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pca technique  (Respiratory Motion)

 
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    Respiratory Motion pca technique
    Pca Technique, supplied by Respiratory Motion, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pca+technique/pca+technique/pm40280889-26-0-32
    Average 90 stars, based on 1 article reviews
    pca technique - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Comparison:

    Article Title: Evaluation of a motion correction algorithm in lung cancer PET/CT: Phantom validation and patient studies.
    Article Snippet: Funding information Tianjin health research project, Grant/Award Numbers: TJWJ2023MS055, TJWJ2023QN105 Abstract Background: Data-driven gating (DDG) is an emerging technology that can reduce the respiratory motion artifacts in positron emission tomography (PET) images.. Purpose: The aim of this study is to use phantom and patient data to validate the performance of DDG with a motion correction algorithm based on the reconstruct, register, and average (RRA) method.. Methods: A customized motion platform drove the phantom (five spheres with diameters of 10–28 mm) using a periodic motion that had a duration of 3–5 s and amplitudes of 2–4 cm.

    Article Title: Towards the use of implanted cardiac leads or the diaphragm for active respiratory motion management in stereotactic arrhythmia radioablation.
    Article Snippet: For the 3D motion data, PCA will align the three principal components (PC1, PC2 & PC3) in orthogonal directions characterizing decreasing amounts of variance in the respiratory motion.

    Article Title: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model
    Article Snippet: In addition, PCA was used in both studies to capture the respiratory motion signals from the displacement field.

    Article Title: Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks
    Article Snippet: By utilizing PCA to reduce the dimensionality of the DVFs, the ConvLSTM network focuses on the most significant components of respiratory motion.

    Article Title: An original methodology for quantitative assessment of perfusion in small animal studies using contrast-enhanced ultrasound
    Article Snippet: This estimation was based on the assumption that not only one but several components held information related to the respiratory motion R ( t ), so that the latter was found as a linear combination of the five main components provided by PCA, referred to as C 0 , ... , C 4 ( ): R ( t ) = C 0 ( t ) + λ 1 C 1 ( t ) + λ 2 C 2 ( t ) + λ 3 C 3 ( t ) + λ 4 C 4 ( t ). (1) fig ft0 fig mode=article f1 Fig. 1 caption a4 Exemple of the five main normalized components C 0 to C 4 and C 0 ∗ to C 4 ∗ given by PCA, before (a) and after (b) frame selection, respectively.

    Article Title: XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing
    Article Snippet: As proposed by Pang et al ( 29 ), PCA can be interpreted as a procedure to determine the most common signal variation mode among all coils, and the principal component with the highest peak in the frequency range of 0.1–0.5 Hz was selected to represent respiratory motion ( ).

    Article Title: Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling
    Article Snippet: Respiratory motion detection was then performed using PCA.

    Plasmid Preparation:

    Article Title: Evaluation of a motion correction algorithm in lung cancer PET/CT: Phantom validation and patient studies.
    Article Snippet: Funding information Tianjin health research project, Grant/Award Numbers: TJWJ2023MS055, TJWJ2023QN105 Abstract Background: Data-driven gating (DDG) is an emerging technology that can reduce the respiratory motion artifacts in positron emission tomography (PET) images.. Purpose: The aim of this study is to use phantom and patient data to validate the performance of DDG with a motion correction algorithm based on the reconstruct, register, and average (RRA) method.. Methods: A customized motion platform drove the phantom (five spheres with diameters of 10–28 mm) using a periodic motion that had a duration of 3–5 s and amplitudes of 2–4 cm.

    Article Title: Towards the use of implanted cardiac leads or the diaphragm for active respiratory motion management in stereotactic arrhythmia radioablation.
    Article Snippet: For the 3D motion data, PCA will align the three principal components (PC1, PC2 & PC3) in orthogonal directions characterizing decreasing amounts of variance in the respiratory motion.

    Article Title: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model
    Article Snippet: In addition, PCA was used in both studies to capture the respiratory motion signals from the displacement field.

    Article Title: Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks
    Article Snippet: By utilizing PCA to reduce the dimensionality of the DVFs, the ConvLSTM network focuses on the most significant components of respiratory motion.

    Article Title: An original methodology for quantitative assessment of perfusion in small animal studies using contrast-enhanced ultrasound
    Article Snippet: This estimation was based on the assumption that not only one but several components held information related to the respiratory motion R ( t ), so that the latter was found as a linear combination of the five main components provided by PCA, referred to as C 0 , ... , C 4 ( ): R ( t ) = C 0 ( t ) + λ 1 C 1 ( t ) + λ 2 C 2 ( t ) + λ 3 C 3 ( t ) + λ 4 C 4 ( t ). (1) fig ft0 fig mode=article f1 Fig. 1 caption a4 Exemple of the five main normalized components C 0 to C 4 and C 0 ∗ to C 4 ∗ given by PCA, before (a) and after (b) frame selection, respectively.

    Article Title: XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing
    Article Snippet: As proposed by Pang et al ( 29 ), PCA can be interpreted as a procedure to determine the most common signal variation mode among all coils, and the principal component with the highest peak in the frequency range of 0.1–0.5 Hz was selected to represent respiratory motion ( ).

    Article Title: Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling
    Article Snippet: Respiratory motion detection was then performed using PCA.

    Generated:

    Article Title: Evaluation of a motion correction algorithm in lung cancer PET/CT: Phantom validation and patient studies.
    Article Snippet: Funding information Tianjin health research project, Grant/Award Numbers: TJWJ2023MS055, TJWJ2023QN105 Abstract Background: Data-driven gating (DDG) is an emerging technology that can reduce the respiratory motion artifacts in positron emission tomography (PET) images.. Purpose: The aim of this study is to use phantom and patient data to validate the performance of DDG with a motion correction algorithm based on the reconstruct, register, and average (RRA) method.. Methods: A customized motion platform drove the phantom (five spheres with diameters of 10–28 mm) using a periodic motion that had a duration of 3–5 s and amplitudes of 2–4 cm.

    Article Title: Towards the use of implanted cardiac leads or the diaphragm for active respiratory motion management in stereotactic arrhythmia radioablation.
    Article Snippet: For the 3D motion data, PCA will align the three principal components (PC1, PC2 & PC3) in orthogonal directions characterizing decreasing amounts of variance in the respiratory motion.

    Article Title: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model
    Article Snippet: In addition, PCA was used in both studies to capture the respiratory motion signals from the displacement field.

    Article Title: Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks
    Article Snippet: By utilizing PCA to reduce the dimensionality of the DVFs, the ConvLSTM network focuses on the most significant components of respiratory motion.

    Article Title: An original methodology for quantitative assessment of perfusion in small animal studies using contrast-enhanced ultrasound
    Article Snippet: This estimation was based on the assumption that not only one but several components held information related to the respiratory motion R ( t ), so that the latter was found as a linear combination of the five main components provided by PCA, referred to as C 0 , ... , C 4 ( ): R ( t ) = C 0 ( t ) + λ 1 C 1 ( t ) + λ 2 C 2 ( t ) + λ 3 C 3 ( t ) + λ 4 C 4 ( t ). (1) fig ft0 fig mode=article f1 Fig. 1 caption a4 Exemple of the five main normalized components C 0 to C 4 and C 0 ∗ to C 4 ∗ given by PCA, before (a) and after (b) frame selection, respectively.

    Article Title: XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing
    Article Snippet: As proposed by Pang et al ( 29 ), PCA can be interpreted as a procedure to determine the most common signal variation mode among all coils, and the principal component with the highest peak in the frequency range of 0.1–0.5 Hz was selected to represent respiratory motion ( ).

    Article Title: Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling
    Article Snippet: Respiratory motion detection was then performed using PCA.



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    Image Search Results


    Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Journal: Human Brain Mapping

    Article Title: Dynamic Functional Connectivity Between Amygdala and Cognitive Control Network Predicts Delay Discounting in Older Adolescents

    doi: 10.1002/hbm.70270

    Figure Lengend Snippet: Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Article Snippet: A PCA was performed for each amygdala's dFC in MATLAB (2018b).

    Techniques: Functional Assay, Control