feng liu

Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Publisher: MDPI AG

Date: 05-02-2021

DOI: 10.3390/APP11041435

Abstract: Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-s ling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

Passive Shim Design and a Shimming Approach for Biplanar Permanent Magnetic Resonance Imaging Magnets

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 03-2008

DOI: 10.1109/TMAG.2007.914770

A dedicated eight‐channel receive RF coil array for monkey brain MRI at 9.4 T

Publisher: Wiley

Date: 30-07-2020

DOI: 10.1002/NBM.4369

Pseudo-Polar Fourier Transform-Based Compressed Sensing MRI

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-2017

DOI: 10.1109/TBME.2016.2578930

Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/ACCESS.2021.3058271

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

Publisher: Elsevier BV

Date: 05-2022

DOI: 10.1016/J.ZEMEDI.2021.06.004

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Publisher: Elsevier BV

Date: 03-2015

DOI: 10.1016/J.JMR.2014.12.004

Abstract: Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) was capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo sensitivity profiles (B1(-)) become distinct at various angular positions. Therefore, sensitivity maps at other angular positions cannot be obtained by numerically rotating the acquired ones. In this work, a novel sensitivity estimation method for the RRFCA was developed and validated with human brain imaging. This method employed a library database and registration techniques to estimate coil sensitivity at an arbitrary angular position. The estimated sensitivity maps were then compared to the acquired sensitivity maps. The results indicate that the proposed method is capable of accurately estimating both magnitude and phase of sensitivity at an arbitrary angular position, which enables us to employ the rotating-SENSE algorithm to accelerate acquisition and reconstruct image. Compared to a stationary coil array with the same number of coil elements, the RRFCA was able to reconstruct images with better quality at a high reduction factor. It is hoped that the proposed rotation-dependent sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI.

An Efficient Integral-Based Method for Three-Dimensional MR-EPT and the Calculation of the RF-Coil-Induced ${B_z}$ Field

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2018

DOI: 10.1109/TBME.2017.2763590

Metamaterial-Inspired Radiofrequency (RF) Shield With Reduced Specific Absorption Rate (SAR) and Improved Transmit Efficiency for UHF MRI

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-2021

DOI: 10.1109/TBME.2020.3022884

Inverse design of a phased-array coil for breast magnetic resonance imaging

Publisher: Wiley

Date: 28-10-2009

DOI: 10.1002/CMR.B.20145

Improved k-

Publisher: Hindawi Limited

Date: 2017

DOI: 10.1155/2017/4816024

Abstract: The k - t principal component analysis ( k - t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise lification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k - t PCA that combines the k - t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k - t PCA method by further eliminating the reconstruction error derived from complex subtraction of the s led k - t space from the original reconstructed k - t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k - t PCA algorithm, the sparse k - t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.

An actively shielded gradient coil design for use in planar MRI systems with limited space

Publisher: AIP Publishing

Date: 09-2018

DOI: 10.1063/1.5043331

Abstract: In planar magnetic resonance imaging (MRI) systems, gradient coils are usually placed within a very limited space owing to the physical constraints of the small gap size (pole-pole) distance of the permanent magnet. Typically, the unshielded or partially shielded design scheme is adopted to generate required magnetic fields with reduced system costs. However, non-fully shielded coils can induce large eddy currents on the surrounding metal structures, including magnet poles, that significantly impact the imaging performance. This paper elaborates a new design strategy to resolve the limited space problem. Using the peripheral sections of the MRI system, a set of actively shielded gradient coils are purposefully designed. Between the two magnet poles, the actively shielded gradient coils occupy merely four coil layers (six coil layers are usually required), which offers an excellent shielding effect, thus reducing the image distortions. The saved space can be used to integrate a high-efficient cooling system. Moreover, the design scheme does not significantly increase the fabricating complexity.

Inverse field-based approach for simultaneous B1 mapping at high fields – A phantom based study

Publisher: Elsevier BV

Date: 04-2012

DOI: 10.1016/J.JMR.2012.02.004

Abstract: Based on computational electromagnetics and multi-level optimization, an inverse approach of attaining accurate mapping of both transmit and receive sensitivity of radiofrequency coils is presented. This paper extends our previous study of inverse methods of receptivity mapping at low fields, to allow accurate mapping of RF magnetic fields (B(1)) for high-field applications. Accurate receive sensitivity mapping is essential to image domain parallel imaging methods, such as sensitivity encoding (SENSE), to reconstruct high quality images. Accurate transmit sensitivity mapping will facilitate RF-shimming and parallel transmission techniques that directly address the RF inhomogeneity issue, arguably the most challenging issue of high-field magnetic resonance imaging (MRI). The inverse field-based approach proposed herein is based on computational electromagnetics and iterative optimization. It fits an experimental image to the numerically calculated signal intensity by iteratively optimizing the coil-subject geometry to better resemble the experiments. Accurate transmit and receive sensitivities are derived as intermediate results of the optimization process. The method is validated by imaging studies using homogeneous saline phantom at 7T. A simulation study at 300MHz demonstrates that the proposed method is able to obtain receptivity mapping with errors an order of magnitude less than that of the conventional method. The more accurate receptivity mapping and simultaneously obtained transmit sensitivity mapping could enable artefact-reduced and intensity-corrected image reconstructions. It is hoped that by providing an approach to the accurate mapping of both transmit and receive sensitivity, the proposed method will facilitate a range of applications in high-field MRI and parallel imaging.

Detecting Depression Using Single-Channel EEG and Graph Methods

Publisher: MDPI AG

Date: 08-11-2022

DOI: 10.3390/MATH10224177

Abstract: Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe.

Highly accelerated acquisition and homogeneous image reconstruction with rotating RF coil array at 7T—A phantom based study

Publisher: Elsevier BV

Date: 03-2014

DOI: 10.1016/J.JMR.2013.11.002

Abstract: Parallel imaging (PI) is widely used for imaging acceleration by means of coil spatial sensitivities associated with phased array coils (PACs). By employing a time- ision multiplexing technique, a single-channel rotating radiofrequency coil (RRFC) provides an alternative method to reduce scan time. Strategically combining these two concepts could provide enhanced acceleration and efficiency. In this work, the imaging acceleration ability and homogeneous image reconstruction strategy of 4-element rotating radiofrequency coil array (RRFCA) was numerically investigated and experimental validated at 7T with a homogeneous phantom. Each coil of RRFCA was capable of acquiring a large number of sensitivity profiles, leading to a better acceleration performance illustrated by the improved geometry-maps that have lower maximum values and more uniform distributions compared to 4- and 8-element stationary arrays. A reconstruction algorithm, rotating SENSitivity Encoding (rotating SENSE), was proposed to provide image reconstruction. Additionally, by optimally choosing the angular s ling positions and transmit profiles under the rotating scheme, phantom images could be faithfully reconstructed. The results indicate that, the proposed technique is able to provide homogeneous reconstructions with overall higher and more uniform signal-to-noise ratio (SNR) distributions at high reduction factors. It is hoped that, by employing the high imaging acceleration and homogeneous imaging reconstruction ability of RRFCA, the proposed method will facilitate human imaging for ultra high field MRI.

Minimizing the induced fields in MRI occupational workers by lowering the imager

Publisher: Wiley

Date: 02-2008

DOI: 10.1002/CMR.B.20102

Universal Undersampled MRI Reconstruction

Publisher: Springer International Publishing

Date: 2021

DOI: 10.1007/978-3-030-87231-1_21

Technical Note: Sequential combination of parallel imaging and dynamic artificial sparsity framework for rapid free‐breathing golden‐angle radial dynamic MRI: K‐T ARTS‐GROWL

Publisher: Wiley

Date: 20-11-2017

DOI: 10.1002/MP.12639

Abstract: To develop and validate a fast dynamic MR imaging scheme. A novel approach termed K-T ARTificial Sparsity enhanced GROWL (K-T ARTS-GROWL) is proposed that integrates dynamic artificial sparsity and GROWL-based parallel imaging (PI). Golden-angle radial k-space data are acquired with the free-breathing s ling scheme and then sorted into a time series by grouping consecutive spokes into temporal frames. The reconstruction framework sequentially applies PI and dynamic artificial sparsity. In the implementation, GROWL is taken as a special PI instance for its high computational efficiency and the K-T sparse is exploited to improve the PI reconstruction performance, because the dynamic MR images are often sparse in the x-f domain. In the final reconstruction procedure, artificial sparsity is constructed and fed back to the previous reconstruction. The K-T ARTS-GROWL results in high spatial and temporal resolution reconstructions. By exploiting dynamic artificial sparsity, the acceleration capability is further improved compared to the PI alone. The experimental results demonstrate that K-T ARTS-GROWL leads to significantly better image quality (P < 0.05) than the frame-by-frame GROWL and frame-by-frame ARTS-GROWL for in vivo liver imaging. Compared with the tested K-T reconstruction algorithms, the K-T ARTS-GROWL results in a better or comparable image quality and temporal resolution with greatly decreased computational costs. The proposed technique enables sparse, fast imaging of high spatial, high temporal resolutions for dynamic MRI.

Hybrid numerical techniques for the modelling of radiofrequency coils in MRI

Publisher: Wiley

Date: 16-12-2008

DOI: 10.1002/NBM.1344

Abstract: Radiofrequency (RF) coils for use in MRI can have a significant effect on both the signal-to-noise-ratio of MR images and the specific absorption rate inside the biological s le. In the past, prototypes were constructed and tested to investigate the performance of the RF coils and often required several iterations to achieve an acceptable result. However, with the advancement in computational electromagnetic techniques, RF coil modelling has now become the modus operandi of coil design because it can produce accurate numerical results, thus reducing the time and effort spent in designing and prototyping RF coils. Two hybrid methods -method of moments (MoM)/finite difference time domain (FDTD) and MoM/finite element method (FEM) - for RF coil modelling are presented herein. The paper provides a brief overview of FDTD, FEM and MoM. It discusses the hybridisation of these methods and how they are integrated to form versatile techniques. The numerical results obtained from these hybrid methods are compared with experimental results from prototype coils over a range of operating frequencies. The methods are then applied to the design of a new type of phased-array coil - the rotary phased array. From these comparisons, it can be seen that the numerical methods provide a useful aid for the design and optimisation of RF coils for use in MRI.

3D-Gradient Coil Structures for MRI Designed using Fuzzy Membership Functions

Publisher: IEEE

Date: 08-2007

DOI: 10.1109/IEMBS.2007.4352721

Reference-Based Integral MR-EPT: Simulation and Experiment Studies at 9.4 T MRI

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-2019

DOI: 10.1109/TBME.2018.2879667

Compressed Sensing-Based Simultaneous Recovery of Magnitude and Phase MR Images via Dual Trigonometric Sparsity

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/ACCESS.2021.3062837

An electromagnetic reverse method of coil sensitivity mapping for parallel MRI – Theoretical framework

Publisher: Elsevier BV

Date: 11-2010

DOI: 10.1016/J.JMR.2010.08.009

Abstract: In this paper, a novel sensitivity mapping method is proposed for the image domain parallel MRI (pMRI) technique. Instead of refining raw sensitivity maps by means of conventional image processing operations such as polynomial fitting, the presented method determines coil sensitivity profiles through an iterative optimization process. During the algorithm implementation the optimization cost function is defined as the difference between the raw sensitivity profile and the desired profile. The minimization is governed by the physics of low-frequency electromagnetic and reciprocity theories. The performance of the method was theoretically investigated and compared with that of a traditional polynomial fitting, against a range of system noise levels. It was found that, the new method produces high-fidelity sensitivity profiles with noise litudes, measured as root mean square deviation an order of magnitude less than that of the polynomial fitting method. Using the sensitivity profiles generated by our method, SENSE (sensitivity encoding) reconstructions produce significantly less image artefacts than conventional methods. The successful implementation of this method has far-reaching implications that accurate sensitivity mapping is not only important for parallel reconstruction, but also essential for its transmission analogy, such as Transmit SENSE.

Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI‐Linac system

Publisher: Wiley

Date: 02-08-2020

DOI: 10.1002/MP.14382

On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks

Publisher: Elsevier BV

Date: 04-2021

DOI: 10.1016/J.MRI.2020.12.019

Image reconstructions with the rotating RF coil

Publisher: Elsevier BV

Date: 12-2009

DOI: 10.1016/J.JMR.2009.09.009

Abstract: Recent studies have shown that rotating a single RF transceive coil (RRFC) provides a uniform coverage of the object and brings a number of hardware advantages (i.e. requires only one RF channel, averts coil-coil coupling interactions and facilitates large-scale multi-nuclear imaging). Motion of the RF coil sensitivity profile however violates the standard Fourier Transform definition of a time-invariant signal, and the images reconstructed in this conventional manner can be degraded by ghosting artifacts. To overcome this problem, this paper presents Time Division Multiplexed-Sensitivity Encoding (TDM-SENSE), as a new image reconstruction scheme that exploits the rotation of the RF coil sensitivity profile to facilitate ghost-free image reconstructions and reductions in image acquisition time. A transceive RRFC system for head imaging at 2 Tesla was constructed and applied in a number of in vivo experiments. In this initial study, alias-free head images were obtained in half the usual scan time. It is hoped that new sequences and methods will be developed by taking advantage of coil motion.

A numerical and experimental study of RF shimming in the presence of hip prostheses using adaptive SAR at 3 T

Publisher: Wiley

Date: 25-02-2019

DOI: 10.1002/MRM.27688

Abstract: Parallel transmission techniques in MRI have the potential to improve the image quality near metal implants at 3 T. However, current testing of implants only evaluates the risk of radiofrequency (RF) heating in phantoms in circularly polarized mode. We investigate the influence of changing the transmission settings in a 2-channel body coil on the peak temperature near 2 CoCrMo hip prostheses, using adaptive specific absorption rate (SAR) as an estimate of RF heating. Adaptive SAR is a SAR averaging method that is optimized to correlate with thermal simulations and limit the temperature to 39°C near hip implants. The simulated peak temperature was compared when using whole-body SAR, SAR Simulations and measurements showed excellent agreement. Limiting whole-body SAR to 2 W/kg and SAR Significant RF heating can occur at 3 T near hip implants when parallel transmission is used. Adaptive SAR can be integrated in RF shimming algorithms to improve the uniformity and reduce heating.

Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

Publisher: Wiley

Date: 07-01-2020

DOI: 10.1002/MP.13979

Abstract: The magnetic resonance imaging (MRI)-Linac system combines a MRI scanner and a linear accelerator (Linac) to realize real-time localization and adaptive radiotherapy for tumors. Given that the Australian MRI-Linac system has a 30-cm diameter of spherical volume (DSV) with a shimmed homogeneity of ±4.05 parts per million (ppm), a gradient nonlinearity (GNL) of <5% can only be assured within 15 cm from the system's isocenter. GNL increases from the isocenter and escalates close to and outside of the edge of the DSV. Gradient nonlinearity can cause large geometric distortions, which may provide inaccurate tumor localization and potentially degrade the radiotherapy treatment. In this study, we aimed to characterize and correct the geometric distortions both inside and outside of the DSV. On the basis of phantom measurements, an inverse electromagnetic (EM) method was developed to reconstitute the virtual current density distribution that could generate gradient fields. The obtained virtual EM source was capable of characterizing the GNL field both inside and outside of the DSV. With the use of this GNL field information, our recently developed "GNL-encoding" reconstruction method was applied to correct the distortions implemented in the k-space domain. Both phantom and in vivo human images were used to validate the proposed method. The results showed that the maximal displacements within an imaging volume of 30 cm × 30 cm × 30 cm after using the fifth-order spherical harmonic (SH) method and the proposed method were 6.1 ± 0.6 mm and 1.8 ± 0.6 mm, respectively. Compared with the fifth-order SH-based method, the new solution decreased the percentage of markers (within an imaging volume of 30 cm × 30 cm × 30 cm) with ≥1.5-mm distortions from 6.3% to 1.3%, indicating substantially improved geometric accuracy. The experimental results indicated that the proposed method could provide substantially improved geometric accuracy for the region outside of the DSV, when comparing with the fifth-order SH-based method.

Optimizing multicontrast MRI reconstruction with shareable feature aggregation and selection

Publisher: Wiley

Date: 11-05-2021

DOI: 10.1002/NBM.4540

Abstract: This paper proposes a new method for optimizing feature sharing in deep neural network‐based, rapid, multicontrast magnetic resonance imaging (MC‐MRI). Using the shareable information of MC images for accelerated MC‐MRI reconstruction, current algorithms stack the MC images or features without optimizing the sharing protocols, leading to suboptimal reconstruction results. In this paper, we propose a novel feature aggregation and selection scheme in a deep neural network to better leverage the MC features and improve the reconstruction results. First, we propose to extract and use the shareable information by mapping the MC images into multiresolution feature maps with multilevel layers of the neural network. In this way, the extracted features capture complementary image properties, including local patterns from the shallow layers and semantic information from the deep layers. Then, an explicit selection module is designed to compile the extracted features optimally. That is, larger weights are learned to incorporate the constructive, shareable features and smaller weights are assigned to the unshareable information. We conduct comparative studies on publicly available T2‐weighted and T2‐weighted fluid attenuated inversion recovery brain images, and the results show that the proposed network consistently outperforms existing algorithms. In addition, the proposed method can recover the images with high fidelity under 16 times acceleration. The ablation studies are conducted to evaluate the effectiveness of the proposed feature aggregation and selection mechanism. The results and the visualization of the weighted features show that the proposed method does effectively improve the usage of the useful features and suppress useless information, leading to overall enhanced reconstruction results. Additionally, the selection module can zero‐out repeated and redundant features and improve network efficiency.

A comparison study of different RF shields for an 8-element transceive small animal array at 9.4T

Publisher: IEEE

Date: 08-2011

DOI: 10.1109/IEMBS.2011.6090100

Image Reconstruction for a Rotating Radiofrequency Coil (RRFC) Using Self-Calibrated Sensitivity From Radial Sampling

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2017

DOI: 10.1109/TBME.2016.2552489

The optimization of an 8-channel transceive volume array for small animal MRI at 9.4T

Publisher: IEEE

Date: 08-2011

DOI: 10.1109/IEMBS.2011.6090783

A MOM/FEM-based coil sensitivity mapping method for high-field parallel MRI

Publisher: IEEE

Date: 08-2011

DOI: 10.1109/IEMBS.2011.6090784

A Dichotomization Winding Scheme on a Novel Asymmetric Head Gradient Coil Design with an Improved Force and Torque Balance

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2019

DOI: 10.1109/TBME.2019.2904213

ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI‐Linac scanner

Publisher: Wiley

Date: 20-04-2021

DOI: 10.1002/MP.14861

Abstract: The hybrid system combining a magnetic resonance imaging (MRI) scanner with a linear accelerator (Linac) has become increasingly desirable for tumor treatment because of excellent soft tissue contrast and nonionizing radiation. However, image distortions caused by gradient nonlinearity (GNL) can have detrimental impacts on real‐time radiotherapy using MRI‐Linac systems, where accurate geometric information of tumors is essential. In this work, we proposed a deep convolutional neural network‐based method to efficiently re cover u ndistorted i mages ( ReUINet ) for real‐time image guidance. The ReUINet , based on the encoder‐decoder structure, was created to learn the relationship between the undistorted images and distorted images. The ReUINet was pretrained and tested on a publically available brain MR image dataset acquired from 23 volunteers. Then, transfer learning was adopted to implement the pretrained model (i.e., network with optimal weights) on the experimental three‐dimensional (3D) grid phantom and in‐vivo pelvis image datasets acquired from the 1.0 T Australian MRI‐Linac system. Evaluations on the phantom (768 slices) and pelvis data (88 slices) showed that the ReUINet achieved improvement over 15 times and 45 times on computational efficiency in comparison with standard interpolation and GNL‐encoding methods, respectively. Moreover, qualitative and quantitative results demonstrated that the ReUINet provided better correction results than the standard interpolation method, and comparable performance compared to the GNL‐encoding approach. Validated by simulation and experimental results, the proposed ReUINet showed promise in obtaining accurate MR images for the implementation of real‐time MRI‐guided radiotherapy.

Divergence-Based Magnetic Resonance Electrical Properties Tomography

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2021

DOI: 10.1109/TBME.2020.3003460

Integral MR-EPT With the Calculation of Coil Current Distributions

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/TMI.2019.2922318

Deep unregistered multi-contrast MRI reconstruction

Publisher: Elsevier BV

Date: 09-2021

DOI: 10.1016/J.MRI.2021.05.005

A Fast Parallel Imaging Rotary Phased Array Head Coil with Improved Sensitivity Profile Deep in the Center of the Brain

Publisher: IEEE

Date: 08-2007

DOI: 10.1109/IEMBS.2007.4352333

Hign acceleration with a rotating radiofrequency coil array (RRFCA) in parallel magnetic resonance imaging (MRI)

Publisher: IEEE

Date: 08-2012

DOI: 10.1109/EMBC.2012.6346126

Efficient sleep classification based on entropy features and a support vector machine classifier

Publisher: IOP Publishing

Date: 26-11-2018

DOI: 10.1088/1361-6579/AAE943

Abstract: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method based on entropy features and a support vector machine classifier, named SC-En&SVM. Entropy features, including fuzzy measure entropy (FuzzMEn), fuzzy entropy, and s le entropy are applied for the analysis and classification of sleep stages. FuzzyMEn has been used for heart rate variability analysis since it was proposed, while this is the first time it has been used for sleep scoring. The three features are extracted from 6 376 730 s epochs from Fpz-Cz electroencephalogram (EEG), Pz-Oz EEG and horizontal electrooculogram (EOG) signals in the sleep-EDF database. The independent s les t-test shows that the entropy values have significant differences among six sleep stages. The multi-class support vector machine (SVM) with a one-against-all class approach is utilized in this specific application for the first time. We perform 10-fold cross-validation as well as leave-one-subject-out cross-validation for 61 subjects to test the effectiveness and reliability of SC-En&SVM. The 10-fold cross-validation shows an effective performance with high stability of SC-En&SVM. The average accuracy and standard deviation for 2-6 states are 97.02 ± 0.58, 92.74 ± 1.32, 89.08 ± 0.90, 86.02 ± 1.06 and 83.94 ± 1.61, respectively. While for a more practical evaluation, the independent scheme is further performed, and the results show that our method achieved similar or slightly better average accuracies for 2-6 states of 94.15%, 85.06%, 80.96%, 78.68% and 75.98% compared with state-of-the-art methods. The corresponding kappa coefficients (0.81, 0.74, 0.72, 0.71, 0.67) guarantee substantial agreement of the classification. We propose a novel sleep stage scoring method, SC-En&SVM, with easily accessible features and a simple classification algorithm, without reducing the classification performance compared with other approaches.

Reverse-engineering of gradient coil designs based on experimentally measured magnetic fields and approximate knowledge of coil geometry-application in exposure evaluations

Publisher: Wiley

Date: 02-2009

DOI: 10.1002/CMR.B.20129

Improving SAR estimations in MRI using subject-specific models

Publisher: IOP Publishing

Date: 23-11-2012

DOI: 10.1088/0031-9155/57/24/8153

Abstract: To monitor and strategically control energy deposition in magnetic resonance imaging (MRI), measured as a specific absorption rate (SAR), numerical methods using generic human models have been employed to estimate worst-case values. Radiofrequency (RF) sequences are therefore often designed conservatively with large safety margins, potentially hindering the full potential of high-field systems. To more accurately predict the patient SAR values, we propose the use of image registration techniques, in conjunction with high-resolution image and tissue libraries, to create patient-specific voxel models. To test this, a matching model from the archives was first selected. Its tissue information was then warped to the patient's coordinates by registering the high-resolution library image to the pilot scan of the patient. Results from studying the models' 1 g SAR distribution suggest that the developed patient model can predict regions of elevated SAR within the patient with remarkable accuracy. Additionally, this work also proposes a voxel analytical metric that can assist in the construction of a patient library and the selection of the matching model from the library for a patient. It is hoped that, by developing voxel models with high accuracy in patient-specific anatomy and positioning, the proposed method can accurately predict the safety margins for high-field human applications and, therefore maximize the safe use of RF sequence power in high-field MRI systems.

An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity

Publisher: Wiley

Date: 08-08-2016

DOI: 10.1002/MRM.26360

Abstract: To improve the performance of non-Cartesian partially parallel imaging (PPI) by exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions (GRAPPA) operator for wider band lines (GROWL) is taken as a specific ex le for explanation. This work is based on the GRAPPA-like PPI having an improved performance when the to-be-reconstructed image is sparse in the image domain. A systematic scheme is proposed to artificially generate the sparse image for non-Cartesian trajectory. Using GROWL as a specific non-Cartesian PPI method, artificial sparsity-enhanced GROWL (ARTS-GROWL) is used to demonstrate the efficiency of the proposed scheme. The ARTS-GROWL consists of three steps: 1) generating synthetic k-space data corresponding to an image with smaller support, that is, artificial sparsity 2) applying GROWL to the synthetic k-space data from previous step and 3) recovering the final image from the reconstruction with the processed data. For simulation and in vivo data, the experiments demonstrate that the proposed ARTS-GROWL significantly reduces the reconstruction errors compared with the conventional GROWL technique for the tested acceleration factors. Taking ARTS-GROWL, for instance, experimental results indicate that artificial sparsity improved the signal-to-noise ratio and normalized root-mean-square error of non-Cartesian PPI. Magn Reson Med 78:271-279, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

An Improved Hybrid MoM/FDTD Technique for MRI RF Coils Modeling Using Huygen's Equivalent Surface Method

Publisher: IEEE

Date: 08-2007

DOI: 10.1109/IEMBS.2007.4353185

Electromechanical Design and Construction of a Rotating Radio-Frequency Coil System for Applications in Magnetic Resonance

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-2012

DOI: 10.1109/TBME.2012.2182993

xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks

Publisher: Wiley

Date: 27-12-2021

DOI: 10.1002/NBM.4461

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Publisher: Elsevier BV

Date: 10-2021

DOI: 10.1016/J.NEUROIMAGE.2021.118404

Electrocardiogram Reconstruction Based on Compressed Sensing

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2019

DOI: 10.1109/ACCESS.2019.2905000

Radial magnetic resonance imaging (MRI) using a rotating radiofrequency (RF) coil at 9.4 T

Publisher: Wiley

Date: 27-12-2017

DOI: 10.1002/NBM.3860

Abstract: The rotating radiofrequency coil (RRFC) has been developed recently as an alternative approach to multi-channel phased-array coils. The single-element RRFC avoids inter-channel coupling and allows a larger coil element with better B

A theoretical study for the inverse design of an ellipsoidal phased-array breast coil

Publisher: IEEE

Date: 08-2011

DOI: 10.1109/IEMBS.2011.6090099

A comparative numerical study of rotating and stationary RF coils in terms of flip angle and specific absorption rate for 7T MRI

Publisher: Elsevier BV

Date: 11-2013

DOI: 10.1016/J.JMR.2013.08.016

Abstract: While high-field magnetic resonance imaging promises improved image quality and faster scan time, it is affected by non-uniform flip angle distributions and unsafe specific absorption rate levels within the patient, as a result of the complicated radiofrequency (RF) field-tissue interactions. This numerical study explored the possibility of using a single mechanically rotating RF coil for RF shimming and specific absorption rate management applications at 7 T. In particular, this new approach (with three different RF coil element arrangements) was compared against both an 8-channel parallel coil array and a birdcage volume coil, with and without RF current optimisation. The evaluation was conducted using an in-house developed and validated finite-difference time-domain method in conjunction with a tissue-equivalent human head model. It was found that, without current optimisation, the rotating RF coil method produced a more uniform flip angle distribution and a lower maximum global and local specific absorption rate compared to the 8-channel parallel coil array and birdcage resonator. In addition, due to the large number of degrees of freedom in the form of rotated sensitivity profiles, the rotating RF coil approach exhibited good RF shimming and specific absorption rate management performance. This suggests that the proposed method can be useful in the development of techniques that address contemporary RF issues associated with high-field magnetic resonance imaging.

Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell’s Curl Equations

Publisher: MDPI AG

Date: 10-05-2020

DOI: 10.3390/APP10093318

Abstract: Magnetic Resonance-Electrical Properties Tomography (MR-EPT) is a method to reconstruct the electrical properties (EPs) of bio-tissues from the measured radiofrequency (RF) field in Magnetic Resonance Imaging (MRI). Current MR-EPT approaches reconstruct the EP profile by solving a second-order partial differential wave equation problem, which is sensitive to noise and can induce large reconstruction artefacts near tissue boundaries and areas with inaccurate field measurements. In this paper, a novel MR-EPT approach is proposed, which is based on a direct solution to Maxwell’s curl equations. The distribution of EPs is calculated by iteratively fitting the RF field calculated by the finite-difference-time-domain (FDTD) technique to the measured values. To solve the time-consuming problem of the iterative fitting, a graphics processing unit (GPU) is used to accelerate the FDTD technique to process the field calculation kernel. The new EPT method was evaluated by investigating a numerical head phantom, and it was found that EP values can be accurately calculated and were relatively insensitive to simulated RF field errors. The feasibility of the proposed method was further validated using phantom-based experimental data collected from a 9.4 Tesla (T) Magnetic Resonance Imaging (MRI) system. The results also indicated that more accurate EP values could be reconstructed from the new method compared with conventional methods. Moreover, even in the absence of phase information of the RF field, the proposed approach is still capable of offering robust EPT solutions, thus having improved practicality for potential clinical applications.

Gradient Field Deviation (GFD) Correction Using a Hybrid-Norm Approach With Wavelet Sub-Band Dependent Regularization: Implementation for Radial MRI at 9.4 T

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 09-2019

DOI: 10.1109/TBME.2019.2895091

Integrated Multi-Modal Antenna With Coupled Radiating Structures (I-MARS) for 7T pTx Body MRI

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TMI.2021.3103654

Age-related network topological difference based on the sleep ECG signal

Publisher: IOP Publishing

Date: 31-08-2018

DOI: 10.1088/1361-6579/AAD941

Abstract: Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on the sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on exploring the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful (W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages. The subjects are ided into two age groups: a younger (age ⩽ 40, n = 11) group and an older group (age > 40, n = 25). For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between the two age groups, including the mean degree ([Formula: see text]), the clustering coefficient ([Formula: see text]), and the path length ([Formula: see text]) features, such as the slope distribution of [Formula: see text] in the younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. However, the small-world index in the LS and REM stages significantly decreased with age (p = 0.0006 and p = 0.05, respectively). The comparison analysis result indicates that the network topology variations in the sleep ECG signals are prone to show age-relevant differences that could be used for sleep stage classification and sleep disorder diagnosis.

GPU-Accelerated FDTD Modeling of Radio-Frequency Field–Tissue Interactions in High-Field MRI

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2011

DOI: 10.1109/TBME.2011.2116020

A ultra high field multi-element transceive volume array for small animal MRI

Publisher: IEEE

Date: 08-2008

DOI: 10.1109/IEMBS.2008.4649592

Numerical simulations on active shielding methods comparison and wrapped angle optimization for gradient coil design in MRI with enhanced shielding effect

Publisher: AIP Publishing

Date: 05-2018

DOI: 10.1063/1.5020087

Abstract: The switching of a gradient coil current in magnetic resonance imaging will induce an eddy current in the surrounding conducting structures while the secondary magnetic field produced by the eddy current is harmful for the imaging. To minimize the eddy current effects, the stray field shielding in the gradient coil design is usually realized by minimizing the magnetic fields on the cryostat surface or the secondary magnetic fields over the imaging region. In this work, we explicitly compared these two active shielding design methods. Both the stray field and eddy current on the cryostat inner surface were quantitatively discussed by setting the stray field constraint with an ultra-low maximum intensity of 2 G and setting the secondary field constraint with an extreme small shielding ratio of 0.000 001. The investigation revealed that the secondary magnetic field control strategy can produce coils with a better performance. However, the former (minimizing the magnetic fields) is preferable when designing a gradient coil with an ultra-low eddy current that can also strictly control the stray field leakage at the edge of the cryostat inner surface. A wrapped-edge gradient coil design scheme was then optimized for a more effective control of the stray fields. The numerical simulation on the wrapped-edge coil design shows that the optimized wrapping angles for the x and z coils in terms of our coil dimensions are 40° and 90°, respectively.

Integrating model- and data-driven methods for synchronous adaptive multi-band image fusion

Publisher: Elsevier BV

Date: 02-2020

DOI: 10.1016/J.INFFUS.2019.07.009

Zhejiang University

Location: China

View Organisation

Discovery Projects - Grant ID: DP180101257

Start Date: 06-2018

End Date: 06-2022

Amount: $462,080.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP130100202

Start Date: 2013

End Date: 12-2017

Amount: $780,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP120200375

Start Date: 07-2012

End Date: 06-2017

Amount: $255,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP160103260

Start Date: 2016

End Date: 12-2020

Amount: $516,800.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0776463

Start Date: 09-2008

End Date: 12-2012

Amount: $620,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP140100408

Start Date: 2014

End Date: 12-2017

Amount: $320,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP1095902

Start Date: 2010

End Date: 12-2014

Amount: $450,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0558134

Start Date: 2005

End Date: 09-2008

Amount: $398,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP230101628

Start Date: 10-2023

End Date: 10-2026

Amount: $512,607.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE230100045

Start Date: 07-2023

End Date: 08-2024

Amount: $586,779.00

Funder: Australian Research Council