A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.

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Rigid Motion Artifact Reduction in CT Using the Phase Correlation Method

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200606224154 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1119-1128

Author(s):

Yuan Zhang ◽

Liyi Zhang ◽

Yunshan Sun

Keyword(s):

Rigid Motion ◽

Correlation Method ◽

Reconstruction Algorithm ◽

Human Head ◽

Motion Artifact ◽

Motion Artifacts ◽

Phase Correlation ◽

Artifact Reduction ◽

Patient Motion ◽

Reconstruction Quality

Background: In Computed Tomography (CT), it is often not possible for the subject to remain stationary during a scan. Unfortunately, a patient motion would result in degraded spatial resolution and image artifacts. It is desirable to improve reconstruction quality and reduce motion artifacts caused by patient motion. Methods: In this work, a method was proposed to eliminate the influence of the motion on image quality, based on the phase correlation method. Based on our previous work, projections were first taken by Radon transform and motion parameters were estimated by the phase-only correlation of projections in the Radon domain. In addition, an improved image reconstruction algorithm was performed to compensate for the motion effects. Results: Experimental results proved that the proposed method could not only obtain high precision and good real-time performance but also ensure a superior artifact reduction. Conclusion: Besides, the efficacy of the proposed method has been demonstrated in both simulated and human head experiments.

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Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

European Radiology ◽

10.1007/s00330-020-07006-1 ◽

2020 ◽

Vol 30 (11) ◽

pp. 5923-5932

Author(s):

M.-L. Kromrey ◽

D. Tamada ◽

H. Johno ◽

S. Funayama ◽

N. Nagata ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Motion Artifact ◽

Motion Artifacts ◽

Arterial Phase ◽

Artifact Reduction ◽

Gadoxetate Disodium ◽

Mr Images ◽

Liver Mri ◽

Lesion Conspicuity

Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

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Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Medical Image Analysis ◽

10.1016/j.media.2020.101883 ◽

2021 ◽

Vol 67 ◽

pp. 101883

Author(s):

Youngjun Ko ◽

Seunghyuk Moon ◽

Jongduk Baek ◽

Hyunjung Shim

Keyword(s):

Rigid Motion ◽

Motion Artifact ◽

Artifact Reduction ◽

X Ray

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Rigid motion artifact reduction in CT using frequency domain analysis

Journal of X-Ray Science and Technology ◽

10.3233/xst-16193 ◽

2017 ◽

Vol 25 (5) ◽

pp. 721-736 ◽

Cited By ~ 2

Author(s):

Yuan Zhang ◽

Liyi Zhang ◽

Yunshan Sun

Keyword(s):

Frequency Domain ◽

Rigid Motion ◽

Motion Artifact ◽

Domain Analysis ◽

Frequency Domain Analysis ◽

Artifact Reduction

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Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths

Sensors ◽

10.3390/s19030673 ◽

2019 ◽

Vol 19 (3) ◽

pp. 673 ◽

Cited By ~ 17

Author(s):

Yifan Zhang ◽

Shuang Song ◽

Rik Vullings ◽

Dwaipayan Biswas ◽

Neide Simões-Capela ◽

...

Keyword(s):

Signal Reconstruction ◽

Motion Artifact ◽

Motion Artifacts ◽

Health Conditions ◽

Average Error ◽

Artifact Reduction ◽

Modular Algorithm ◽

Algorithm Framework ◽

Electrocardiogram Ecg

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

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MRI: How to perform a pediatric scan

Acta Radiologica ◽

10.1177/0284185112474917 ◽

2013 ◽

Vol 54 (9) ◽

pp. 991-997 ◽

Cited By ~ 5

Author(s):

Øystein E Olsen

Keyword(s):

Signal To Noise Ratio ◽

Motion Artifact ◽

Motion Artifacts ◽

Acquisition Time ◽

Artifact Reduction ◽

Resonance Imaging ◽

Basic Principles ◽

Low Snr ◽

Patient Preparation ◽

Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) is rich in diagnostic information but requires optimization for use in children. The main problems are motion artifacts and poor signal-to-noise ratio (SNR). SNR is proportional to voxel volume, which must therefore not be too small, however, usually needs to be reduced compared to adult imaging to account for the finer anatomy of the child. The use of multi-channel coils with element sizes appropriate for the anatomy of interest ensures optimal baseline SNR. Longer acquisition time increases SNR (with a square-root factor), but the flip-side is that this allows more motion artifacts. Attention to patient preparation and to techniques for motion artifact reduction is therefore crucial, and the most important principles are discussed. Low SNR may in part be compensated by optimizing the image contrast by weighting (tissue and lesions T1 and T2 may differ from adults) and by using contrast agents. It is also powerful to combine different image contrasts during postprocessing. The basic principles are discussed, followed by an example scan protocol.

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Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths

Sensors ◽

10.3390/s20051493 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1493 ◽

Cited By ~ 4

Author(s):

Jongshill Lee ◽

Minseong Kim ◽

Hoon-Ki Park ◽

In Young Kim

Keyword(s):

Motion Artifact ◽

Motion Artifacts ◽

Measuring System ◽

Depth Information ◽

Artifact Reduction ◽

Truncated Singular Value Decomposition ◽

Fast Walking ◽

Multiple Wavelengths ◽

Measure Heart Rate ◽

Value Decomposition

Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.

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