Object: The objective of this work is to propose an imaging sequence based upon the wavelet encoding approach to provide MRI images free from folding artifacts, in the small field of view (FOV) regime, such as dynamic magnetic resonance imaging (MRI) studies.
Materials And Methods: The method consists of using a 2D spatially selective RF excitation pulse inserted into a gradient- echo pulse sequence to excite spins within a determined plane where wavelet encoding is achieved in one direction and slice selection is performed in the second direction. Wavelet encoding allows for spatially localized excitation and consequently restricts the spins excited within a reduced FOV. It consists of varying, according to a predetermined scheme, the width and position of the profile of the so-called fast RF pulse of the 2D RF excitation pulse, to obey wavelet encoding translation and dilation conditions. This sequence is implemented on a 3 Tesla whole body Siemens scanner.
Results: Compared to Fourier encoding, the proposed technique tested on phantoms with different shapes and structures, is able to provide gradient-echo reduced FOV images free from aliased signals.
Conclusion: Wavelet encoding is suitable for small FOV imaging in dynamic MRI studies.
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http://dx.doi.org/10.1007/s10334-009-0193-z | DOI Listing |
Med Phys
December 2024
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Assam, India.
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School of Electrical and Electronic Information, Xihua University, Chengdu, 610039, Sichuan, China.
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High dynamic range (HDR) video offers a more realistic visual experience than standard dynamic range (SDR) video, while introducing new challenges to both compression and transmission. Rate control is an effective technology to overcome these challenges, and ensure optimal HDR video delivery. However, the rate control algorithm in the latest video coding standard, versatile video coding (VVC), is tailored to SDR videos, and does not produce well coding results when encoding HDR videos.
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Semantic segmentation of electron microscopy (EM) images is crucial for nanoscale analysis. With the development of deep neural networks (DNNs), semantic segmentation of EM images has achieved remarkable success. However, current EM image segmentation models are usually extensions or adaptations of natural or biomedical models.
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