scholarly journals Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1077
Author(s):  
Karl Ludger Radke ◽  
Lena Marie Wollschläger ◽  
Sven Nebelung ◽  
Daniel Benjamin Abrar ◽  
Christoph Schleich ◽  
...  
Keyword(s):  
Real Time ◽  
Imaging Modality ◽  
Dynamic Assessment ◽  
Ligament Injury ◽  
Carpal Bone ◽  
Post Processing ◽  
Similarity Coefficients ◽  
Wrist Movement ◽  
Scapholunate Ligament Injury ◽  
Static Assessment

While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland–Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities.

scholarly journals Towards on-the-fly data post-processing for real-time tomographic imaging at TOMCAT

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Federica Marone ◽  
Alain Studer ◽  
Heiner Billich ◽  
Leonardo Sala ◽  
Marco Stampanoni
Keyword(s):  
Real Time ◽  
Tomographic Imaging ◽  
Post Processing

scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

scholarly journals Sensitivity of GNSS tropospheric gradients to processing options

2019 ◽  
Vol 37 (3) ◽  
pp. 429-446 ◽  
Author(s):  
Michal Kačmařík ◽  
Jan Douša ◽  
Florian Zus ◽  
Pavel Václavovic ◽  
Kyriakos Balidakis ◽  
...  
Keyword(s):  
Real Time ◽  
Elevation Angle ◽  
Mapping Function ◽  
Global Navigation Satellite Systems ◽  
Gradient Estimates ◽  
Post Processing ◽  
Data Set ◽  
Mapping Functions ◽  
Gradient Mapping

Abstract. An analysis of processing settings impacts on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 Global Navigation Satellite Systems (GNSS) reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using precise point positioning (PPP) with the G-Nut/Tefnut software. The impacts of the gradient mapping function, elevation cut-off angle, GNSS constellation, observation elevation-dependent weighting and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather models (NWMs). Tropospheric gradients estimated in post-processing GNSS solutions using final products were in good agreement with NWM outputs. The quality of high-resolution gradients estimated in (near-)real-time PPP analysis still remains a challenging task due to the quality of the real-time orbit and clock corrections. Comparisons of GNSS and NWM gradients suggest the 3∘ elevation angle cut-off and GPS+GLONASS constellation for obtaining optimal gradient estimates provided precise models for antenna-phase centre offsets and variations, and tropospheric mapping functions are applied for low-elevation observations. Finally, systematic errors can affect the gradient components solely due to the use of different gradient mapping functions, and still depending on observation elevation-dependent weighting. A latitudinal tilting of the troposphere in a global scale causes a systematic difference of up to 0.3 mm in the north-gradient component, while large local gradients, usually pointing in a direction of increasing humidity, can cause differences of up to 1.0 mm (or even more in extreme cases) in any component depending on the actual direction of the gradient. Although the Bar-Sever gradient mapping function provided slightly better results in some aspects, it is not possible to give any strong recommendation on the gradient mapping function selection.

scholarly journals Design, Development, and Multi-Characterization of an Integrated Clinical Transrectal Ultrasound and Photoacoustic Device for Human Prostate Imaging

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 566 ◽  
Author(s):  
Sumit Agrawal ◽  
Kerrick Johnstonbaugh ◽  
Joseph Y. Clark ◽  
Jay D. Raman ◽  
Xueding Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Needle Biopsy ◽  
Imaging Modality ◽  
Experimental Studies ◽  
Transrectal Ultrasound ◽  
Human Prostate ◽  
Design Development ◽  
Imaging Device ◽  
Prostate Imaging ◽  
Clinically Significant

The standard diagnostic procedure for prostate cancer (PCa) is transrectal ultrasound (TRUS)-guided needle biopsy. However, due to the low sensitivity of TRUS to cancerous tissue in the prostate, small yet clinically significant tumors can be missed. Magnetic resonance imaging (MRI) with TRUS fusion biopsy has recently been introduced as a way to improve the identification of clinically significant PCa in men. However, the spatial errors in coregistering the preprocedural MRI with the real-time TRUS causes false negatives. A real-time and intraprocedural imaging modality that can sensitively detect PCa tumors and, more importantly, differentiate aggressive from nonaggressive tumors could largely improve the guidance of biopsy sampling to improve diagnostic accuracy and patient risk stratification. In this work, we seek to fill this long-standing gap in clinical diagnosis of PCa via the development of a dual-modality imaging device that integrates the emerging photoacoustic imaging (PAI) technique with the established TRUS for improved guidance of PCa needle biopsy. Unlike previously published studies on the integration of TRUS with PAI capabilities, this work introduces a novel approach for integrating a focused light delivery mechanism with a clinical-grade commercial TRUS probe, while assuring much-needed ease of operation in the transrectal space. We further present the clinical potential of our device by (i) performing rigorous characterization studies, (ii) examining the acoustic and optical safety parameters for human prostate imaging, and (iii) demonstrating the structural and functional imaging capabilities using deep-tissue-mimicking phantoms. Our TRUSPA experimental studies demonstrated a field-of-view in the range of 130 to 150 degrees and spatial resolutions in the range of 300 μm to 400 μm at a soft tissue imaging depth of 5 cm.

scholarly journals A New Asynchronous RTK Method to Mitigate Base Station Observation Outages

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3376 ◽  
Author(s):  
Yuan Du ◽  
Guanwen Huang ◽  
Qin Zhang ◽  
Yang Gao ◽  
Yuting Gao
Keyword(s):  
Real Time ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Observation Data ◽  
Post Processing ◽  
Positioning Accuracy ◽  
Real Time Processing ◽  
Phase Measurements ◽  
Satellite Systems ◽  
Precise Ephemeris

Real-time kinematic (RTK) positioning is a satellite navigation technique that is widely used to enhance the precision of position data obtained from global navigation satellite systems (GNSS). This technique can reduce or eliminate significant correlation errors via the enhancement of the base station observation data. However, observations received by the base station are often interrupted, delayed, and/or discontinuous, and in the absence of base station observation data the corresponding positioning accuracy of a rover declines rapidly. With the strategies proposed till date, the positioning accuracy can only be maintained at the centimeter-level for a short span of time, no more than three min. To address this, a novel asynchronous RTK method (that addresses asynchronous errors) that can bridge significant gaps in the observations at the base station is proposed. First, satellite clock and orbital errors are eliminated using the products of the final precise ephemeris during post-processing or the ultra-rapid precise ephemeris during real-time processing. Then the tropospheric error is corrected using the Saastamoinen model and the asynchronous ionospheric delay is corrected using the carrier phase measurements from the rover receiver. Finally, a straightforward first-degree polynomial function is used to predict the residual asynchronous error. Experimental results demonstrate that the proposed approach can achieve centimeter-level accuracy for as long as 15 min during interruptions in both real-time and post-processing scenarios, and that the accuracy of the real-time scheme can be maintained for 15 min even when a large systematic error is projected in the U direction.

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Vol 26 (3) ◽  
pp. 339-357 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

Scapholunate Ligament Injury

2019 ◽  
pp. 201-234
Author(s):  
Jonathan Adamthwaite ◽  
Sina Babazadeh ◽  
Marc Garcia-Elias
Keyword(s):  
Ligament Injury ◽  
Scapholunate Ligament ◽  
Scapholunate Ligament Injury

scholarly journals Modern Trends in Imaging V: Optical Coherence Tomography for Rapid Tissue Screening and Directed Histological Sectioning

2012 ◽  
Vol 35 (3) ◽  
pp. 129-143 ◽  
Author(s):  
Woonggyu Jung ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Real Time ◽  
Image Guidance ◽  
Diagnostic Yield ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Optical Coherence ◽  
Sampling Errors ◽  
Cross Sectional

In pathology, histological examination of the “gold standard” to diagnose various diseases. It has contributed significantly toward identifying the abnormalities in tissues and cells, but has inherent drawbacks when used for fast and accurate diagnosis. These limitations include the lack ofin vivoobservation in real time and sampling errors due to limited number and area coverage of tissue sections. Its diagnostic yield also varies depending on the ability of the physician and the effectiveness of any image guidance technique that may be used for tissue screening during excisional biopsy. In order to overcome these current limitations of histology-based diagnostics, there are significant needs for either complementary or alternative imaging techniques which perform non-destructive, high resolution, and rapid tissue screening. Optical coherence tomography (OCT) is an emerging imaging modality which allows real-time cross-sectional imaging with high resolutions that approach those of histology. OCT could be a very promising technique which has the potential to be used as an adjunct to histological tissue observation when it is not practical to take specimens for histological processing, when large areas of tissue need investigating, or when rapid microscopic imaging is needed. This review will describe the use of OCT as an image guidance tool for fast tissue screening and directed histological tissue sectioning in pathology.

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