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MRI phase image unwrapping using DCT-based modified weighted least square algorithm.

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Title: MRI phase image unwrapping using DCT-based modified weighted least square algorithm.
Authors: Verma, Shradha1 (AUTHOR) shradha_rs@ece.nits.ac.in, Goel, Tripti1 (AUTHOR) triptigoel@ece.nits.ac.in
Source: Sādhanā: Academy Proceedings in Engineering Sciences. Jun2026, Vol. 51 Issue 2, p1-11. 11p.
Subjects: Phase unwrapping (Digital image processing), Discrete cosine transforms, Image denoising, Least squares, Signal-to-noise ratio, Neurodegeneration
Abstract: 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, Parkinson, and others. However, directly extracted phase images from MRI exhibit the wrapped phase values within the ± π radian range. To circumvent these phase jumps or discontinuities, phase unwrapping is required. Path-following and minimum-norm algorithms are unwrapping methods for retrieving 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 unwrapped phase data. This paper presents a discrete cosine transform (DCT)-based modified minimum norm-based weighted least square (WLS) phase unwrapping to improve the visibility and noise immunity of phase images. The proposed algorithm suppresses high-frequency residual noise by imposing spectral truncation of the high-frequency coefficient. For the experimental validation of the proposed method, performance is compared in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). The proposed phase unwrapping method outperforms state-of-the-art techniques. [ABSTRACT FROM AUTHOR]
Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Jun2026, Vol. 51 Issue 2, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Phase+unwrapping+%28Digital+image+processing%29%22">Phase unwrapping (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+cosine+transforms%22">Discrete cosine transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Neurodegeneration%22">Neurodegeneration</searchLink>
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  Data: 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, Parkinson, and others. However, directly extracted phase images from MRI exhibit the wrapped phase values within the ± π radian range. To circumvent these phase jumps or discontinuities, phase unwrapping is required. Path-following and minimum-norm algorithms are unwrapping methods for retrieving 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 unwrapped phase data. This paper presents a discrete cosine transform (DCT)-based modified minimum norm-based weighted least square (WLS) phase unwrapping to improve the visibility and noise immunity of phase images. The proposed algorithm suppresses high-frequency residual noise by imposing spectral truncation of the high-frequency coefficient. For the experimental validation of the proposed method, performance is compared in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). The proposed phase unwrapping method outperforms state-of-the-art techniques. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s12046-026-03072-1
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      – Code: eng
        Text: English
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      – SubjectFull: Phase unwrapping (Digital image processing)
        Type: general
      – SubjectFull: Discrete cosine transforms
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      – SubjectFull: Image denoising
        Type: general
      – SubjectFull: Least squares
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      – SubjectFull: Signal-to-noise ratio
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      – SubjectFull: Neurodegeneration
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      – TitleFull: MRI phase image unwrapping using DCT-based modified weighted least square algorithm.
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            NameFull: Verma, Shradha
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            NameFull: Goel, Tripti
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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