Phase Unwrapping Using Energy Minimization Methods for MRI Phase Image

Phase Unwrapping (PU) is reconstruction of absolute phase data from its wrapped phase. The absolute phase cannot be extracted from the wrapped phase data directly. Without phase noise, singularity, and aliasing problems, the phase information can be unwrapped easily. However, the phase data are always contaminated by noise and discontinuities, making the PU process more complicated. Therefore, a suitable PU algorithm is required to address the problems properly. In this method, the energy difference between neighborhood pixels in level 3 is counted, followed by getting the probability value to obtain its total fringes. The capability of the proposed method to unwrap simulated and actual MRI phase images is also demonstrated. In actual MRI phase image, PU can be implemented for water and fat separation.

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Comparative study of phase unwrapping by energy minimization methods: First and third level pixels configuration

International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009 ◽

10.1109/icici-bme.2009.5417271 ◽

2009 ◽

Author(s):

Kusworo Adi ◽

Tati L. R. Mengko ◽

Andriyan B. Suksmono ◽

H. Gunawan

Keyword(s):

Comparative Study ◽

Energy Minimization ◽

Phase Unwrapping ◽

Minimization Methods

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N-Dimensional Phase Unwrapping

The Insight Journal ◽

10.54294/9dmwyf ◽

2015 ◽

Author(s):

Davis Vigneault ◽

Wen-Tung Wang ◽

Michael Tee ◽

David Bluemke ◽

Alison Noble

Keyword(s):

Magnetic Resonance Imaging ◽

Image Analysis ◽

Magnetic Resonance ◽

Phase Unwrapping ◽

Resonance Imaging ◽

Phase Information ◽

Phase Data ◽

Magnetic Resonance Imaging Mri ◽

Medical Disciplines

Although phase data can take on any value, it is generally only possible to measure phase as a principle value, i.e., wrapped within the range (-pi,pi]. Determining the unwrapped phase from its principle value is a topic of considerable interest in magnetic resonance imaging (MRI), as well as many non-medical disciplines. Despite their importance in image analysis, filters for manipulating phase information have not been incorporated into ITK. This article introduces the ITKPhase module, containing filters useful for understanding, analyzing, and unwrapping n-dimensional phase data, and also serves as a practical introduction to phase unwrapping.

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Electron holography of catalysts

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100138452 ◽

1995 ◽

Vol 53 ◽

pp. 416-417

Author(s):

A. K. Datye ◽

D. S. Kalakkad ◽

L. F. Allard ◽

E. Völkl

Keyword(s):

Heterogeneous Catalysts ◽

High Surface ◽

High Surface Area ◽

Atomic Scale ◽

Nano Particles ◽

Phase Image ◽

Electron Holography ◽

Specimen Thickness ◽

Phase Information ◽

Particle Structure

The active phase in heterogeneous catalysts consists of nanometer-sized metal or oxide particles dispersed within the tortuous pore structure of a high surface area matrix. Such catalysts are extensively used for controlling emissions from automobile exhausts or in industrial processes such as the refining of crude oil to produce gasoline. The morphology of these nano-particles is of great interest to catalytic chemists since it affects the activity and selectivity for a class of reactions known as structure-sensitive reactions. In this paper, we describe some of the challenges in the study of heterogeneous catalysts, and provide examples of how electron holography can help in extracting details of particle structure and morphology on an atomic scale.Conventional high-resolution TEM imaging methods permit the image intensity to be recorded, but the phase information in the complex image wave is lost. However, it is the phase information which is sensitive at the atomic scale to changes in specimen thickness and composition, and thus analysis of the phase image can yield important information on morphological details at the nanometer level.

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An Absolute Phase Estimation Method for Interferometry Imagery

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2019.1.2.4 ◽

2019 ◽

Vol 1 (2) ◽

pp. 14-19

Author(s):

Sui Ping Lee ◽

Yee Kit Chan ◽

Tien Sze Lim

Keyword(s):

Noise Suppression ◽

Estimation Method ◽

Phase Image ◽

Phase Estimation ◽

Phase Reconstruction ◽

Estimation Algorithms ◽

Absolute Phase ◽

Filtering Rate ◽

Interferometric Phase ◽

Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise

Photonics ◽

10.3390/photonics8070255 ◽

2021 ◽

Vol 8 (7) ◽

pp. 255

Author(s):

Marie Tahon ◽

Silvio Montresor ◽

Pascal Picart

Keyword(s):

Phase Error ◽

Speckle Noise ◽

Experimental Conditions ◽

Benchmark Data ◽

Wide Range ◽

Phase Images ◽

Different Depths ◽

Phase Data ◽

Do So

Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In this paper, de-noising is addressed with phase images corrupted with speckle noise. To do so, DnCNN residual networks with different depths were built and trained with various holographic noisy phase data. The possibility of using a network pre-trained on natural images with Gaussian noise is also investigated. All models are evaluated in terms of phase error with HOLODEEP benchmark data and with three unseen images corresponding to different experimental conditions. The best results are obtained using a network with only four convolutional blocks and trained with a wide range of noisy phase patterns.

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Phase Image Segmentation and Filtering Algorithm Based on Direction of the Gradient Factor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.670-671.1488 ◽

2014 ◽

Vol 670-671 ◽

pp. 1488-1492

Author(s):

Chi Zhang ◽

Zhong Wei Li ◽

Yu Sheng Shi

Keyword(s):

Image Segmentation ◽

Median Filter ◽

Phase Image ◽

Point Matching ◽

Performance Specifications ◽

Noise Data ◽

Image Edge ◽

Phase Data ◽

Effective Phase ◽

Phase Shift Method

To satisfy rigid performance specifications of structure light measurement by phase shift method, the algorithm of phase image segmentation and filter based on direction of the gradient factor was introduced in this paper. In this method, phase image was divided into three parts by condition of extreme value and direction of the gradient factor, including continuous area, edge area and regional noise. Only phase data in continuous area was processed by median filter. This method can reduce the point matching computation of 3-D reconstruction, and at the same time can protect the image edge details, so as to reduce the noise data, provide accurate and effective phase data for reconstruction. Results of segmentation verify feasibility and effectiveness of presented method.

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