A Review on Image Reconstruction through MRI k-Space Data

Jhamb, Tanuj Kumar; Rejathalal, Vinith; Govindan, V. K.

doi:10.5815/ijigsp.2015.07.06

Cited by 18 publications

(6 citation statements)

References 63 publications

(48 reference statements)

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“…The sampling strategy may vary depending on the acquisition trajectory [18]. However, regardless of its trajectory, sampling requires a methodology for image reconstruction, which is based on interpolation paradigms [19]. K-space interpolation methods have been developed to restore corrupted data points acquired with MRI, [20] and more generally to enforce MR image reconstruction [21].…”

Section: Mri K-space Acquisition and Image Space Reconstructionmentioning

confidence: 99%

Sampling technique for Fourier convolution theorem based k‐space filtering

et al. 2023

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This research presents a sampling technique for Fourier convolution theorem (FCT) based k-space filtering. One polynomial function and three transfer functions were selected: (1) Gaussian, (2) Bessel, (3) Butterworth, and (4) Chebyshev. The functions were sampled on the image grid, and they are called filtering functions. Each filtering function was multiplied by the Sinc function to obtain the "Sinc-shaped convolving function." The k-space of the Sinc-shaped convolving function is calculated by direct Fourier transform (FT) and it is featured by a central region. This central region of the k-space is rectangular in its shape because it is consequential to the direct FT of the product between the filtering function and the Sinc function. Low-pass and highpass filtering is obtained by inverse FT of the pointwise multiplication between the k-space of the departing image and the k-space of the Sinc-shaped convolving function. A variety of cut-off frequencies, bandwidth, sampling rates, and numbers of poles of the filters were verified as effective to filter the images. Filtering strength can be modified by fine-tuning the size of the central rectangular k-space region of the Sinc-shaped convolving functions. K-space analysis of departing images and filtered images provide additional evidence of effective filtering. Moreover, k-space filtering was compared to Z-space filtering using the extension of the FCT to Zspace. The novelty of this research is the sampling technique used to determine the Sinc-shaped convolving function. The sampling technique uses fine-tuning of bandwidth and sampling rate to determine the strength of the k-space filter.

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Section: Mri K-space Acquisition and Image Space Reconstructionmentioning

confidence: 99%

Sampling technique for Fourier convolution theorem based k‐space filtering

et al. 2023

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“…Most of the information in k-space resides in the lower frequency region. The higher frequency regions are normally sparse in k-space, and the majority of the image information is residing near the central part of k-space [15].…”

Section: Mr K-spacementioning

confidence: 99%

A Characterization of CS-MRI Reconstruction Using PSO for Random Under Sampling Pattern

2019

IJRTE

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MR imaging method is widely used for diagnosis applications. The echo signal received from the MR scanning machine is used to generate the image. The data acquisition and reconstruction are the important operations. In this paper the kspace is compressively sampled using Radial Sampling pattern for acquiring the k-space data and Particle Swarm Optimization (PSO) with Total Variation (TV) is used as the reconstruction algorithm for the faithful reconstruction of MR image. The experiments are conducted on MR images of Brain, Head Angiogram and Shoulder images. Performance of the proposed method of reconstruction is analyzed for different sampling kspace scanning percentages. The reconstruction results are compared with the standard sampling pattern used for compressive sampling prove the novelty of the proposed method. The results are verified in terms of Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and Structural Similarity index (SSIM).

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“…Light grey or white areas in an MRI image are referred as high signal while as dark grey or black areas in an MRI image are referred to as low signal.MRI images are used to detect many types of tumors like brain tumor and abdomen tumors [37][38][39]. MRI images can help doctors to determine whether a tumor is noncancerous or cancerous.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Imaging Techniques for Cancer Diagnosis and Scope for Enhancement

Malik¹,

Lone²,

Quadri³

2016

IJIGSP

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Abstract-Imaging techniques are used to create images of structure, function and pathology of human body organs for cancer diagnosis. Various imaging techniques like X-ray, Positron Emission Tomography (PET), Ultrasound (US), MRI etc. are used for cancer diagnosis. These imaging techniques have gone through Lot of advancements during last few years. These techniques vary in the technology and application. Various artifacts exist in these imaging techniques and images produced by theses imaging techniques. These artifacts can be exploited to enhance the imaging technique and the images produced by them.

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A Review on Image Reconstruction through MRI k-Space Data

Cited by 18 publications

References 63 publications

Sampling technique for Fourier convolution theorem based k‐space filtering

Sampling technique for Fourier convolution theorem based k‐space filtering

A Characterization of CS-MRI Reconstruction Using PSO for Random Under Sampling Pattern

Imaging Techniques for Cancer Diagnosis and Scope for Enhancement

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