SU‐GG‐I‐181: Noise Characterization of a Clinical Flat‐Panel Cone‐Beam Computed Tomography (CBCT) System

S. Vedantham; M. Gounis; M. Van Der Bom; A. Karellas; A. Wakhloo

doi:10.1118/1.3468217

SU‐GG‐I‐181: Noise Characterization of a Clinical Flat‐Panel Cone‐Beam Computed Tomography (CBCT) System

S. Vedantham, M. Gounis, M. Van Der Bom, A. Karellas, A. Wakhloo

Research output: Contribution to journal › Article › peer-review

Abstract

Purpose: To quantitatively characterize the noise performance of a neurointerventional c‐arm based flat‐panel cone‐beam computed tomography (FP‐CBCT) system with an aim of exploring the feasibility of performing cerebral blood volume (CBV) calculations. Method and Materials: Five CBCT scans of ∼11 cm diameter water phantom positioned at the isocenter were acquired at 120kVp and reconstructed to 512³ matrix with 0.33mm voxels. Central Regions of Interest (ROIs) of 193³ matrix corresponding to ∼(63mm)³ were extracted from each scan. Spatial uniformity was determined using the standard deviation (SD) in Hounsfield Units (HU) and repeatability was measured using the root‐mean‐squared deviation (RMSD) of mean signal and using the coefficient of variation (COV) of SD metrics. 3‐D noise power spectra (NPS) were computed with and without subtraction of the average ROI volume from each ROI, which allowed determination of structural noise. Results: Mean signal and SD determined from (193)³ matrix and averaged over the 5 scans were 9.3HU (range: 8.4–10.3; RMSD=0.5) and 19.1HU (range: 19.05–19.15; COV<0.3%). Mean signal determined from (193)² matrix in each axial plane along the cone‐beam direction indicated small variations (< 5HU). NPS analysis indicated high noise power at zero‐frequency, suggestive of aliasing effects. Increased noise power was also observed along the axes, which were not associated with fixed‐pattern structural noise. Conclusion: Variability in mean signal across multiple scans and its difference from theory suggest the need for improved HU calibration. While the observed noise (SD) suggest the potential feasibility of performing CBV calculations with FP‐CBCT, methods for noise reduction may improve the quantitative accuracy of such studies. Supported in part by NIH R01CA128906 and R21EB007767. Contents are solely the responsibility of the authors and do not represent the views of NIH, NCI, or NIBIB. Conflict of Interest: Institutional master research agreement with Philips Healthcare.

Original language	English (US)
Pages (from-to)	3143
Number of pages	1
Journal	Medical physics
Volume	37
Issue number	6
DOIs	https://doi.org/10.1118/1.3468217
State	Published - Jun 2010
Externally published	Yes

ASJC Scopus subject areas

Biophysics
Radiology Nuclear Medicine and imaging

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10.1118/1.3468217

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title = "SU‐GG‐I‐181: Noise Characterization of a Clinical Flat‐Panel Cone‐Beam Computed Tomography (CBCT) System",

abstract = "Purpose: To quantitatively characterize the noise performance of a neurointerventional c‐arm based flat‐panel cone‐beam computed tomography (FP‐CBCT) system with an aim of exploring the feasibility of performing cerebral blood volume (CBV) calculations. Method and Materials: Five CBCT scans of ∼11 cm diameter water phantom positioned at the isocenter were acquired at 120kVp and reconstructed to 5123 matrix with 0.33mm voxels. Central Regions of Interest (ROIs) of 1933 matrix corresponding to ∼(63mm)3 were extracted from each scan. Spatial uniformity was determined using the standard deviation (SD) in Hounsfield Units (HU) and repeatability was measured using the root‐mean‐squared deviation (RMSD) of mean signal and using the coefficient of variation (COV) of SD metrics. 3‐D noise power spectra (NPS) were computed with and without subtraction of the average ROI volume from each ROI, which allowed determination of structural noise. Results: Mean signal and SD determined from (193)3 matrix and averaged over the 5 scans were 9.3HU (range: 8.4–10.3; RMSD=0.5) and 19.1HU (range: 19.05–19.15; COV<0.3%). Mean signal determined from (193)2 matrix in each axial plane along the cone‐beam direction indicated small variations (< 5HU). NPS analysis indicated high noise power at zero‐frequency, suggestive of aliasing effects. Increased noise power was also observed along the axes, which were not associated with fixed‐pattern structural noise. Conclusion: Variability in mean signal across multiple scans and its difference from theory suggest the need for improved HU calibration. While the observed noise (SD) suggest the potential feasibility of performing CBV calculations with FP‐CBCT, methods for noise reduction may improve the quantitative accuracy of such studies. Supported in part by NIH R01CA128906 and R21EB007767. Contents are solely the responsibility of the authors and do not represent the views of NIH, NCI, or NIBIB. Conflict of Interest: Institutional master research agreement with Philips Healthcare.",

author = "S. Vedantham and M. Gounis and {Van Der Bom}, M. and A. Karellas and A. Wakhloo",

year = "2010",

month = jun,

doi = "10.1118/1.3468217",

language = "English (US)",

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T2 - Noise Characterization of a Clinical Flat‐Panel Cone‐Beam Computed Tomography (CBCT) System

AU - Vedantham, S.

AU - Gounis, M.

AU - Van Der Bom, M.

AU - Karellas, A.

AU - Wakhloo, A.

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N2 - Purpose: To quantitatively characterize the noise performance of a neurointerventional c‐arm based flat‐panel cone‐beam computed tomography (FP‐CBCT) system with an aim of exploring the feasibility of performing cerebral blood volume (CBV) calculations. Method and Materials: Five CBCT scans of ∼11 cm diameter water phantom positioned at the isocenter were acquired at 120kVp and reconstructed to 5123 matrix with 0.33mm voxels. Central Regions of Interest (ROIs) of 1933 matrix corresponding to ∼(63mm)3 were extracted from each scan. Spatial uniformity was determined using the standard deviation (SD) in Hounsfield Units (HU) and repeatability was measured using the root‐mean‐squared deviation (RMSD) of mean signal and using the coefficient of variation (COV) of SD metrics. 3‐D noise power spectra (NPS) were computed with and without subtraction of the average ROI volume from each ROI, which allowed determination of structural noise. Results: Mean signal and SD determined from (193)3 matrix and averaged over the 5 scans were 9.3HU (range: 8.4–10.3; RMSD=0.5) and 19.1HU (range: 19.05–19.15; COV<0.3%). Mean signal determined from (193)2 matrix in each axial plane along the cone‐beam direction indicated small variations (< 5HU). NPS analysis indicated high noise power at zero‐frequency, suggestive of aliasing effects. Increased noise power was also observed along the axes, which were not associated with fixed‐pattern structural noise. Conclusion: Variability in mean signal across multiple scans and its difference from theory suggest the need for improved HU calibration. While the observed noise (SD) suggest the potential feasibility of performing CBV calculations with FP‐CBCT, methods for noise reduction may improve the quantitative accuracy of such studies. Supported in part by NIH R01CA128906 and R21EB007767. Contents are solely the responsibility of the authors and do not represent the views of NIH, NCI, or NIBIB. Conflict of Interest: Institutional master research agreement with Philips Healthcare.

AB - Purpose: To quantitatively characterize the noise performance of a neurointerventional c‐arm based flat‐panel cone‐beam computed tomography (FP‐CBCT) system with an aim of exploring the feasibility of performing cerebral blood volume (CBV) calculations. Method and Materials: Five CBCT scans of ∼11 cm diameter water phantom positioned at the isocenter were acquired at 120kVp and reconstructed to 5123 matrix with 0.33mm voxels. Central Regions of Interest (ROIs) of 1933 matrix corresponding to ∼(63mm)3 were extracted from each scan. Spatial uniformity was determined using the standard deviation (SD) in Hounsfield Units (HU) and repeatability was measured using the root‐mean‐squared deviation (RMSD) of mean signal and using the coefficient of variation (COV) of SD metrics. 3‐D noise power spectra (NPS) were computed with and without subtraction of the average ROI volume from each ROI, which allowed determination of structural noise. Results: Mean signal and SD determined from (193)3 matrix and averaged over the 5 scans were 9.3HU (range: 8.4–10.3; RMSD=0.5) and 19.1HU (range: 19.05–19.15; COV<0.3%). Mean signal determined from (193)2 matrix in each axial plane along the cone‐beam direction indicated small variations (< 5HU). NPS analysis indicated high noise power at zero‐frequency, suggestive of aliasing effects. Increased noise power was also observed along the axes, which were not associated with fixed‐pattern structural noise. Conclusion: Variability in mean signal across multiple scans and its difference from theory suggest the need for improved HU calibration. While the observed noise (SD) suggest the potential feasibility of performing CBV calculations with FP‐CBCT, methods for noise reduction may improve the quantitative accuracy of such studies. Supported in part by NIH R01CA128906 and R21EB007767. Contents are solely the responsibility of the authors and do not represent the views of NIH, NCI, or NIBIB. Conflict of Interest: Institutional master research agreement with Philips Healthcare.

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SU‐GG‐I‐181: Noise Characterization of a Clinical Flat‐Panel Cone‐Beam Computed Tomography (CBCT) System

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