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Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 42

Enhancing MRI Imaging Efficiency: A Hybrid Under-Sampling Strategy for k-Space Data Acquisition

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DOI: http://dx.doi.org/10.15439/2024R58

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 147–150 ()

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Abstract. Compressed Sensing (CS) offers a promising solution to reduce MRI acquisition times, addressing challenges of prolonged scans and patient discomfort. This paper presents a new method for compressing and reconstructing MRI images using k-space gradients. A hybrid under-sampling approach allocates 80\% of measurements to random sampling and 20\% to deterministic sampling near the k-space center. Additionally, it explores the impact of reducing kx samples by 15\%, 25\%, and 50\% on image quality. Reconstruction uses a nonlinear conjugate gradient method, with image quality assessed via a similarity index Q. Results show the proposed CS approach effectively compresses MRI data while preserving essential image quality, optimizing protocols and reducing scan times.

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