Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review

This work investigates the general problem of phase unwrapping for arbitrary N-dimensional phase maps. A cost function-based approach is outlined that leads to an integer programming problem. To solve this problem, a best-pair-first region merging approach is adopted as the optimization method. The algorithm was implemented and tested with 3D MRI medical data for venogram studies, as well as for fMRI applications in EPI unwarping and rapid, automated shimming.

Fast, automated, N‐dimensional phase‐unwrapping algorithm

In 1984, Dixon published a first paper on a simple spectroscopic imaging technique for water and fat separation. The technique acquires two separate images with a modified spin echo pulse sequence. One is a conventional spin echo image with water and fat signals in-phase and the other is acquired with the readout gradient slightly shifted so that the water and fat signals are 180 degrees out-of-phase. Dixon showed that from these two images, a water-only image and a fat-only image can be generated. The water-only image by the Dixon's technique can serve the purpose of fat suppression, an important and widely used imaging option for clinical MRI. Additionally, the availability of both the water-only and fat-only images allows direct image-based water and fat quantitation. These applications, as well as the potential that the technique can be made highly insensitive to magnetic field inhomogeneity, have generated substantial research interests and efforts from many investigators. As a result, significant improvement to the original technique has been made in the last 2 decades. The following article reviews the underlying physical principles and describes some major technical aspects in the development of these Dixon techniques.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.21492

Dixon techniques for water and fat imaging

Synthetic aperture radar (SAR) interferometry (InSAR) is primarily used in remote-sensing applications and has created a new class of radar data that has significantly evolved during the last couple of decades. Most of the InSAR applications (e.g., topographic mapping and deformation monitoring) typically use a technique called phase unwrapping (PU). In this article, we present an overview of PU techniques in InSAR signal processing. First, we review the established single-baseline (SB) PU methods and then describe innovative PU techniques and concepts related to multibaseline (MB) PU and large-scale (LS) PU. In addition, we discuss several numerical processing examples of these PU techniques. It is our hope that this review will provide guidelines to future researchers to enhance further PU algorithmic developments.

Phase Unwrapping in InSAR : A Review

Magnetic susceptibility is an important physical property of tissues, and can be used as a contrast mechanism in magnetic resonance imaging (MRI). Recently, targeting contrast agents by conjugation with signaling molecules and labeling stem cells with contrast agents have become feasible. These contrast agents are strongly paramagnetic, and the ability to quantify magnetic susceptibility could allow accurate measurement of signaling and cell localization. Presented here is a technique to estimate arbitrary magnetic susceptibility distributions by solving an ill-posed inversion problem from field maps obtained in an MRI scanner. Two regularization strategies are considered: conventional Tikhonov regularization and a sparsity promoting nonlinear regularization using the l 1 norm. Proof of concept is demonstrated using numerical simulations, phantoms, and in a stroke model in a rat. Initial experience indicates that the nonlinear regularization better suppresses noise and streaking artifacts common in susceptibility estimation.

Nonlinear Regularization for Per Voxel Estimation of Magnetic Susceptibility Distributions From MRI Field Maps

This paper describes phase maps. A review of the phase unwrapping problem is given. Different structures, in particular fringelines, cutlines, and poles, contained within a phase map are described and their origin and behavior investigated. The problem of phase unwrapping can then be addressed with a better understanding of the source of poles or inconsistencies. This understanding, along with some assumptions about what is being encoded in the phase of a magnetic resonance image, are used to derive a new method for phase unwrapping which relies only on the phase map. The method detects cutlines and distinguishes between noise-induced poles and signal undersampling poles based on the length of the fringelines. The method was shown to be robust to noise and successful in unwrapping challenging clinical cases.

Understanding phase maps in MRI: a new cutline phase unwrapping method

A new implementation of the minimum spanning tree (MST) phase unwrapping method is presented. The time complexity of the MST method is reduced from O(n/sup 2/) to O(n log/sub 2/ n), where n is the number of pixels in the phase map. Typical 256/spl times/256 phase maps from magnetic resonance imaging can be unwrapped in seconds, compared with tens of minutes with the O(n/sup 2/) implementation. This makes the pixel-level MST method time efficient and practically attractive.

A fast implementation of the minimum spanning tree method for phase unwrapping

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Understanding phase maps in MRI: a new cutline phase unwrapping method

A fast implementation of the minimum spanning tree method for phase unwrapping