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Arshad SM, Potter LC, Chen C, Liu Y, Chandrasekaran P, Crabtree C, Tong MS, Simonetti OP, Han Y, Ahmad R. Motion-robust free-running volumetric cardiovascular MRI. Magn Reson Med 2024. [PMID: 38733066 DOI: 10.1002/mrm.30123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 05/13/2024]
Abstract
PURPOSE To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.
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Affiliation(s)
- Syed M Arshad
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
- Electrical & Computer Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Lee C Potter
- Electrical & Computer Engineering, The Ohio State University, Columbus, Ohio, USA
- Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Chong Chen
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
- Electrical & Computer Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Yingmin Liu
- Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Preethi Chandrasekaran
- Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | | | - Matthew S Tong
- Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Orlando P Simonetti
- Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Yuchi Han
- Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
- Electrical & Computer Engineering, The Ohio State University, Columbus, Ohio, USA
- Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
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Carr AV, Bollis NE, Pavek JG, Shortreed MR, Smith LM. Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics. Proteomics 2024; 24:e2300234. [PMID: 38487981 DOI: 10.1002/pmic.202300234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/15/2024] [Accepted: 02/29/2024] [Indexed: 04/05/2024]
Abstract
The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.
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Affiliation(s)
- Austin V Carr
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nicholas E Bollis
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - John G Pavek
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Lv X, Wang L, Huang D, Wang S. A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications. Sensors (Basel) 2023; 23:7483. [PMID: 37687939 PMCID: PMC10490744 DOI: 10.3390/s23177483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/18/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023]
Abstract
The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.
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Affiliation(s)
- Xiaoyang Lv
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
- Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China
| | - Lei Wang
- Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, China
- College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (D.H.); (S.W.)
| | - Dexiang Huang
- College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (D.H.); (S.W.)
- National Deep Sea Center, Qingdao 266237, China
| | - Shengli Wang
- College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (D.H.); (S.W.)
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Dutta A, Hasan MK, Ahmad M, Awal MA, Islam MA, Masud M, Meshref H. Early Prediction of Diabetes Using an Ensemble of Machine Learning Models. Int J Environ Res Public Health 2022; 19:ijerph191912378. [PMID: 36231678 PMCID: PMC9566114 DOI: 10.3390/ijerph191912378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/20/2022] [Accepted: 09/24/2022] [Indexed: 05/15/2023]
Abstract
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
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Affiliation(s)
- Aishwariya Dutta
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
- Department of Biomedical Engineering (BME), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Md. Kamrul Hasan
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Mohiuddin Ahmad
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Md. Abdul Awal
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
- Electronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh
- Correspondence:
| | | | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hossam Meshref
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Fischer C, Wetzl J, Schaeffter T, Giese D. Fully automated background phase correction using M-estimate SAmple consensus (MSAC)-Application to 2D and 4D flow. Magn Reson Med 2022; 88:2709-2717. [PMID: 35916368 DOI: 10.1002/mrm.29363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/11/2022] [Accepted: 05/25/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE Flow quantification by phase-contrast MRI is hampered by spatially varying background phase offsets. Correction performance by polynomial regression on stationary tissue may be affected by outliers such as wrap-around or constant flow. Therefore, we propose an alternative, M-estimate SAmple Consensus (MSAC) to reject outliers, and improve and fully automate background phase correction. METHODS The MSAC technique fits polynomials to randomly drawn small samples from the image. Over several trials, it aims to find the best consensus set of valid pixels by rejecting outliers to the fit and minimizing the residuals of the remaining pixels. The robustness of MSAC to its few parameters was investigated and verified using third-order polynomial correction fits on a total of 118 2D flow (97 with wrap-around) and 18 4D flow data sets (14 with wrap-around), acquired at 1.5 T and 3 T. Background phase was compared with standard stationary correction and phantom correction. Pulmonary/systemic flow ratios in 2D flow were derived, and exemplary 4D flow analysis was performed. RESULTS The MSAC technique is robust over a range of parameter choices, and a unique set of parameters is suitable for both 2D and 4D flow. In 2D flow, phase errors were significantly reduced by MSAC compared with stationary correction (p = 0.005), and stationary correction shows larger errors in pulmonary/systemic flow ratios compared with MSAC. In 4D flow, MSAC shows similar performance as stationary correction. CONCLUSIONS The MSAC method provides fully automated background phase correction to 2D and 4D flow data and shows improved robustness over stationary correction, especially with outliers present.
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Affiliation(s)
- Carola Fischer
- Department of Medical Imaging, Technical University of Berlin, Berlin, Germany.,Magnetic Resonance, Siemens Healthcare, Erlangen, Germany
| | - Jens Wetzl
- Magnetic Resonance, Siemens Healthcare, Erlangen, Germany
| | - Tobias Schaeffter
- Department of Medical Imaging, Technical University of Berlin, Berlin, Germany.,Biomedical Imaging, Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Berlin, Germany.,School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daniel Giese
- Magnetic Resonance, Siemens Healthcare, Erlangen, Germany.,Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
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Chan M, Ganti VG, Heller JA, Abdallah CA, Etemadi M, Inan OT. Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum. Biosensors (Basel) 2021; 11:bios11120521. [PMID: 34940278 PMCID: PMC8699050 DOI: 10.3390/bios11120521] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 05/31/2023]
Abstract
In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.
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Affiliation(s)
- Michael Chan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
| | - Venu G. Ganti
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - J. Alex Heller
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (J.A.H.); (M.E.)
| | - Calvin A. Abdallah
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
| | - Mozziyar Etemadi
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (J.A.H.); (M.E.)
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60201, USA
| | - Omer T. Inan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Lee K, Johnson EN. Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation. Sensors (Basel) 2020; 20:s20072036. [PMID: 32260451 PMCID: PMC7181286 DOI: 10.3390/s20072036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 03/28/2020] [Accepted: 04/01/2020] [Indexed: 11/16/2022]
Abstract
With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as “outlier-adaptive filtering”. Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework.
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Affiliation(s)
- Kyuman Lee
- School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30313, USA
- Correspondence: ; Tel.: +1-404-422-3697
| | - Eric N. Johnson
- Faculty of Aerospace Engineering, The Pennsylvania State University, 229 Hammond Building, University Park, PA 16802, USA;
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Dolui S, Wang Z, Shinohara RT, Wolk DA, Detre JA. Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series. J Magn Reson Imaging 2017; 45:1786-1797. [PMID: 27570967 PMCID: PMC5332532 DOI: 10.1002/jmri.25436] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 08/05/2016] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To propose and validate Structural Correlation-based Outlier REjection (SCORE), a novel algorithm for removal of artifacts arising from outlier control-label pairs in 2D arterial spin labeling (ASL) data. MATERIALS AND METHODS The proposed method was assessed with respect to other state-of-the-art ASL signal processing approaches using 2D pulsed ASL data obtained with a 3T Siemens scanner from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Longitudinal data from control participants acquired 3 months apart were used to assess within-subject coefficient of variation (wsCV) based on the assumption that the optimal signal processing strategy will minimize control subject retest variability in Cerebral Blood Flow (CBF). SCORE was further evaluated by determining its sensitivity for distinguishing patients with Alzheimer's disease (AD) from controls based on hypoperfusion in predefined regions of interest (ROIs) that are known to be sensitive to AD-related changes. RESULTS SCORE coupled with a preprocessing step to discard a few extreme outliers (combined algorithm referred to as SCORE+) reduced wsCV up to 21% in gray matter and 39% in smaller ROIs compared to the reference algorithms. It also provided an average increase in effect size for patient-control differences of 50% compared to other algorithms in a priori ROIs sensitive to AD-related changes. This increase was statistically significant (P < 0.05) for the majority of the ROIs and methods as evaluated by permutation tests. CONCLUSION CBF maps generated with SCORE or SCORE + provide improved retest reliability in control subjects while simultaneously increasing sensitivity to pathological CBF effects between controls and patients. J. Magn. Reson. Imaging 2016 Level of Evidence: 2 J. MAGN. RESON. IMAGING 2017;45:1786-1797.
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Affiliation(s)
- Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ze Wang
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Departments of Psychiatry and Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Johansson A, Balter J, Feng M, Cao Y. An Overdetermined System of Transform Equations in Support of Robust DCE-MRI Registration With Outlier Rejection. ACTA ACUST UNITED AC 2016; 2:188-196. [PMID: 28367502 PMCID: PMC5373730 DOI: 10.18383/j.tom.2016.00145] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Quantitative hepatic perfusion parameters derived by fitting dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of liver to a pharmacokinetic model are prone to errors if the dynamic images are not corrected for respiratory motion by image registration. The contrast-induced intensity variations in pre- and postcontrast phases pose challenges for the accuracy of image registration. We propose an overdetermined system of transformation equations between the image volumes in the DCE-MRI series to achieve robust alignment. In this method, we register each volume to every other volume. From the transforms produced by all pairwise registrations, we constructed an overdetermined system of transform equations that was solved robustly by minimizing the L1/2-norm of the residuals. This method was evaluated on a set of 100 liver DCE-MRI examinations from 35 patients by examining the area under spikes appearing in the voxel time–intensity curves. The robust alignment procedure significantly reduced the area under intensity spikes compared with unregistered volumes (P < .001) and volumes registered to a single reference phase (P < .001). Our registration procedure provides a larger number of reliable time–intensity curve samples. The additional reliable samples in the precontrast baseline are important for calculating the postcontrast signal enhancement and thereby for converting intensity to contrast concentration. On the intensity ramp, retained samples help to better describe the uptake dynamics, providing a better foundation for parameter estimation. The presented method also simplifies the analysis of data sets with many patients by eliminating the need for manual intervention during registration.
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Affiliation(s)
- Adam Johansson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Mary Feng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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10
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You W, Serag A, Evangelou IE, Andescavage N, Limperopoulos C. Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia. Proc SPIE Int Soc Opt Eng 2015; 9417:941700. [PMID: 25859294 DOI: 10.1117/12.2082451] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed from ROI-averaged signals. The results demonstrate that the proposed method is effective in enhancing motion correction in fetal fMRI without large data loss, compared to traditional motion correction methods.
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Affiliation(s)
- Wonsang You
- Division of Diagnostic Imaging and Radiology, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA
| | - Ahmed Serag
- Division of Diagnostic Imaging and Radiology, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA
| | - Iordanis E Evangelou
- Division of Diagnostic Imaging and Radiology, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA
| | - Nickie Andescavage
- Division of Fetal and Transitional Medicine, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA ; Division of Neonatology, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA
| | - Catherine Limperopoulos
- Division of Diagnostic Imaging and Radiology, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA ; Division of Fetal and Transitional Medicine, Childrens National Medical Center, 111 Michigan Ave. N.W., Washington, D.C., USA
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Abstract
In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with `merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled `paired-refinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.
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Affiliation(s)
- K Diederichs
- Faculty of Biology, University of Konstanz, M647, 78457 Konstanz, Germany.
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