Direct Mapping Method Research Articles

Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities

BackgroundThe Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure.MethodsService users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range.ResultsAnalyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior.ConclusionsThis study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

The Perturbed Chen–Lee–Liu Equation: Diverse Optical Soliton Solutions and Other Wave Solutions

The objective of the present study is to extract the optical soliton solutions (OSSs) of the perturbed Chen–Lee–Liu equation by exerting three techniques, which are the extended Wang’s direct mapping method, tanh/coth function method and the Subequation method. Various OSSs, including the dark, bright, and singular soliton solutions, as well as other wave solutions, such as the singular periodic and algebraic solitary wave solutions, are successfully found. By assigning the appropriate parameters, the dynamic characteristics of the attained solutions are depicted graphically via the Mathematica software. The outcomes confirm that the methods are highly effective for constructing the OSSs and can also be adopted to investigate the other PDEs in optics.

Temporal resolution of ultrafast compressive imaging using a single-chirped optical probe.

Ultrafast compressive imaging captures three-dimensional spatiotemporal information of transient events in a single shot. When a single-chirped optical probe is applied, the temporal information is obtained from the probe modulated in amplitude or phase using a direct frequency-time mapping method. Here, we extend the analysis of the temporal resolution of conventional one-dimensional ultrafast measurement techniques such as spectral interferometry to that in three-dimensional ultrafast compressive imaging. In this way, both the amplitude and phase of the probe are necessary for a full Fourier transform method, which obtains temporal information with an improved resolution determined by probe spectral bandwidth. The improved temporal resolution potentially enables ultrafast compressive imaging with an effective imaging speed at the quadrillion-frames-per-second level.

Diverse optical solitons to the Radhakrishnan–Kundu–Lakshmanan equation for the light pulses

In this paper, we study the Radhakrishnan–Kundu–Lakshmanan equation (RKLE) that plays a key role in modeling the nonlinear propagation of the light pulses. Two novel methods namely Wang’s Bäcklund transformation-based method and Wang’s direct mapping method are employed to construct the diverse exact optical soliton solutions. Abundant exact solutions like the bright soliton solution, dark soliton solutions, singular soliton solutions, algebraic solitary wave solutions and the singular periodic wave solutions expressed in the form of the rational function type, double-exp function type, Sin-Cos function type, Tan function type, Cot function type, Sinh-Cosh function type, Tanh function type, Coth function type, Sech function type, Csch function type, Sec function type and Csc function type are obtained. The dynamics of the different exact solutions are presented through the 3D plot, contour and 2D curve, and the corresponding physical interpretations are elaborated in detail. It is confirmed that the proposed methods are effective and powerful, and can be adopted to construct the optical solitons of the other PDEs arising in the optical physics.

The pulse narrowing nonlinear transmission lines model within the local fractional calculus on the Cantor sets

PurposeThe fractal and fractional calculus have obtained considerable attention in the electrical and electronic engineering since they can model many complex phenomena that the traditional integer-order calculus cannot. The purpose of this paper is to develop a new fractional pulse narrowing nonlinear transmission lines model within the local fractional calculus for the first time and derive a novel method, namely, the direct mapping method, to seek for the nondifferentiable (ND) exact solutions.Design/methodology/approachBy defining some special functions via the Mittag–Leffler function on the Cantor sets, a novel approach, namely, the direct mapping method is derived via constructing a group of the nonlinear local fractional ordinary differential equations. With the aid of the direct mapping method, four groups of the ND exact solutions are obtained in just one step. The dynamic behaviors of the ND exact solutions on the Cantor sets are also described through the 3D graphical illustration.FindingsIt is found that the proposed method is simple but effective and can construct four sets of the ND exact solutions in just one step. In addition, one of the ND exact solutions becomes the exact solution of the classic pulse narrowing nonlinear transmission lines model for the special case 9 = 1, which strongly proves the correctness and effectiveness of the method. The ideas in the paper can be used to study the other fractal partial differential equations (PDEs) within the local fractional derivative (LFD) arising in electrical and electronic engineering.Originality/valueThe fractional pulse narrowing nonlinear transmission lines model within the LFD is proposed for the first time in this paper. The proposed method in the work can be used to study the other fractal PDEs arising in electrical and electronic engineering. The findings in this work are expected to shed a light on the study of the fractal PDEs arising in electrical and electronic engineering.

A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction.

We propose a dual-domain deep learning technique for accelerating compressed sensing magnetic resonance image reconstruction. An advanced convolutional neural network with residual connectivity and an attention mechanism was developed for frequency and image domains. First, the sensor domain subnetwork estimates the unmeasured frequencies of k-space to reduce aliasing artifacts. Second, the image domain subnetwork performs a pixel-wise operation to remove blur and noisy artifacts. The skip connections efficiently concatenate the feature maps to alleviate the vanishing gradient problem. An attention gate in each decoder layer enhances network generalizability and speeds up image reconstruction by eliminating irrelevant activations. The proposed technique reconstructs real-valued clinical images from sparsely sampled k-spaces that are identical to the reference images. The performance of this novel approach was compared with state-of-the-art direct mapping, single-domain, and multi-domain methods. With acceleration factors (AFs) of 4 and 5, our method improved the mean peak signal-to-noise ratio (PSNR) to 8.67 and 9.23, respectively, compared with the single-domain Unet model; similarly, our approach increased the average PSNR to 3.72 and 4.61, respectively, compared with the multi-domain W-net. Remarkably, using an AF of 6, it enhanced the PSNR by 9.87 ± 1.55 and 6.60 ± 0.38 compared with Unet and W-net, respectively.

A point cloud segmentation method for power lines and towers based on a combination of multiscale density features and point-based deep learning

ABSTRACT The point segmentation of power lines and towers aims to use unmanned aerial vehicles (UAVs) for the inspection of power facilities, risk detection and modelling. Because of the unclear spatial relationship between the point clouds, the point segmentation of power lines and towers is challenging. In this paper, the power line and tower point datasets are constructed using Light Detection and Ranging (LiDAR) and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network. First, the data are blocked and the neighbourhood is constructed. Second, the point clouds are downsampled to produce sparse point clouds. The point clouds before and after sampling are rotated, and their density is calculated. Next, a direct mapping method is selected to fuse the density information; a lightweight network is built to learn the features. Finally, the point clouds are segmented by concatenating the local features provided by PointCNN. The algorithm performs effectively on different types of power lines and towers. The mean interaction over union is 82.73%, and the overall accuracy can reach 91.76%. This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.

Investigation of optical soliton solutions of higher-order nonlinear Schrödinger equation having Kudryashov nonlinear refractive index

Purpose:In this paper, optical solitons of higher-order nonlinear Schrödinger equation with Kudryashov’s sextic power-law of nonlinear refractive index are investigated via the direct mapping method. The considered model identifies the optical soliton pulse propagation in the optical fibers. Deriving the optical solutions of investigated model such as sextic power is critical but difficult work. The primary aim of this paper is to graphically examine the impact of power-law nonlinearity (pLawNL) and chromatic dispersion (CD) parameters clarifying self-phase modulation (SPM) in the equation on soliton behavior as well as obtaining optical soliton solutions. Methodology:To according to the used technique, we first used the complex wave transform to generate the nonlinear ordinary differential equation (NLODE) form of the nonlinear Schrödinger equation (NLSE) with Kudryashov’s sextic power-law the nonlinear refractive index (SPLawNRI). Then, we were able to produce a system of linear equations in polynomial form by using the approach. Different solution sets including the values of the parameters of the studied equation and the suggested approach were produced by solving the linear system of equations. Findings:We acquired the optical soliton solutions of the main equation after inserting the sets and wave transformation into the solution functions suggested by the approach. The constraint conditions for the related solutions were suggested. We proved that the gained solutions satisfied the NLSE with Kudryashov’s SPLawNRI under the suggested constraint conditions. Originality:We present contour, 3D and 2D depictions in various simulations in figures to comment the obtained solution functions. Besides, we investigate the effects of the power-law nonlinearity parameter that expresses SPM in the main equation and the parameter that are the group velocity dispersion (GVD) or chromatic dispersion (CD) on soliton behavior. The results suggest that the utilized approach is efficient, reliable, and powerful to be readily applied to various NLSEs with higher-order or higher pLawNLs that characterize real-life problems.

Obtaining EQ-5D-3L utility index from the health status scale of traditional Chinese medicine (TCM-HSS) based on a mapping study

BackgroundAlmost all traditional Chinese medicine (TCM) quality of life measures are non-preference-based measures (non-PBMs), which do not provide utilities for cost-utility analysis in pharmacoeconomic evaluation. Whereas the mapping has become a new instrument to obtain utilities, which builds a bridge between non-PBMs and PBMs.PurposeTo develop mapping algorithms from the health status scale of traditional Chinese medicine (TCM-HSS) onto the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L).MethodsThe cross-sectional data were collected by questionnaire survey from a tertiary hospital visit population and community residents in China, and randomly divided into training and validation set by 2:1. Based on the training set, direct and indirect mapping methods (7 regression methods and 4 model specifications) were conducted to establish alternative models, which were comprehensively evaluated based on the validation set by mean absolute error, root mean square error, and Spearman correlation coefficient between predicted and observed values. Based on the whole sample, the preferred mapping algorithm was developed.ResultsA total of 639 samples were included, with an average age of 45.24 years and 61.66% of respondents were female. The mean EQ-5D-3L index was 0.9225 [SD = 0.1458], and the mean TCM-HSS index was 3.4144 [SD = 3.1154]. The final mapping algorithm was a two-part regression model including the TCM-HSS subscales, interaction terms, and demographic covariates (age and gender). The prediction performance was good. The mean error was 0.0003, the mean absolute error was 0.0566, the root mean square error was 0.1039, and 83.10% of the prediction errors were within 0.1; the Spearman correlation coefficient between predicted and observed EQ-5D-3L values was 0.6479.ConclusionIt is the first study to develop a mapping algorithm between the TCM-HSS and EQ-5D-3L, which demonstrates excellent prediction accuracy and estimates utility value for economic evaluation from TCM quality of life measures.

Diverse optical soliton solutions to the Kundu-Mukherjee-Naskar equation via two novel techniques

In this work, the Kundu-Mukherjee-Naskar (KMN) equation that addresses the optical soliton dynamics in (2 +1)-dimensions is studied via two novel techniques, which are the Cole-Hopf transform based method and Wang’s direct mapping method for the first time. Firstly, based on the Cole-Hopf transform, the symbolic computation with the ansatz function schemes are employed to construct the abundant soliton solutions. Some new soliton solutions including the rational breathe, cross-periodic, multi-wave, double exponential form and interaction soliton solutions are obtained. Secondly, Wang’s direct mapping method is adopted to seek for the breather, bright-dark and kinky periodic soliton solutions. Meanwhile, the behaviors of the different soliton solutions are presented in the form of 3-D plot, 2-D contour and 2-D curve. The ideas in this work are expected to provide some new enlightenment for the study of the exact soliton solutions of the PDEs in the optics.

On the Efficiency of the Advancing-Front Surface Mesh Generation Algorithm

An efficient advancing front algorithm is presented to create high-quality surface elements on CAD models. Various novel schemes are developed to improve the efficiency of the algorithm while not sacrificing its robustness and the resulting mesh quality. New features include a fast projection scheme that can perform robustly in case of badly defined surfaces, a procedure that computes the ideal point by an approach that can preserve the advantageous features of both the mapping and direct methods, and an octree data structure that can speed up the local search of all existing front nodes about the ideal point. To our knowledge, the proposed algorithm is one of the fastest algorithms for surface mesh generation. On an AMD Ryzen 5 3600 machine, the sequential version of the proposed algorithm can create a quality mesh at a speed of up to 100,000 elements per second. This speed is more than 3 times higher than a state-of-the-art code can achieve. Another advantage of the proposed algorithm is its ability to maintain its high efficiency when millions or more elements are created on one single surface. In this circumstance, the efficiency of some state-of-the-arts codes slows down by one or two orders. After parallelized based on the taskflow approach, the proposed algorithm can create more than 1.6 million elements on real-world configurations in 7 s by using 6 computer cores. This speed is higher than a state-of-the-art commercial code can achieve by one order. • An efficient surface mesh generator for large-scale triangular mesh generation of industrial CAD models, which is far faster than existing schemes. • The nonlinear calculation such as orthogonal projection and ideal point calculation is comprehensively improved. • Traditional surface mesh generation methods such as direct method and mapping method are combined. • The design of spatial index data structure (octree and AD tree) makes the algorithm have strong scalability.

A GtoG Direct Coding Mapping Method for Multi-Type Global Discrete Grids Based on Space Filling Curves

DGGS (Discrete Global Grid System) has many subdivision models and coding methods. Due to the lack of underlying consistency of different DGGS codes, most of them are converted through longitude–latitude, which greatly reduces the interoperability efficiency of different DGGS data and has become one of the bottlenecks in efficient integration of multi-source DGGS data. Therefore, a direct mapping method from one grid code to another (Grid to Grid, GtoG) for multi-type DGGSs is proposed based on three classical DGGSs (triangular, diamond and hexagonal grids) and two commonly used filling curves (Hilbert curve and Z-curve). The mutual conversion rules of different grids expressing spatial point, line and surface data are constructed. Then, the above method is extended to the spherical icosahedral grid framework, and three different region coding mapping rule tables of the basic inside cells, boundary cells and vertex cells are designed. Finally, the experimental results show that, compared with the longitude–latitude conversion method, the average conversion efficiency of spatial point, line and surface data is increased by 2–4 orders of magnitude. This new method greatly improves the interoperability efficiency and provides a feasible solution for the efficient integration of multi-source DGGS data.

Optimization of electric vehicle sound package based on LSTM with an adaptive learning rate forest and multiple-level multiple-object method

The sound absorption and sound insulation performance of an acoustic package (AP) system directly affect the noise, vibration and harshness performance of a vehicle. Numerous studies have studied the optimization of vehicle sound package, however, there are two deficiencies in the current research of sound package: (1) The noise transmission path of acoustic package is complex and hierarchical. Most of the related works focus on the data-driven part while ignoring the knowledge attributes behind the acoustic package design problem, which limits the further improvement of prediction and optimization of acoustic package performance. (2) In using intelligent neural networks-based methods such as long short-term memory (LSTM), reducing the learning rate during training gradually narrows the search interval of a solution, and adjusting the learning rate in a small range may tend to trap local optima. In this study, a knowledge- and data-driven approach is proposed for the development of acoustic package systems. A multiple-level multiple-object method is proposed as the knowledge model, and a multilayer structure of the acoustic package system that contains the system, subsystem and component layers is developed. In addition, an improved long short-term memory model based on an adaptive learning rate forest, which can increase and decrease the learning rate adaptively, is proposed as the data-driven model. The knowledge- and data-driven method is applied to optimize the sound absorption and insulation of the acoustic package system. In the experimental validation, the effectiveness and robustness of the proposed method outperformed the traditional direct mapping method and the conventional long short-term memory method.

Mapping the Patient-Reported Outcomes Measurement Information System (PROMIS-29) to EQ-5D-5L.

The Patient-Reported Outcomes Measurement Information System (PROMIS-29) is gaining popularity as healthcare system funders increasingly seek value-based care. However, it is limited in its ability to estimate utilities and thus inform economic evaluations. This study develops the first mapping algorithm for estimating EuroQol 5-Dimension 5-Level (EQ-5D-5L) utilities from PROMIS-29 responses using a large dataset and through extensive comparisons between econometric models. An online survey was conducted to collect responses to PROMIS-29 and EQ-5D-5L from the general Australian population (N = 3013). Direct and indirect mapping methods were explored, including linear regression, Tobit, generalised linear model, censored regression model, beta regression (Betamix), the adjusted limited dependent variable mixture model (ALDVMM) and generalised ordered logit. The most robust model was selected by assessing the performance based on average ten-fold cross-validation geometric mean absolute error and geometric mean squared error, the predicted mean, maximum and minimum utilities, as well as the fitting across the entire distribution. The direct approach using ALDVMM was considered the preferred model based on lowest geometric mean absolute error and geometric mean squared error in cross-validation (0.0882, 0.0299) and its superiority in predicting the actual observed mean, full health states and lower utility extremes. The robustness and precision in prediction across the entire distribution of utilities with ALDVMM suggest it is an accurate and valid mapping algorithm. Moreover, the suggested mapping algorithm outperformed previously published algorithms using Australian data, indicating the validity of this model for economic evaluations. This study developed a robust algorithm to estimate EQ-5D-5L utilities from PROMIS-29. Consistent with the recent literature, the ALDVMM outperformed all other econometric models considered in this study, suggesting that the mixture models have relatively better performance and are an ideal candidate model for mapping.

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach

Objective. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning methods for achieving direct mapping from detector data to the intrinsic dose distribution. Approach. We performed Monte Carlo simulations using GATE/Geant4 10.4 simulation toolkits to generate a dataset using human CT phantom irradiated with high-energy protons and imaged with compact in-beam PET for realistic beam delivery in a single-fraction (∼2 Gy). We developed a neural network model based on conditional generative adversarial networks to generate dose maps conditioned on coincidence distributions in the detector. The model performance is evaluated by the mean relative error, absolute dose fraction difference, and shift in Bragg peak position. Main results. The relative deviation in the dose and range of the distributions predicted by the model from the true values for mono-energetic irradiation between 50 and 122 MeV lie within 1% and 2%, respectively. This was achieved using 105 coincidences acquired five minutes after irradiation. The relative deviation in the dose and range for spread-out Bragg peak distributions were within 1% and 2.6% uncertainties, respectively. Significance. An important aspect of this study is the demonstration of a method for direct mapping from detector counts to dose domain using the low count data of compact detectors suited for practical implementation in particle therapy. Including additional prior information in the future can further expand the scope of our model and also extend its application to other areas of medical imaging.

Onboard Estimation of Mean Orbital Elements Extended to State Jumping Case

Mean orbital elements play a significant role in constellation guidance, orbital maintenance, and transfer strategy design. Hence, research on mean orbital elements estimation flourishes with the development of mega-constellation construction, for instance, the Starlink program. This paper investigates the autonomous on-orbit estimation problem of mean orbital elements under the consideration of real-time mean orbital elements acquisition, reliable mean orbital elements prediction, and extreme condition handling of state jumping caused by protective orbit transfer or nonfatal spacecraft collision. A novel mean orbital elements estimator based on the Savitzky–Golay filter (“Smoothing and Differentiation of Data by Simplified Least Squares Procedures,” Analytical Chemistry, Vol. 36, No. 8, 1964, pp. 1627–1639) with adaptive frame size, maneuvering detector, and multimode switching mechanism is proposed. Adaptive frame size according to the orbital period for completing the Savitzky–Golay process guarantees the estimation efficiency and accuracy of mean orbital elements estimation and prediction. Then, the prediction value is employed to generate the mean orbital elements difference compared with the estimation. Finally, the mean orbital elements difference is adopted to govern the satellite state detector for mode switching. Comparison simulations for both with and without state jumping are conducted, and the results demonstrate the superiority of the mean orbital elements estimator over the direct mapping method and the Savitzky–Golay filter in estimation efficiency, accuracy, and range of application.

Reducing fronthaul traffic with direct constellation mapping method

Reducing fronthaul traffic with direct constellation mapping method

Geology and Potential Flash Flood Vulnerability of Sungai Jernih Village and Surroundings, Pondok Tinggi District, Kerinci Regency, Jambi

Sungai Jernih Village and its surroundings have relatively steep slopes. Based on rainfall data for the last three years this area has moderate rainfall, with a tropical climate. In general, making the weathering level around it high and causing weathered soil/rock material conditions so that it is not strong retain the volume of water that is absorbed by the surface. The weathering can be carried away by water currents and flash floods. Pondok Tinggi sub-district has experienced flash floods, one of which was on May 13, 2017. The method used in this study is a direct mapping method, namely conducting field observations to map the distribution of lithology. Prior to field observations, analysis of secondary data obtained from literature studies and other sources was carried out to consider the results of field observations as a basic reference for research. Mapping results in the form of maps and parameter analysis using scoring overlaid with ArcGIS software. The low level of vulnerability with an area of ​​51.25% consists of 2 sub-districts, are in Pondok Tinggi District and Kumun District. The level of vulnerability is sufficient with an area of ​​25.39% consisting of 2 sub-districts are Sungai Bungkal District and Pondok Tinggi District. Then the highest vulnerability level with an area of ​​23.36% consists of 4 districts are Pesisir Bukit District, Sungai Bungkal District, Sungai Bungkal District Full and Pondok Tinggi District.

GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic.

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

Phase-contrast acceleration mapping with synchronized encoding.

To develop a phase-contrast (PC) -based method for direct and unbiased quantification of the acceleration vector field by synchronization of the spatial and acceleration encoding time points. The proposed method explicitly aims at in-vitro applications, requiring high measurement accuracy, as well as the validation of clinically relevant acceleration-encoded sequences. A velocity-encoded sequence with synchronized encoding (SYNC SPI) was modified to allow direct acceleration mapping by replacing the bipolar encoding gradients with tripolar gradient waveforms. The proposed method was validated in two in-vitro flow cases: a rotation and a stenosis phantom. The thereby obtained velocity and acceleration vector fields were quantitatively compared to those acquired with conventional PC methods, as well as to theoretical data. The rotation phantom study revealed a systematic bias of the conventional PC acceleration mapping method that resulted in an average pixel-wise relative angle between the measured and theoretical vector field of (7.8 ± 3.2)°, which was reduced to (-0.4 ± 2.7)° for the proposed SYNC SPI method. Furthermore, flow features in the stenosis phantom were displaced by up to 10 mm in the conventional PC data compared with the acceleration-encoded SYNC SPI data. This work successfully demonstrates a highly accurate method for direct acceleration mapping. It thus complements the existing velocity-encoded SYNC SPI method to enable the direct and unbiased quantification of both the velocity and acceleration vector field for in vitro studies. Hence, this method can be used for the validation of conventional acceleration-encoded PC methods applicable in-vivo.