scholarly journals Fast TWIST with iterative reconstruction improves diagnostic accuracy of AVM of the hand

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Veronika I. Huf ◽  
Claudia Fellner ◽  
Walter A. Wohlgemuth ◽  
Christian Stroszczynski ◽  
Michaela Schmidt ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Study Protocol ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Study Data ◽  
Sign Test ◽  
Data Set ◽  
Time Resolved ◽  
Magnetic Resonance Imaging Mri ◽  
Reliable Diagnostic Method

Abstract Very high temporal and spatial resolution is mandatory for the diagnosis of arteriovenous malformations (AVM) of the hand. Until now, magnetic resonance imaging (MRI) has not fulfilled both requirements simultaneously. This study presents how the combination of a very fast TWIST MRI (time-resolved angiography with interleaved stochastic trajectories) sequence and iterative reconstructions optimizes temporal as well as spatial resolution. 11 patients were examined at a 3-T MRI scanner with two different TWIST protocols: the standard and the study protocol, acquiring a data set every 5.57 s and 1.44 s respectively. The study data was retrospectively iteratively reconstructed with different regularization factors (0.001, 0.002, 0.004, 0.008). Results were compared using the sign-test. P-values < 0.05 were regarded statistically significant. With a low amount of contrast medium, the temporal resolution of the study protocol enabled the differentiation of arteries from veins in all patients whereas the signal-to-noise ratio (SNR) deteriorated. Depending on the regularization factors, SNR, delineation of arterial feeders and non-involved hand and interdigital arteries, as well as artefact levels varied. Overall, iterative reconstruction with regularization factor 0.004 achieved the best results, consequently showing the ability of MRI as a reliable diagnostic method in AVMs of the hand.

RFID-Based Real-Time Navigation for Interventional Magnetic Resonance Imaging: Development and Evaluation of a Novel Tracking System

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Felix Güttler ◽  
Andreas Heinrich ◽  
Peter Krauß ◽  
Jonathan Guntermann ◽  
Maximilian de Bucourt ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Radio Frequency Identification ◽  
Signal To Noise Ratio ◽  
Tracking System ◽  
Single Shot ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Steady State Precession

The purpose of this study was to evaluate the suitability of a novel radio-frequency identification (RFID)-based tracking system for intraoperative magnetic resonance imaging (MRI). A RFID tracking system was modified to fulfill MRI-compatibility and tested according to ASTM and NEMA. The influence of the RFID tracking system on MRI was analyzed in a phantom study using a half-Fourier acquisition single-shot turbospin echo (HASTE) and true fast imaging with steady-state precession sequence (TrueFISP) sequence. The RFID antenna was gradually moved closer to the isocenter of the MR scanner from 90 to 210 cm to investigate the influence of the distance. Furthermore, the RF was gradually changed between 865 and 869 MHz for a distance of 90 cm, 150 cm, and 210 cm to the isocenter of the magnet to investigate the influence of the frequency. The specific spatial resolution was measured with and without a permanent line of sight (LOS). After the modification of the reader, no significant change of the signal-to-noise ratio (SNR) could be observed with increasing distance of the RFID tracking system to the isocenter of the MR scanner. Also, different radio frequencies of the RFID tracking system did not influence the SNR of the MR-images significantly. The specific spatial resolution deviated on average by 8.97 ± 7.33 mm with LOS and 11.23 ± 12.03 mm without LOS from the reference system. The RFID tracking system had no relevant influence on the MR-image quality. RFID tracking solved the LOS problem. However, the spatial accuracy of the RFID tracking system has to be improved for medical usage.

scholarly journals LAB–QA2GO: A free, easy-to-use toolbox for the quality assessment of magnetic resonance imaging data

2019 ◽  
Author(s):  
Christoph Vogelbacher ◽  
Miriam H. A. Bopp ◽  
Verena Schuster ◽  
Peer Herholz ◽  
Andreas Jansen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Virtual Machine ◽  
Signal To Noise Ratio ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Data Set ◽  
Magnetic Resonance Imaging Mri ◽  
Movement Parameters ◽  
Set Up

AbstractImage characteristics of magnetic resonance imaging (MRI) data (e.g. signal-to-noise ratio, SNR) may change over the course of a study. To monitor these changes a quality assurance (QA) protocol is necessary. QA can be realized both by performing regular phantom measurements and by controlling the human MRI datasets (e.g. noise detection in structural or movement parameters in functional datasets). Several QA tools for the assessment of MRI data quality have been developed. Many of them are freely available. This allows in principle the flexible set-up of a QA protocol specifically adapted to the aims of one’s own study.However, setup and maintenance of these tools bind time, in particular since the installation and operation often require a fair amount of technical knowledge. In this article we present a light-weighted virtual machine, named LAB-QA2GO, which provides scripts for fully automated QA analyses of phantom and human datasets. This virtual machine is ready for analysis by starting it the first time. With minimal configuration in the guided web-interface the first analysis can start within 10 minutes, while adapting to local phantoms and needs is easily possible. The usability and scope of LAB–QA2GO is illustrated using a data set from the QA protocol of our lab. With LAB–QA2GO we hope to provide an easy-to-use toolbox that is able to calculate QA statistics without high effort.

scholarly journals Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minna Bührer ◽  
Hong Xu ◽  
Allard A. Hendriksen ◽  
Felix N. Büchi ◽  
Jens Eller ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Computational Time ◽  
Short Exposure ◽  
Dynamic Processes ◽  
Dynamic Features ◽  
Signal To Noise ◽  
Time Resolved ◽  
X Ray ◽  
Noise Ratio

AbstractTime-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations.

Quantitative voxel-to-voxel comparison of TriBeam and DCT strontium titanate three-dimensional data sets

2015 ◽  
Vol 48 (4) ◽  
pp. 1034-1046 ◽  
Author(s):  
William C. Lenthe ◽  
McLean P. Echlin ◽  
Andreas Trenkle ◽  
Melanie Syha ◽  
Peter Gumbsch ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Strontium Titanate ◽  
Spatial Resolution ◽  
Reference Frames ◽  
Three Dimensional ◽  
Data Sets ◽  
Reconstruction Method ◽  
Data Set ◽  
Time Resolved ◽  
Tomographic Data

Recently, techniques for the acquisition of three-dimensional tomographic and four-dimensional time-resolved data sets have emerged, allowing for the analysis of mm3volumes of material with nm-scale resolution. The ability to merge multi-modal data sets acquiredviamultiple techniques for the quantitative analysis of structure, chemistry and phase information is still a significant challenge. Large three-dimensional data sets have been acquired by time-resolved diffraction contrast tomography (DCT) and a new TriBeam tomography technique with high spatial resolution to address grain growth in strontium titanate. A methodology for combining three-dimensional tomographic data has been developed. Algorithms for the alignment of orientation reference frames, unification of sampling grids and automated grain matching have been integrated, and the resulting merged data set permits the simultaneous analysis of all tomographic data on a voxel-by-voxel and grain-by-grain basis. Quantitative analysis of merged data sets collected using DCT and TriBeam tomography shows that the spatial resolution of the DCT technique is limited near grain boundaries and the sample edge, resolving grains down to 10 µm diameter for the reconstruction method used. While the TriBeam technique allows for higher-resolution analysis of boundary plane location, it is a destructive tomography approach and can only be employed at the conclusion of a four-dimensional experiment.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

A Time-Resolved Low-Angle Light Scattering Apparatus. Application to Phase Separation Problems in Polymer Systems

2001 ◽  
Vol 66 (6) ◽  
pp. 973-982 ◽  
Author(s):  
Čestmír Koňák ◽  
Jaroslav Holoubek ◽  
Petr Štěpánek
Keyword(s):  
Phase Separation ◽  
Light Scattering ◽  
Signal To Noise Ratio ◽  
Scattered Light ◽  
Ccd Camera ◽  
Time Resolved ◽  
Polymer Systems ◽  
Software Analysis ◽  
Phase Separation Kinetics ◽  
Small Angle Light Scattering

A time-resolved small-angle light scattering apparatus equipped with azimuthal integration by means of a conical lens or software analysis of scattering patterns detected with a CCD camera was developed. Averaging allows a significant reduction of the signal-to-noise ratio of scattered light and makes this technique suitable for investigation of phase separation kinetics. Examples of applications to time evolution of phase separation in concentrated statistical copolymer solutions and dissolution of phase-separated domains in polymer blends are given.

Parent and Grandparent Relationships in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Emerging Adulthood ◽  
Attachment Theory ◽  
Emerging Adults ◽  
Family Relationships ◽  
Longitudinal Research ◽  
Study Data ◽  
Family Development ◽  
Data Set ◽  
Theory Framework

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

scholarly journals Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation

2021 ◽  
pp. 135245852098863
Author(s):  
Frank Dahlke ◽  
Douglas L Arnold ◽  
Piet Aarden ◽  
Habib Ganjgahi ◽  
Dieter A Häring ◽  
...  
Keyword(s):  
Disease Activity ◽  
Brain Volume ◽  
Young Patients ◽  
Clinical Trial Data ◽  
Study Data ◽  
Phase Iii ◽  
Brain Images ◽  
Data Set ◽  
Brain Volume Loss ◽  
Phase Iii Study

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

Association of Demographic and Disability Characteristics With Total PROMISE Expenditure: Wisconsin PROMISE Findings—Brief Report

2021 ◽  
pp. 003435522098079
Author(s):  
Emre Umucu ◽  
Beatrice Lee ◽  
Veronica Estala-Gutierrez ◽  
Timothy Tansey
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Treatment Group ◽  
Exploratory Study ◽  
Health Care Expenditure ◽  
Study Data ◽  
Data Set ◽  
Youth With Disabilities ◽  
Total Health Care Expenditure

The purpose of this exploratory study was to examine whether demographic and disability variables predict total health care expenditure of Wisconsin PROMISE. The findings are intended to assist in promoting cost-effectiveness for future similar initiates. This study data were extracted from Wisconsin PROMISE data set. This study had a total of 1,443 youth with disabilities ( Mage = 14.89). The majority of participants were male (69%). Our results indicated that some demographic and disability–related characteristics are associated with total health care expenditure in control with VR case during PROMISE, control without VR case during PROMISE, and treatment group. Overall, findings of the current study suggest demographic and disability variables do assist in predicting total health care expenditure of Wisconsin PROMISE.

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