The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Priest, Andrew N - One of the best experts on this subject based on the ideXlab platform.

  • Subtractive NCE-MRA: Improved background suppression using robust Regression-based weighted subtraction.
    'Organisation for Economic Co-Operation and Development (OECD)', 2021
    Co-Authors: Li Hao, Wang Shuo, Graves Martin, Lomas, David J, Priest, Andrew N
    Abstract:

    Purpose: To correct the intensity difference of static background signal between bright-blood images (BBIs) and dark-blood images (DBIs) in subtractive Non-contrast-enhanced MR Angiography (NCE-MRA) using robust Regression, thereby improving static background signal suppression on subtracted angiograms. Method: Robust Regression (RR), using iteratively reweighted least squares, is used to calculate the Regression coefficient of background tissues from a scatter plot showing the voxel intensity of BBIs versus DBIs. The weighting function is based on either the Euclidean distance from the Estimated Regression Line or the deviation angle. Results from RR using deviation angle (RRDA), conventional RR using the Euclidean distance (cRR) and ordinary least-squares Regression (OLS) were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic Flow-Sensitive Dephasing (FSD) datasets, 13 iliac FSD datasets and 26 femoral Fresh Blood Imaging (FBI) datasets. Result: RRDA achieved robust and accurate performance in all types of images, with small bias, small mean absolute error and high correlation coefficients with reference values. Background tissues, such as muscle, veins and bladder, were suppressed while the vascular signal was preserved. cRR gave good performance for thoracic and iliac FSD but could not suppress background tissues in femoral FBI. OLS was sensitive to outliers and overEstimated Regression coefficients in thoracic FSD. Conclusion: Weighted subtraction using RR was able to acquire the Regression coefficients of background signal and improve background suppression of subtractive NCE-MRA techniques. RRDA has the most robust and accurate overall performance among three Regression methods

Li Hao - One of the best experts on this subject based on the ideXlab platform.

  • Subtractive NCE-MRA: Improved background suppression using robust Regression-based weighted subtraction.
    'Organisation for Economic Co-Operation and Development (OECD)', 2021
    Co-Authors: Li Hao, Wang Shuo, Graves Martin, Lomas, David J, Priest, Andrew N
    Abstract:

    Purpose: To correct the intensity difference of static background signal between bright-blood images (BBIs) and dark-blood images (DBIs) in subtractive Non-contrast-enhanced MR Angiography (NCE-MRA) using robust Regression, thereby improving static background signal suppression on subtracted angiograms. Method: Robust Regression (RR), using iteratively reweighted least squares, is used to calculate the Regression coefficient of background tissues from a scatter plot showing the voxel intensity of BBIs versus DBIs. The weighting function is based on either the Euclidean distance from the Estimated Regression Line or the deviation angle. Results from RR using deviation angle (RRDA), conventional RR using the Euclidean distance (cRR) and ordinary least-squares Regression (OLS) were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic Flow-Sensitive Dephasing (FSD) datasets, 13 iliac FSD datasets and 26 femoral Fresh Blood Imaging (FBI) datasets. Result: RRDA achieved robust and accurate performance in all types of images, with small bias, small mean absolute error and high correlation coefficients with reference values. Background tissues, such as muscle, veins and bladder, were suppressed while the vascular signal was preserved. cRR gave good performance for thoracic and iliac FSD but could not suppress background tissues in femoral FBI. OLS was sensitive to outliers and overEstimated Regression coefficients in thoracic FSD. Conclusion: Weighted subtraction using RR was able to acquire the Regression coefficients of background signal and improve background suppression of subtractive NCE-MRA techniques. RRDA has the most robust and accurate overall performance among three Regression methods

Wang Shuo - One of the best experts on this subject based on the ideXlab platform.

  • Subtractive NCE-MRA: Improved background suppression using robust Regression-based weighted subtraction.
    'Organisation for Economic Co-Operation and Development (OECD)', 2021
    Co-Authors: Li Hao, Wang Shuo, Graves Martin, Lomas, David J, Priest, Andrew N
    Abstract:

    Purpose: To correct the intensity difference of static background signal between bright-blood images (BBIs) and dark-blood images (DBIs) in subtractive Non-contrast-enhanced MR Angiography (NCE-MRA) using robust Regression, thereby improving static background signal suppression on subtracted angiograms. Method: Robust Regression (RR), using iteratively reweighted least squares, is used to calculate the Regression coefficient of background tissues from a scatter plot showing the voxel intensity of BBIs versus DBIs. The weighting function is based on either the Euclidean distance from the Estimated Regression Line or the deviation angle. Results from RR using deviation angle (RRDA), conventional RR using the Euclidean distance (cRR) and ordinary least-squares Regression (OLS) were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic Flow-Sensitive Dephasing (FSD) datasets, 13 iliac FSD datasets and 26 femoral Fresh Blood Imaging (FBI) datasets. Result: RRDA achieved robust and accurate performance in all types of images, with small bias, small mean absolute error and high correlation coefficients with reference values. Background tissues, such as muscle, veins and bladder, were suppressed while the vascular signal was preserved. cRR gave good performance for thoracic and iliac FSD but could not suppress background tissues in femoral FBI. OLS was sensitive to outliers and overEstimated Regression coefficients in thoracic FSD. Conclusion: Weighted subtraction using RR was able to acquire the Regression coefficients of background signal and improve background suppression of subtractive NCE-MRA techniques. RRDA has the most robust and accurate overall performance among three Regression methods

Graves Martin - One of the best experts on this subject based on the ideXlab platform.

  • Subtractive NCE-MRA: Improved background suppression using robust Regression-based weighted subtraction.
    'Organisation for Economic Co-Operation and Development (OECD)', 2021
    Co-Authors: Li Hao, Wang Shuo, Graves Martin, Lomas, David J, Priest, Andrew N
    Abstract:

    Purpose: To correct the intensity difference of static background signal between bright-blood images (BBIs) and dark-blood images (DBIs) in subtractive Non-contrast-enhanced MR Angiography (NCE-MRA) using robust Regression, thereby improving static background signal suppression on subtracted angiograms. Method: Robust Regression (RR), using iteratively reweighted least squares, is used to calculate the Regression coefficient of background tissues from a scatter plot showing the voxel intensity of BBIs versus DBIs. The weighting function is based on either the Euclidean distance from the Estimated Regression Line or the deviation angle. Results from RR using deviation angle (RRDA), conventional RR using the Euclidean distance (cRR) and ordinary least-squares Regression (OLS) were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic Flow-Sensitive Dephasing (FSD) datasets, 13 iliac FSD datasets and 26 femoral Fresh Blood Imaging (FBI) datasets. Result: RRDA achieved robust and accurate performance in all types of images, with small bias, small mean absolute error and high correlation coefficients with reference values. Background tissues, such as muscle, veins and bladder, were suppressed while the vascular signal was preserved. cRR gave good performance for thoracic and iliac FSD but could not suppress background tissues in femoral FBI. OLS was sensitive to outliers and overEstimated Regression coefficients in thoracic FSD. Conclusion: Weighted subtraction using RR was able to acquire the Regression coefficients of background signal and improve background suppression of subtractive NCE-MRA techniques. RRDA has the most robust and accurate overall performance among three Regression methods

Lomas, David J - One of the best experts on this subject based on the ideXlab platform.

  • Subtractive NCE-MRA: Improved background suppression using robust Regression-based weighted subtraction.
    'Organisation for Economic Co-Operation and Development (OECD)', 2021
    Co-Authors: Li Hao, Wang Shuo, Graves Martin, Lomas, David J, Priest, Andrew N
    Abstract:

    Purpose: To correct the intensity difference of static background signal between bright-blood images (BBIs) and dark-blood images (DBIs) in subtractive Non-contrast-enhanced MR Angiography (NCE-MRA) using robust Regression, thereby improving static background signal suppression on subtracted angiograms. Method: Robust Regression (RR), using iteratively reweighted least squares, is used to calculate the Regression coefficient of background tissues from a scatter plot showing the voxel intensity of BBIs versus DBIs. The weighting function is based on either the Euclidean distance from the Estimated Regression Line or the deviation angle. Results from RR using deviation angle (RRDA), conventional RR using the Euclidean distance (cRR) and ordinary least-squares Regression (OLS) were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic Flow-Sensitive Dephasing (FSD) datasets, 13 iliac FSD datasets and 26 femoral Fresh Blood Imaging (FBI) datasets. Result: RRDA achieved robust and accurate performance in all types of images, with small bias, small mean absolute error and high correlation coefficients with reference values. Background tissues, such as muscle, veins and bladder, were suppressed while the vascular signal was preserved. cRR gave good performance for thoracic and iliac FSD but could not suppress background tissues in femoral FBI. OLS was sensitive to outliers and overEstimated Regression coefficients in thoracic FSD. Conclusion: Weighted subtraction using RR was able to acquire the Regression coefficients of background signal and improve background suppression of subtractive NCE-MRA techniques. RRDA has the most robust and accurate overall performance among three Regression methods