Quality-guided Phase Unwrapping Research Articles

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network

This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Encoding with Stimulated Echoes cardiac image phases acquired from 30 breast cancer patients and 30 healthy females were unwrapped via a DeepLabV3 + fully convolutional network (FCN). Myocardial strains were directly computed from the unwrapped phases with the Radial Point Interpolation Method. FCN-unwrapped phases of a phantom’s rotating gel were validated against quality-guided phase-unwrapping (QGPU) and robust transport of intensity equation (RTIE) phase-unwrapping. FCN performance on unwrapping human LV data was measured with F1 and Dice scores versus QGPU ground-truth. The reliability of FCN-based strains was assessed against RTIE-based strains with Cronbach's alpha (C-α) intraclass correlation coefficient. Mean squared error (MSE) of unwrapping the phantom experiment data at 0 dB signal-to-noise ratio were 1.6, 2.7 and 6.1 with FCN, QGPU and RTIE techniques. Human data classification accuracies were F1 = 0.95 (Dice = 0.96) with FCN and F1 = 0.94 (Dice = 0.95) with RTIE. GLS results from FCN and RTIE were −16 ± 3% vs. −16 ± 3% (C-α = 0.9) for patients and −20 ± 3% vs. −20 ± 3% (C-α = 0.9) for healthy subjects. The low MSE from the phantom validation demonstrates accuracy of phase-unwrapping with the FCN and comparable human subject results versus RTIE demonstrate GLS analysis accuracy. A deep-learning methodology for phase-unwrapping in medical images and GLS computation was developed and validated in a heterogeneous cohort.

Abstract P345: A MRI Displacement-based Deep-learning Semantic Segmentation Tool For Left-ventricular Longitudinal Strain Analysis In Cardiotoxicity

Global longitudinal strain (GLS) computed in the left-ventricle (LV) is an established metric for detecting cardiotoxicity in breast cancer patients treated with antineoplastic agents. The purpose of this study was to develop a novel, MRI-based, deep-learning semantic segmentation tool that automates the phase-unwrapping for LV displacement computation in GLS. Strain analysis via phase-unwrapping was conducted on 30 breast cancer patients and 30 healthy females acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence. A ResNet-50 deep convolutional neural network (DCNN) architecture for automated phase-unwrapping, a previously validated ResNet-50 DCNN for chamber quantification and the Radial Point Interpolation Method were used for GLS computation (Figure 1). The DCNN's performance was measured with F1 and Dice scores, and validated in comparison to the robust transport of intensity equation (RTIE) and quality guided phase-unwrapping (QGPU) conventional algorithms. The three techniques were compared by intraclass correlation coefficient with Cronbach’s alpha (C-alpha) index. Classification accuracy with the DCNN was F1 score of 0.92 and Dice score of 0.89. The GLS results from RTIE, QGPU and DCNN were -16.0 ± 2%, -16.1 ± 3% and -15.9 ± 3% (C-alpha = 0.89) for patients and -18.9 ± 3%, -19.0 ± 4% and -18.9 ± 3% (C-alpha = 0.92) for healthy subjects. Comparable validation results from the three techniques show the feasibility of a DCNN-based approach to LV displacement and GLS analysis. The dissimilarities between patients and healthy subjects demonstrate that DCNN-based GLS computation may detect LV abnormalities related to cardiotoxicity.

Accuracy improvement of the inSAR quality-guided phase unwrapping based on a modified PDV map.

In this paper, an accuracy improvement of the quality-guided phase unwrapping algorithm is proposed. Our proposal is based on a modified phase derivative variance which provides more details on local variations especially for important patterns such as fringes and edges, hence distorted regions may be re-unwrapped according to this new reliable PDV. The proposed improvement is not only effective on accuracy but also on time, the obtained results have shown that the running time with our proposal is less than that of a skillful optimization-based algorithm. To prove effectiveness, the experimental test is carried out on simulated and real data, and the comparison is made under several relevant criteria.

Experimental evaluation of the strain intensity factor at the inclusion tip using digital photoelasticity

Digital photoelasticity is used for the experimental evaluation of the strain intensity factor for a rigid line inclusion embedded in an elastic matrix. The multi-parameter stress field equations are derived using the Airy’s stress function approach. The ten-step phase shifting technique (PST) is used to obtain the whole field isochromatic and isoclinic data, and the adaptive quality-guided phase unwrapping (AQGPU) algorithm is used for unwrapping the phase map. An over-deterministic linear least squares approach is used to solve the multi-parameter stress field equations in an optimization framework to obtain the inclusion tip parameters and the actual inclusion tip location. The relations for strain intensity factor and stress intensity factor (SIF) with the parameters is presented. Photoelastic experiments are performed on two inclusion configurations where inclusion is kept along and inclined to the direction of loading. The obtained strain intensity factor is compared with the analytical solution, and a good match is observed.

Fast single fringe-pattern processing with graphics processing unit

Optical interferometric techniques provide noncontact, full-field, and high-precision measurements that are very attractive in various research and application fields. Single fringe-pattern processing (SFPP) is often required when measuring fast phenomena, which contain multiple steps including noise removal, phase demodulation, and unwrapping. However, several difficulties are encountered during SFPP, among which the processing time is of interest due to the increasing computational load brought by the large amount and high-resolution fringe patterns in recent years. In this paper, we propose a general and complete graphics processing unit (GPU)-based SFPP framework to perform a systematic discussion on SFPP acceleration. Typical methods from the spatial domain, the transform-based, and the path-related are chosen to have a variety of methods in the framework for better parallelization demonstration, namely, coherence-enhancing diffusion for denoising, spiral phase quadrature transform for demodulation, and quality-guided phase unwrapping. To the best of our knowledge, this is the first time a complete GPU-based framework has been proposed for SFPP. The advantages of performing the analysis and parallelization in framework level are demonstrated, where processing redundancy can be identified and reduced. The proposed framework can be used as an example to demonstrate the GPU-based parallelization in SFPP. Methods in the framework can be replaced but the framework level analysis, the parallel design, and the involved functions are always good references. Experiments are performed on simulated and experimental fringe patterns to demonstrate the effectiveness of the proposed work and achieve at most 29.8 times speedup compared with CPU-based sequential processing.

Snake-assisted quality-guided phase unwrapping for discontinuous phase fields.

Quality-guided phase unwrapping (QGPU) has been proven as an effective phase unwrapping method. As the QGPU assumes that the true phase map is continuous, it suffers from the discontinuity problem. To solve this problem, the windowed Fourier transform (WFT) based methods have been adopted to obtain effective quality measures. Nevertheless, in some special cases, using the quality maps from the WFT-based methods could not lead to a correct result. In this paper, we propose a method that incorporates the GVF snake model to assist the unwrapping process. This new method achieves better results than using the WFT-based methods directly in some discontinuous examples.

Quality-guided phase unwrapping implementation: an improved indexed interwoven linked list.

Quality-guided phase unwrapping (QGPU) is a widely used technique, and an adjoin list plays a very important role in the QGPU process. Indexed interwoven linked list (I2L2) is a data structure for implementing the adjoin list. In this paper, we propose three improvements on the I2L2. The first improvement is resumed searching, which records the highest nonempty level in the I2L2 and reduces the computational redundancy; the second is an adaptive mapping between the quality values and the I2L2 levels, which reduces the effect of concentrated quality value distribution. Last, I2L2-H, a new variant of the I2L2 combining the advantages of both the I2L2 and heap, is developed. With these three improvements, the improved I2L2 is over 6 times faster than the original one in the best cases, and it can process large phase maps in almost real time.

AN IMPROVED QUALITY GUIDED PHASE UNWRAPPING METHOD AND ITS APPLICATIONS TO MRI

An improved method of quality guided phase unwrapping (QGPU) is proposed in this work. It extracts the quality map via a median flltered phase derivative variance (MFPDV) that applies a two-dimensional median fllter on the phase derivative variance (PDV) map, in order to reduce the efiect of noise in the background area. In addition, we employed the Indexed Interwoven Linked List (I2L2) structure to store the orderly adjoin list more e-ciently and the Two Section Guided Strategy (TSGS) to reduce comparison frequency. The experiments demonstrate that the normalized L1 norm of MFPDV of a brain MR image is only 0.0827, less than that of PDV method at 0.0923. Besides, the computation time of QGPU with I2L2 technique is only 30% of that with sequence structure, and the computation time of QGPU with TSGS is only 65% of that without TSGS. In total, the proposed MFPDV upwrap phase images better than conventional PDV map, and I2L2 and TSGS are e-cient strategies to reduce computation time.

Weighted-phase-gradient-based quality maps for two-dimensional quality-guided phase unwrapping

The two-dimensional phase unwrapping plays an important role in phase measurement applications. This paper proposes a simple but effective method to construct reliable quality maps for two-dimensional quality-guided phase unwrapping. A new group of reliable quality maps based on weighted-phase-gradient is proposed to overcome the shortcomings of traditional phase-gradient quality maps. When a quality map of a wrapped phase image is generated, the weighted gradient information of neighborhood pixels of the wrapped phase image is used to determine the quality of the current pixel. Thus the quality of the wrapped phase data can be accurately reflected in the resulting quality map. In order to evaluate the reliability, effectiveness and advantages of the proposed quality maps, a new performance evaluation model for quality maps is also proposed. Unwrapped results demonstrate that the weighted-phase-gradient-based quality maps are excellent indicators of the phase quality and can be used to retrieve a reliable phase profile in quality-guided phase unwrapping process.

Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies

Quality-guided phase unwrapping is a widely used technique with different quality definitions and guiding strategies reported. It is thus necessary to do a detailed comparison of these approaches to choose the optimal quality map and guiding strategy. For quality maps, in the presence of noise, transform-based methods are found to be the best choice. However in the presence of discontinuities, phase unwrapping is itself unresolved and hence quality-guided phase unwrapping is not sufficient. For guiding strategies, classical, two-section, and stack-chain guiding strategies are chosen for comparison. If accuracy is the foremost criterion then the classical guiding strategy with a data structure of indexed interwoven linked list is best. If speed is of essence then the stack-chain guiding strategy is the one to use.

Corrections to “An Improved Quality-Guided Phase-Unwrapping Algorithm Based on Priority Queue” [Mar 11 364-368

In the above letter (ibid., vol. 8, no. 2, pp. 364-368, Mar. 2011), in the Abstract, a sentence is incorrect. The correct sentence is presented here.