Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction
This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The propos...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/11/2/186 |
| _version_ | 1827344052794687488 |
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| author | Vishal Patel Alan Wang Andrew Paul Monk Marco Tien-Yueh Schneider |
| author_facet | Vishal Patel Alan Wang Andrew Paul Monk Marco Tien-Yueh Schneider |
| author_sort | Vishal Patel |
| collection | DOAJ |
| description | This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body. |
| first_indexed | 2024-03-07T22:41:25Z |
| format | Article |
| id | doaj.art-29b5485789fb4c91aeb644e696390cc4 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-07T22:41:25Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-29b5485789fb4c91aeb644e696390cc42024-02-23T15:08:02ZengMDPI AGBioengineering2306-53542024-02-0111218610.3390/bioengineering11020186Enhancing Knee MR Image Clarity through Image Domain Super-Resolution ReconstructionVishal Patel0Alan Wang1Andrew Paul Monk2Marco Tien-Yueh Schneider3Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandAuckland Bioengineering Institute, The University of Auckland, Auckland 1010, New ZealandThis study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.https://www.mdpi.com/2306-5354/11/2/186super-resolutionmagnetic resonance images (MRI)image reconstructionmachine learningconvolutional neural networks (CNN) |
| spellingShingle | Vishal Patel Alan Wang Andrew Paul Monk Marco Tien-Yueh Schneider Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction Bioengineering super-resolution magnetic resonance images (MRI) image reconstruction machine learning convolutional neural networks (CNN) |
| title | Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction |
| title_full | Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction |
| title_fullStr | Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction |
| title_full_unstemmed | Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction |
| title_short | Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction |
| title_sort | enhancing knee mr image clarity through image domain super resolution reconstruction |
| topic | super-resolution magnetic resonance images (MRI) image reconstruction machine learning convolutional neural networks (CNN) |
| url | https://www.mdpi.com/2306-5354/11/2/186 |
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