures exhibited strong correlations (r > 0.96) without significant differences (P = 0.38-0.98). Acceptable interobserver variability (0.19-3.55%) and strong correlations (r > 0.96) were shown for all measures between the two observers. The proposed method is feasible for efficient measurement of LV function from real-time cines. 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:905-914.

Sensitivity of the Penman-Monteith reference evapotranspiration to sunshine duration in the Upper Mekong River Basin

ABSTRACTSensitivity analysis is important in understanding the relationship between sunshine duration (SD) and reference evapotranspiration (ETref). This study was developed in the Lancang-Mekong River Basin using a non-dimensional relative sensitivity coefficient (SN) relating ETref to SD. The SN for January and July for each station were calculated. The results indicate that (a) the SD and SN change in similar ways, where January and July are selected as two representative time slices; (b) the spatial distributions of the long-term averaged SN for January and July are the reverse of each other; (c) the most abrupt changes in climate occur in the 1980s and the middle region is sensitive to climate change; and (d) periodicities of 2–4 and 14–16 years are detected in the basin generally. The significant increase of inter-decadal filter variance indicates changes in the long-term memory of the local climate system.EDITOR Z.W. KundzewiczASSOCIATE EDITOR not assigned

Painful tic convulsif: Case series and literature review

Abstract Background The coexistence of hemifacial spasm and trigeminal neuralgia, a clinical entity known as painful tic convulsive, was first described in 1910. It is an uncommon condition that is worthy of interest in neurosurgical practice, because of its common pathophysiology mechanism: Neuro-vascular compression in most of the cases. Objective To present 2 cases of painful tic convulsive that received treatment at our institution, and to give a brief review of the existing literature related to this. The benefits of micro-surgical decompression and the most common medical therapy used (botulin toxin) are also presented. Clinical cases Two cases of typical painful tic convulsive are described, showing representative slices of magnetic resonance imaging corresponding to the aetiology of each case, as well as a description of the surgical technique employed in our institution. The immediate relief of symptomatology, and the clinical condition at one-year follow-up in each case is described. A brief review of the literature on this condition is presented. Conclusion This very rare neurological entity represents less than 1% of rhizopathies and in a large proportion of cases it is caused by vascular compression, attributed to an aberrant dolichoectatic course of the vertebro-basilar complex. The standard modality of treatment is micro-vascular surgical decompression, which has shown greater effectiveness and control of symptoms in the long-term. However medical treatment, which includes percutaneous infiltration of botulinum toxin, has produced similar results at medium-term in the control of each individual clinical manifestation, but it must be considered as an alternative in the choice of treatment.

Cerebral blood flow laterality derived from arterial spin labeling as a biomarker for assessing the disease severity of parkinson's disease.

To evaluate cerebral blood flow (CBF) laterality derived from arterial spin labeling (ASL) in early-stage Parkinson's disease (PD) patients compared with those with advanced stages. Thirty-eight patients with PD (21 patients in early stages, 17 patients in advanced stages) were retrospectively studied. The CBF maps derived from 3T ASL data were co-registered to the corresponding 3DT1WI using SPM 12 software. Caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were manually traced on the representative axial slices of 3DT1WI. CBF of the CN, PT, GP, and TH was measured using corresponding pixels on the co-registered CBF maps. A laterality index (LI) was calculated as the ratio of the contralateral CBF to primary affected side CBF. Each LI was compared between early and advanced stages of PD using the Mann-Whitney U-test. The LIs were also compared between each stage of PD. In the CN, the LIs were significantly higher in early stages (mean LI ± SD, 95% confidence interval = 1.06 ± 0.14, 1.00-1.13) than in advanced stages (0.94 ± 0.14, 0.87-1.01; P < 0.05). We also observed a tendency toward decreased LIs with disease severity (1.10 ± 0.14, 0.99-1.21 for Hoehn and Yahr stage I; 1.04 ± 0.14, 0.92-1.12 for stage II; 0.96 ± 0.11, 0.89-1.10 for stage III; 0.93 ± 0.17, 0.81-1.05 for stage IV). The evaluation of CBF laterality pattern in the CN using ASL may be useful for assessing the disease severity of PD patients. 3 J. MAGN. RESON. IMAGING 2017;45:1821-1826.