Medical Image Reconstruction in MRI Using Interpolation
DOI:
https://doi.org/10.63876/ijtm.v3i1.99Keywords:
MRI, image reconstruction, image reconstruction MRI, interpolation, PSNR, SSIM, medical imagesAbstract
Medical image reconstruction is a crucial element in magnetic resonance imaging (MRI) to produce high-quality images that support clinical diagnosis. This study aims to develop a medical image reconstruction method based on interpolation techniques that improves spatial accuracy and visual detail in MRI imaging results. The methodology used includes the implementation of bilinear and bicubic interpolation algorithms to process signal data obtained from MRI imaging. The dataset used in this study is brain MRI data from an open database that has been validated. The results show that the bilinear interpolation method provides higher computing speed, while bicubic interpolation produces better visual details on edges and small structures. Quantitative analysis using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics showed an improvement in the quality of the reconstruction images compared to conventional methods. In the brain dataset trial, bicubic interpolation recorded an average PSNR of 38.7 db and SSIM of 0.94, showing a significant improvement compared to the standard method. This research contributes to reducing artifacts and blurring in MRI reconstruction results, thus supporting more accurate medical decision-making. The implementation of this method also shows great potential to be applied in a variety of other clinical applications, such as soft tissue or internal organ imaging. This research is expected to be integrated with deep learning techniques to improve the efficiency and performance of medical image reconstruction in real time.
Downloads
References
H. Pan, Y. Fu, Z. Li, F. Wen, J. Hu, and B. Wu, “Images Reconstruction from Functional Magnetic Resonance Imaging Patterns Based on the Improved Deep Generative Multiview Model,” Neuroscience, vol. 509, pp. 103–112, Jan. 2023, doi: https://doi.org/10.1016/j.neuroscience.2022.11.021.
G. Yang, L. Zhang, A. Liu, X. Fu, X. Chen, and R. Wang, “MGDUN: An interpretable network for multi-contrast MRI image super-resolution reconstruction,” Comput. Biol. Med., vol. 167, p. 107605, Dec. 2023, doi: https://doi.org/10.1016/j.compbiomed.2023.107605.
C. Maciel and Q. Zou, “Dynamic MRI interpolation in temporal direction using an unsupervised generative model,” Comput. Med. Imaging Graph., vol. 117, p. 102435, Oct. 2024, doi: https://doi.org/10.1016/j.compmedimag.2024.102435.
S. Umirzakova, S. Ahmad, L. U. Khan, and T. Whangbo, “Medical image super-resolution for smart healthcare applications: A comprehensive survey,” Inf. Fusion, vol. 103, p. 102075, Mar. 2024, doi: https://doi.org/10.1016/j.inffus.2023.102075.
Y. Wu, J. Liu, G. M. White, and J. Deng, “Image-based motion artifact reduction on liver dynamic contrast enhanced MRI,” Phys. Medica, vol. 105, p. 102509, Jan. 2023, doi: https://doi.org/10.1016/j.ejmp.2022.12.001.
Y. Chen et al., “Performance evaluation of attention-deep hashing based medical image retrieval in brain MRI datasets,” J. Radiat. Res. Appl. Sci., vol. 17, no. 3, p. 100968, Sep. 2024, doi: https://doi.org/10.1016/j.jrras.2024.100968.
S. D. Desai, P. Naik, V. P. Baligar, and M. S M, “Interpolation based Low Dose CT Image Reconstruction,” Procedia Comput. Sci., vol. 171, pp. 2760–2769, 2020, doi: https://doi.org/10.1016/j.procs.2020.04.300.
C. Albuquerque, R. Henriques, and M. Castelli, “Deep learning-based object detection algorithms in medical imaging: Systematic review,” Heliyon, vol. 11, no. 1, p. e41137, Jan. 2025, doi: https://doi.org/10.1016/j.heliyon.2024.e41137.
J. Sander, B. D. de Vos, and I. Išgum, “Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI,” Med. Image Anal., vol. 78, p. 102393, May 2022, doi: https://doi.org/10.1016/j.media.2022.102393.
L. Deng, Y. Zhang, X. Yang, S. Huang, and J. Wang, “Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution,” Comput. Mater. Contin., vol. 75, no. 2, pp. 2671–2684, 2023, doi: https://doi.org/10.32604/cmc.2023.036642.
J. Panda and S. Meher, “An improved Image Interpolation technique using OLA e-spline,” Egypt. Informatics J., vol. 23, no. 2, pp. 159–172, Jul. 2022, doi: https://doi.org/10.1016/j.eij.2021.10.002.
M. B. Assad and R. Kiczales, “Deep Biomedical Image Classification Using Diagonal Bilinear Interpolation and residual network,” Int. J. Intell. Networks, vol. 1, pp. 148–156, 2020, doi: https://doi.org/10.1016/j.ijin.2020.11.001.
M. Jahnavi, D. R. Rao, and A. Sujatha, “A Comparative Study Of Super-Resolution Interpolation Techniques: Insights For Selecting The Most Appropriate Method,” Procedia Comput. Sci., vol. 233, pp. 504–517, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.240.
F. Ai and V. Lomakin, “Fast Fourier Transform periodic interpolation method for superposition sums in a periodic unit cell,” Comput. Phys. Commun., vol. 304, p. 109291, Nov. 2024, doi: https://doi.org/10.1016/j.cpc.2024.109291.
R. R. Sood et al., “3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction,” Med. Image Anal., vol. 69, p. 101957, Apr. 2021, doi: https://doi.org/10.1016/j.media.2021.101957.
A. E. Ilesanmi, T. O. Ilesanmi, and B. O. Ajayi, “Reviewing 3D convolutional neural network approaches for medical image segmentation,” Heliyon, vol. 10, no. 6, p. e27398, Mar. 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e27398.
K. Wu, Y. Xia, N. Ravikumar, and A. F. Frangi, “Compressed sensing using a deep adaptive perceptual generative adversarial network for MRI reconstruction from undersampled K-space data,” Biomed. Signal Process. Control, vol. 96, p. 106560, Oct. 2024, doi: https://doi.org/10.1016/j.bspc.2024.106560.
V. Roca, G. Kuchcinski, J.-P. Pruvo, D. Manouvriez, X. Leclerc, and R. Lopes, “A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset,” Heliyon, vol. 9, no. 12, p. e22647, Dec. 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e22647.
J. Stankowski and A. Dziembowski, “IV-PSNR: Software for immersive video objective quality evaluation,” SoftwareX, vol. 24, p. 101592, Dec. 2023, doi: https://doi.org/10.1016/j.softx.2023.101592.
A. Shakarami, L. Nicolè, M. Terreran, A. Paolo Dei Tos, and S. Ghidoni, “TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images,” Biomed. Signal Process. Control, vol. 84, p. 104812, Jul. 2023, doi: https://doi.org/10.1016/j.bspc.2023.104812.
X. Qifang, Y. Guoqing, and L. Pin, “Super-resolution Reconstruction of Satellite Video Images Based on Interpolation Method,” Procedia Comput. Sci., vol. 107, pp. 454–459, 2017, doi: https://doi.org/10.1016/j.procs.2017.03.089.
F. Cobos and L. M. Fernández-Cabrera, “Weakly compact bilinear operators among real interpolation spaces,” J. Math. Anal. Appl., vol. 529, no. 2, p. 126837, Jan. 2024, doi: https://doi.org/10.1016/j.jmaa.2022.126837.
W. SIMOES and M. DE SÁ, “PSNR and SSIM: Evaluation of the Imperceptibility Quality of Images Transmitted over Wireless Networks,” Procedia Comput. Sci., vol. 251, pp. 463–470, 2024, doi: https://doi.org/10.1016/j.procs.2024.11.134.
W. Muhammad, Z. Bhutto, S. Masroor, M. Hussain Shaikh, J. Shah, and A. Hussain, “IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution,” Comput. Model. Eng. Sci., vol. 136, no. 2, pp. 1121–1142, 2023, doi: https://doi.org/10.32604/cmes.2023.021438.
