Receiver Coil

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Hajime Sakuma - One of the best experts on this subject based on the ideXlab platform.

Andreas Sigfridsson - One of the best experts on this subject based on the ideXlab platform.

Wan Choi - One of the best experts on this subject based on the ideXlab platform.

  • transmitter current control and Receiver Coil selection in magnetic mimo power transfer systems
    IEEE Wireless Communications Letters, 2020
    Co-Authors: Sangjun Park, Wan Choi
    Abstract:

    In this letter, we jointly optimize transmitter source currents and receive Coil selection in a magnetic multiple input multiple output wireless power transfer system to minimize the total transmit power, while satisfying a required level of the charged power and the limited total transmit power. The optimal transmitter source currents are derived for an arbitrary number of receive Coils. Based on our analysis, we propose a method to find the optimal subset of the receive Coils. Our simulation results show that the proposed scheme requires the lowest transmit power to meet the constraint on the charged power.

Chi K. Tse - One of the best experts on this subject based on the ideXlab platform.

  • Power converter with novel transformer structure for wireless power transfer using a DD2Q power Receiver Coil set
    2016 IEEE Energy Conversion Congress and Exposition (ECCE), 2016
    Co-Authors: Ke Guangjie, Qianhong Chen, Gao Wei, Siu-chung Wong, Chi K. Tse
    Abstract:

    In an Inductive Power Transfer (IPT) system, the magnetic design of the primary and secondary pads is an important factor that determines the power transfer capability. This paper proposes a DD2Q power Receiver Coil set, a novel three Coil magnetic pad that comprises a classical DD winding and two additional quadrature windings placed at the secondary side (referred to as 2Q). The DD2Q Coil set achieves tolerance to larger lateral displacements and rotational displacements than conventional DD pads. A 100 W contactless energy transmission system using the proposed transformer is tested.

Hammad Omer - One of the best experts on this subject based on the ideXlab platform.

  • Transfer learning in deep neural network-based Receiver Coil sensitivity map estimation
    Magnetic Resonance Materials in Physics Biology and Medicine, 2021
    Co-Authors: Madiha Arshad, Mahmood Qureshi, Omair Inam, Hammad Omer
    Abstract:

    Introduction The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the Receiver Coil sensitivity maps. Deep learning-based Receiver Coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming. Materials and methods A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based Receiver Coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T Receiver Coil sensitivity maps) are thoroughly assessed for 3T Receiver Coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T Receiver Coil sensitivity maps. Result and conclusion Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the Receiver Coil sensitivity maps estimated by the proposed method.

  • Transfer learning in deep neural network-based Receiver Coil sensitivity map estimation.
    Magma (New York N.Y.), 2021
    Co-Authors: Madiha Arshad, Mahmood Qureshi, Omair Inam, Hammad Omer
    Abstract:

    The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the Receiver Coil sensitivity maps. Deep learning-based Receiver Coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming. A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based Receiver Coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T Receiver Coil sensitivity maps) are thoroughly assessed for 3T Receiver Coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T Receiver Coil sensitivity maps. Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the Receiver Coil sensitivity maps estimated by the proposed method.

  • Sensitivity Maps Estimation Using Eigenvalues in Sense Reconstruction
    Applied Magnetic Resonance, 2016
    Co-Authors: Amna Shafa Irfan, Ayisha Nisar, Hassan Shahzad, Hammad Omer
    Abstract:

    Magnetic resonance imaging (MRI) is a non-ionizing and non-invasive imaging modality. One major limitation of MRI is its long data acquisition time. Parallel magnetic resonance imaging (PMRl) has the potential to decrease the MRI scan time by acquiring fewer k -space lines while using numerous independent Receiver Coils for data acquisition. SENSE reconstruction is one of the PMRI algorithms most widely used in commercial MRI scanners these days. SENSE needs accurate estimates of the Receiver Coil sensitivity profiles to reconstruct fully sampled images from the acquired undersampled data. This paper presents a comparison between two methods of estimating Receiver Coil sensitivities: (1) eigenvalue approach, in which a series of eigenvalue decompositions at the center of the acquired k -space are performed; (2) pre-scan method which uses a low-resolution image to estimate Receiver Coil sensitivities. In this paper, SENSE reconstruction is performed with Receiver Coil sensitivities estimated using both the methods. The quality of the reconstructed image is evaluated using artifact power, mean signal-to-noise ratio and line profile. The results show that the eigenvalue method to estimate sensitivity maps can be used as an alternate method for Receiver Coil sensitivity estimation, as it provides good reconstruction results without any compromise on the artifact power, mean signal-to-noise ratio and the line profile of the reconstructed image. Moreover, it does not require a pre-scan image to estimate Receiver Coil sensitivities which is required in the pre-scan method.

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