Entropy-Based SVM Classifier for Automatic Detection of Motion Artifacts in Clinical MRI

Preetha, Chandrakanth Jayachandran; Mattern, Hendrik; Juneja, Medha; Vogt, Johannes; Speck, Oliver; Hartkens, Thomas

doi:10.1007/978-3-658-29267-6_23

Cited by 2 publications

(9 citation statements)

References 7 publications

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“…Multiple methods have been proposed for performing motion artifact simulation, 33 including those based on magnitude images [6][7][8][9] and those based on multi-channel complex k-space data. 18,34 Magnitude image-based methods require less computational time and are applicable to more studies because the raw k-space data are not always available.…”

Section: Discussionmentioning

confidence: 99%

“…same coordinates. [6][7][8][9] We adopted the latter approach since it matches more closer to the actual data sampling and image reconstruction process in the presence of motion. To the best of our knowledge, our method of calculating motion-corrupted data directly using nonuniform fast Fourier transform has not been proposed in the earlier studies.…”

Section: Discussionmentioning

confidence: 99%

“…Motion can induce blurring and ringing artifacts and additional noises in the reconstructed image, 3,4 rendering inaccuracy and unreliability of PVS measurement and reducing its diagnostic or scientific relevance. 5 To mitigate the motion-induced artifacts, two widely used schemes including retrospective motion correction [5][6][7][8][9] and prospective motion correction (PMC) have been reported. [10][11][12][13][14][15][16][17][18] An earlier study 5 using fat navigators (Fat-Nav) to estimate motion profile has found the beneficial effects of retrospective motion correction on measuring PVS visibility and physiological changes.…”

Section: Introductionmentioning

confidence: 99%

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Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

Zhao,

Zhou,

Zong

2024

Magnetic Resonance in Med

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PurposeThe effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat‐navigator‐based PMC in improving perivascular space (PVS) visibility in T2‐weighted MRI.MethodsMRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2‐weighted NoPMC and PMC images. To simulate motion artifacts, k‐space data at motion‐perturbed positions were calculated from artifact‐free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion‐induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.ResultsIn simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.ConclusionsThe proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

Zhao,

Zhou,

Zong

2024

Magnetic Resonance in Med

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“…Multiple methods have been proposed for performing motion artifact simulation [34], including those based on magnitude images [6][7][8][9] and those based on multi-channel complex k-space data [18,35]. Motion effects were accounted for by either modifying k-space coordinates while keeping the data intact [8,18,35], or by modifying the k-space data at the same coordinates [6,7,9]. Magnitude image-based methods require less computational time and is applicable to more studies as the raw k-space data are not always available.…”

Section: Discussionmentioning

confidence: 99%

“…To mitigate the motion-induced artifacts, two widely used schemes including retrospective motion correction (RMC) [5][6][7][8][9] and prospective motion correction (PMC) have been reported [10][11][12][13][14][15][16][17][18]. An earlier study [5] using fat navigators (FatNav) to estimate motion profile has found the beneficial effects of RMC on measuring PVS visibility and physiological changes.…”

Section: Introductionmentioning

confidence: 99%

Effects of Prospective Motion Correction on Perivascular Spaces at 7T MRI Evaluated Using Motion Artifact Simulation

Zhao,

Zhou,

Zong

2023

Preprint

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PurposeProspective motion correction (PMC) is a promising method in mitigating motion artifacts in MRI. However, its effectiveness in improving the visibility of vessel-like thin structures in routine studies is unclear. In this study, we aim to demonstrate the ability of fat-navigator based PMC in improving the visibility of perivascular spaces (PVS) using data from two earlier studies.MethodsTwo open source MRI data set were used for motion artifact simulation and evaluating PMC, which consist of 66 T2-weighted images without PMC and 38 T2-weighted images with PMC. PMC was performed by adjusting field of view during scan based on motion parameters derived from fat navigators. Motion artifact simulation was performed by misplacing k-space data at a motion-related non-cartesian grid onto the cartesian grid calculated using motion-free images to generate the images without effects of PMC. The simulation’s ability to reproduce motion-induced blurring and ringing artifacts was evaluated using the sharpness at the lateral ventricle/white matter (WM) boundary and the magnitude of ringing artifact component in the Fourier spectrum. PVS volume fraction in WM was employed to reflect its visibility. Sharpness, magnitude of ringing artifact and PVS volume fraction were then compared between simulated images and real images with and without PMC.ResultsThe consistencies in sharpness (rho ≥ 0.86, corrected p ≤ 4.4 ×10-16) and ringing artifact magnitude (rho ≥ 0.42, corrected p ≤ 0.001) were found between simulated images and real images without PMC. There was a significant negative correlation (rho ≤ -0.27, corrected p ≤ 0.08) between PVS volume fraction and motion severity in both simulated and real images without PMC. PMC removed the above correlations (rho ≥ -0.02, corrected p > 1) and increased the boundary sharpness compared to the images simulated using the same motion traces.ConclusionsMotion artifact simulation can reproduce the desired motion-induced artifacts on images. PMC reduces the negative impacts of motion on image quality and improves PVS visibility.

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Entropy-Based SVM Classifier for Automatic Detection of Motion Artifacts in Clinical MRI

Cited by 2 publications

References 7 publications

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

Effects of Prospective Motion Correction on Perivascular Spaces at 7T MRI Evaluated Using Motion Artifact Simulation

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