• Title/Summary/Keyword: Magnetic gradient

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Minimizing MR Gradient Artefacts on ECG Signals for Cardiac Gating based on an Adaptive Digital Filter (적응 디지털 필터 기반의 MRI Cardiac Gating을 위한 심전도 신호의 MR Gradient 잡음 최소화 방법)

  • Park, Ho-Dong;Jang, Bong-Ryeol;Lee, Kyoung-Joung
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.817-818
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    • 2006

  • In Magnetic Resonance Imaging(MRI), the QRS complex of ECG is used as a trigger signal for MRI scan. But, gradient and RF(radio frequency) artifacts which are caused to static and dynamic field in MRI scanner cause interference in the ECG. Also, the signal shape of theses artifacts can be similar to the QRS-complex, causing possible misinterpretation during patient monitoring and false gating of the MRI. In case of using general FIR or IIR band-pass filters for minimizing the artifacts, artifact-reduction-ratio is not excellent. So, an adaptive real-time digital filter is proposed for reduction of noise by gradient and RF(radio frequency) artifacts. The proposed filter for MRI-Gating is based on the noise-canceller with NLMS(Normalized Least Mean Square) algorithm. The reference signals of the adaptive noise canceller are a combination of the noisy three channel ECG signals. In conclusions, the proposed method showed the acceptable quality of ECG signal with sufficient SNR for gating the MRI and possibility of real time implementation.

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MRI Image Super Resolution through Filter Learning Based on Surrounding Gradient Information in 3D Space (3D 공간상에서의 주변 기울기 정보를 기반에 둔 필터 학습을 통한 MRI 영상 초해상화)

  • Park, Seongsu;Kim, Yunsoo;Gahm, Jin Kyu
    • Journal of Korea Multimedia Society
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    • v.24 no.2
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    • pp.178-185
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    • 2021

  • Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

  • https://doi.org/10.9717/kmms.2020.24.2.178 인용 PDF KSCI HTML