scholarly journals Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5371
Author(s):  
Michał Staniszewski ◽  
Uwe Klose
Keyword(s):  
T1 Mapping ◽  
Region Of Interest ◽  
Computational Time ◽  
Single Shot ◽  
Mean Values ◽  
Model Based ◽  
Fitting Procedure ◽  
Novel Method ◽  
Space Data ◽  
Brain Measurements

Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T1-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter—0.3% and 1.1%, grey matter—0.4% and 1.78% and cerebrospinal fluid—2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer.

scholarly journals Fast Interleaved Multislice T1 Mapping: Model-Based Reconstruction of Single-Shot Inversion-Recovery Radial FLASH

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoqing Wang ◽  
Dirk Voit ◽  
Volkert Roeloffs ◽  
Martin Uecker ◽  
Jens Frahm
Keyword(s):  
High Speed ◽  
T1 Mapping ◽  
Inversion Recovery ◽  
Inversion Pulse ◽  
Breath Hold ◽  
Single Shot ◽  
Radial Sampling ◽  
Model Based ◽  
Inverse Reconstruction ◽  
Space Data

Purpose. To develop a high-speed multislice T1 mapping method based on a single-shot inversion-recovery (IR) radial FLASH acquisition and a regularized model-based reconstruction. Methods. Multislice radial k-space data are continuously acquired after a single nonselective inversion pulse using a golden-angle sampling scheme in a spoke-interleaved manner with optimized flip angles. Parameter maps and coil sensitivities of each slice are estimated directly from highly undersampled radial k-space data using a model-based nonlinear inverse reconstruction in conjunction with joint sparsity constraints. The performance of the method has been validated using a numerical and experimental T1 phantom as well as demonstrated for studies of the human brain and liver at 3T. Results. The proposed method allows for 7 simultaneous T1 maps of the brain at 0.5 × 0.5 × 4 mm3 resolution within a single IR experiment of 4 s duration. Phantom studies confirm similar accuracy and precision as obtained for a single-slice acquisition. For abdominal applications, the proposed method yields three simultaneous T1 maps at 1.25 × 1.25 × 6 mm3 resolution within a 4 s breath hold. Conclusion. Rapid, robust, accurate, and precise multislice T1 mapping may be achieved by combining the advantages of a model-based nonlinear inverse reconstruction, radial sampling, parallel imaging, and compressed sensing.

Model-based reconstruction for T1 mapping using single-shot inversion-recovery radial FLASH

2016 ◽  
Vol 26 (4) ◽  
pp. 254-263 ◽  
Author(s):  
Volkert Roeloffs ◽  
Xiaoqing Wang ◽  
Tilman J. Sumpf ◽  
Markus Untenberger ◽  
Dirk Voit ◽  
...  
Keyword(s):  
T1 Mapping ◽  
Inversion Recovery ◽  
Single Shot ◽  
Model Based

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

scholarly journals Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1107
Author(s):  
Stefano d’Ambrosio ◽  
Roberto Finesso ◽  
Gilles Hardy ◽  
Andrea Manelli ◽  
Alessandro Mancarella ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Rapid Prototyping ◽  
Nitrogen Oxide ◽  
Thermal State ◽  
Pollutant Emissions ◽  
Computational Time ◽  
Nox Emissions ◽  
Model Based ◽  
Brake Mean Effective Pressure ◽  
The Impact

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

scholarly journals A novel method of photogrammetry measurements of study models in orthodontics

2019 ◽  
Vol 147 (1-2) ◽  
pp. 10-16
Author(s):  
Marijana Arapovic-Savic ◽  
Mihajlo Savic ◽  
Mirjana Umicevic-Davidovic ◽  
Adriana Arbutina ◽  
Nenad Nedeljkovic ◽  
...  
Keyword(s):  
Information Technologies ◽  
3D Models ◽  
3D Scanner ◽  
Mean Values ◽  
Digital Representations ◽  
Perspective Distortion ◽  
Tooth Width ◽  
Significant Difference ◽  
Novel Method ◽  
Camera Position

Introduction/Objective. Rapid developments in information technologies lead to the wider use of digital representations of dental study models in orthodontics. Most popular way of digitizing the models is to use a 3D scanner and then perform measurements on 3D models, which requires additional and expensive hardware and software resources. In this paper we present an alternative approach based on the use of photogrammetry in the newly developed OrthoPhoto4D software that calculates and corrects perspective distortion errors. Methods. We measured individual tooth width for 24 teeth, 12 two-teeth segments as well as inter-molar and inter-canine distances on 50 models. Measurements are performed in OrthoPhoto4D software that uses four photographs of each model for measurements, uses QR codes for automation, calculates the camera position and corrects perspective distortion-caused errors in measurements. Obtained measurements are compared to ones obtained from models generated by structured light 3D scanner. Results. Statistical analysis strongly indicates that there is no significant difference between the two methods. The recorded differences also have no clinical impact as they have mean values of 0.2 mm for individual tooth widths, approximately 0.2 mm for two teeth segments, and under 0.3 mm for both intercanine and inter-molar distances. All recorded differences fall within the expected measurement error. Conclusion. We concluded that the described photogrammetry measurements performed in OrthoPhoto4D can be used in diagnosis and therapy planning.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

scholarly journals Performances of different DFT functionals to calculate the anodic limit of fluorinated sulphonyl-imide anions for lithium cells

2021 ◽  
Vol 2090 (1) ◽  
pp. 012078
Author(s):  
A Paolone ◽  
S Brutti
Keyword(s):  
Ionic Liquids ◽  
Lithium Batteries ◽  
Basis Set ◽  
Basis Sets ◽  
Computational Time ◽  
Generalized Gradient ◽  
Neutral Species ◽  
Generalized Gradient Approximation ◽  
Model Based ◽  
Computationally Expensive

Abstract In this paper we investigated the calculation of the anodic limit of two anions of ionic liquids, largely used as electrolyte of lithium batteries. Starting from a model based on calculations performed on single ions at the MP2 level of theory, we showed that the matching between calculation and experiments decreases while using more expanded basis set with respect to 6-31G**, possibly because of the destabilization of the neutral species when larger basis sets are considered. Additionally, in order to decrease the computational time, the performances for the calculation of the anodic limit obtained by means of a series of DFT functionals with increasing level of complexity (from the Generalized Gradient Approximation to the Range Separated Hybrid meta-Generalized Gradient Approximation) were compared. Overall, the best performing functionals are BMK, ωB97M-V and MN12-SX, while acceptable results can be obtained by M06-2X, M11, M08-HX and M11-L. Some less computationally expensive functionals, like CAM-B3LYP and ωB97X-D, also provide reasonable values of the anodic limit.

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