TY - GEN
T1 - Retrospective correction of MRI amplitude inhomogeneities
AU - Meyer, Charles R.
AU - Bland, Peyton H.
AU - Pipe, James
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1995.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 1995
Y1 - 1995
N2 - MRI data sets are corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The use of adiabatic pulses can remove the inhomogeneity effects on transmit, but coil and patient parameters still affect reception. We describe an automatic technique that not only improves the worst corruptions such as those introduced by surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets such as head scans without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects such as rf coil design, and patient dependent effects such as tissue attenuation and dielectric-induced resonances exper! enced in high field MRI. Patient dependent attenuation effects are also encountered in x-ray computed tomography. All of the above are examples of multiplicative inhomogeneities which result in low spatial frequency corruption of acquired volume data sets. While we concentrate on MR in the remainder of the paper, the algorithms and techniques described are directly applicable to CT as well. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation and resonance effects. Key Words: Inhomogeneity correction, intensity correction, background correction, uniformity correction, retrospective, image processing.
AB - MRI data sets are corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The use of adiabatic pulses can remove the inhomogeneity effects on transmit, but coil and patient parameters still affect reception. We describe an automatic technique that not only improves the worst corruptions such as those introduced by surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets such as head scans without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects such as rf coil design, and patient dependent effects such as tissue attenuation and dielectric-induced resonances exper! enced in high field MRI. Patient dependent attenuation effects are also encountered in x-ray computed tomography. All of the above are examples of multiplicative inhomogeneities which result in low spatial frequency corruption of acquired volume data sets. While we concentrate on MR in the remainder of the paper, the algorithms and techniques described are directly applicable to CT as well. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation and resonance effects. Key Words: Inhomogeneity correction, intensity correction, background correction, uniformity correction, retrospective, image processing.
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U2 - 10.1007/978-3-540-49197-2_68
DO - 10.1007/978-3-540-49197-2_68
M3 - Conference contribution
AN - SCOPUS:84956864766
SN - 9783540591207
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 513
EP - 522
BT - Computer Vision, Virtual Reality and Robotics in Medicine - 1st International Conference, CVRMed 1995, Proceedings
A2 - Ayache, Nicholas
PB - Springer Verlag
T2 - 1st International Conference on Computer Vision, Virtual Reality, and Robotics in Medicine, CVRMed 1995
Y2 - 3 April 1995 through 6 April 1995
ER -