Discrete Cosine Transform based Laplacian Phase Unwrapping for Phase Image application

Verma, Shradha; Goel, Tripti; Murugan, R.

doi:10.1109/indicon56171.2022.10039692

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Table 1 and Table 2 evidenced that modified methodology outperformed other methods for the first and second brain scans, respectively. [30] 45.66 0.8303 6.7232 DCT [19] 45.32 0.8302 6.8642 QGPU [31] 45.55 0.2032 11.2700 LPU [32] 46.01 0.2128 10.7840 Proposed Algorithm 46.05 0.8319 6.2558…”

Section: Comparison Of Phase Unwrapping Using Quality Matricesmentioning

confidence: 99%

MRI Phase Image Unwrapping using DCT Based Modified Weighted Least Square Algorithm

Verma,

GOEL,

Tanveer

et al. 2023

Preprint

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Background: Phase images of magnetic resonance imaging (MRI) have applications in many fields, including the medical domain. It is often employed to identify biomarkers of neurodegenerative diseases such as Alzheimer's, Parkinson's, and others. However, directly extracted phase images from MRI exhibit the wrapped phase values within the ±π radian range. Methods: To circumvent these phase jumps or discontinuity, phase unwrapping is required. Path-following and minimum norms algorithms are unwrapping methods to retrieve the original unwrapped phase image. The path-following algorithm extracts the original phase value by considering the adjacent pixels along the integral path. In contrast, the minimum norms algorithm aims to minimize the difference between the partial derivatives of the wrapped and the unwrapped phase data. This paper presents the DCT-based modified minimum norm-based weighted least square (LS) phase unwrapping to improve the visibility and noise immunity of the phase images. The proposed algorithm eradicates residual noise by imposing spectral truncation of the low-frequency coefficient. Results and Conclusions: For the experimental validation of the proposed algorithm, the wrapped and unwrapped image phase profiles are demonstrated to show the effectiveness of the proposed phase unwrapping algorithm. In addition, the performance metrics, peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and root mean squared error (RMSE) are calculated to show the comparison of the proposed phase unwrapping algorithm with the state-of-the-art techniques.

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Section: Comparison Of Phase Unwrapping Using Quality Matricesmentioning

confidence: 99%

MRI Phase Image Unwrapping using DCT Based Modified Weighted Least Square Algorithm

Verma,

GOEL,

Tanveer

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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The effect of fat model variation on muscle fat fraction quantification in a cross‐sectional cohort

Froeling,

Heskamp

2024

NMR in Biomedicine

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Spectroscopic imaging, rooted in Dixon's two‐echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low‐fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left–right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left–right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset.

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Discrete Cosine Transform based Laplacian Phase Unwrapping for Phase Image application

Cited by 2 publications

References 20 publications

MRI Phase Image Unwrapping using DCT Based Modified Weighted Least Square Algorithm

MRI Phase Image Unwrapping using DCT Based Modified Weighted Least Square Algorithm

The effect of fat model variation on muscle fat fraction quantification in a cross‐sectional cohort

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