Simulation of MRI cluster plots and application to neurological segmentation.

The advent of magnetic resonance imaging has provided new opportunities for volume measurement of tissues, with applications increasing dramatically in recent years. Cluster classification techniques have proved the most popular for volume measurement, yet little attention has been paid to how the choice of images for analysis affects the quality and ease of segmentation. To address this issue, we have developed a system to simulate MRI cluster plots using multicompartmental anthropomorphic software models of anatomy, and components for image contrast, signal-to-noise ratio, image nonuniformity, tissue heterogeneity, imager field strength, the partial volume effect, correlation between proton density, T1 and T2, and a variety of data preprocessing techniques. The effect of these components on tissue cluster size, shape, orientation, and separation is demonstrated. The simulation allows an informed choice of pulse sequence, acquisition parameters, and data preprocessing for cluster classification to be made as well as providing an aid to interpretation of acquired data cluster plots and a valuable educational tool. The system has been used to choose suitable images for neurological segmentation of grey matter, white matter, CSF, and multiple sclerosis lesions using spin-echo, inversion recovery, and gradient-echo pulse sequences. Constraints on image selection are discussed.

[1]  Ray Freeman,et al.  Fourier Transform Study of NMR Spin-Lattice Relaxation by , 1971 .

[2]  A. Elster,et al.  Gradient-echo MR imaging: techniques and acronyms. , 1993, Radiology.

[3]  R. Lufkin,et al.  Dynamic-range compression in surface-coil MRI. , 1986, AJR. American journal of roentgenology.

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[6]  V. Abeler,et al.  Multispectral analysis of uterine corpus tumors in magnetic resonance imaging , 1992, Magnetic resonance in medicine.

[7]  P. M. Walker,et al.  V. Multi-center trial with protocols and prototype test objects for the assessment of MRI equipment , 1988 .

[8]  S. Holland.  MRI: Acceptance testing and quality control—The role of the clinical medical physicist , 1989 .

[9]  J V Hajnal,et al.  MR of the brain using fluid-attenuated inversion recovery (FLAIR) pulse sequences. , 1992, AJNR. American journal of neuroradiology.

[10]  J C Gore,et al.  Factors influencing contrast in fast spin-echo MR imaging. , 1992, Magnetic resonance imaging.

[11]  M. Bronskill,et al.  Phase and sensitivity of receiver coils in magnetic resonance imaging. , 1986, Medical physics.

[12]  Guido Gerig,et al.  Nonlinear anisotropic filtering of MRI data , 1992, IEEE Trans. Medical Imaging.

[13]  Paul S. Tofts,et al.  Standing Waves in Uniform Water Phantoms , 1994 .

[14]  A. Alavi,et al.  Analysis of brain and cerebrospinal fluid volumes with MR imaging. Part I. Methods, reliability, and validation. , 1991, Radiology.

[15]  J. B. Kneeland,et al.  Effect of intersection spacing on MR image contrast and study time. , 1986, Radiology.

[16]  D. MacManus,et al.  Accuracy and precision in the measurement of relaxation times from nuclear magnetic resonance images. , 1987, The British journal of radiology.

[17]  Daniel Canet,et al.  Frequency dependence of water proton longitudinal nuclear magnetic relaxation times in mouse tissues at 20°C , 1982 .

[18]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[19]  J Hennig,et al.  RARE imaging: A fast imaging method for clinical MR , 1986, Magnetic resonance in medicine.

[20]  Guido Gerig,et al.  Automating Segmentation of Dual-Echo MR Head Data , 1991, IPMI.

[21]  G. Barker,et al.  Correction of intensity nonuniformity in MR images of any orientation. , 1993, Magnetic resonance imaging.

[22]  R V Mulkern,et al.  Magnetization transfer effects in multislice RARE sequences , 1992, Magnetic resonance in medicine.

[23]  P A Rinck,et al.  Nuclear relaxation of human brain gray and white matter: Analysis of field dependence and implications for MRI , 1990, Magnetic resonance in medicine.

[24]  S R Arridge,et al.  A method for visualization of MRI partial volume regions--PAIR (PArtial volume sensitised Inversion Recovery imaging). , 1994, Magnetic resonance imaging.

[25]  M W Vannier,et al.  Multispectral analysis of MR images of the breast. , 1987, Radiology.

[26]  M Cortsen,et al.  In vivo field dependence of proton relaxation times in human brain, liver and skeletal muscle: a multicenter study. , 1993, Magnetic resonance imaging.

[27]  G. Krone,et al.  Information processing in nuclear magnetic resonance imaging. , 1988, Magnetic resonance imaging.

[28]  R L Ehman,et al.  Reproducibility of relaxation times and spin density calculated from routine MR imaging sequences: clinical study of the CNS. , 1985, AJR. American journal of roentgenology.

[29]  R Kikinis,et al.  3D surface rendered MR images of the brain and its vasculature. , 1991, Journal of computer assisted tomography.

[30]  L. Axel,et al.  Intensity correction in surface-coil MR imaging. , 1987, AJR. American journal of roentgenology.

[31]  J N Lee,et al.  Automated MR image synthesis: feasibility studies. , 1984, Radiology.

[32]  F A Jolesz,et al.  Simultaneous acquisition of proton density, T1, and T2 images with triple contrast RARE sequence. , 1993, Journal of computer assisted tomography.

[33]  W. Edelstein,et al.  The intrinsic signal‐to‐noise ratio in NMR imaging , 1986, Magnetic resonance in medicine.

[34]  F Podo,et al.  Analysis of the first international data bank on in vitro NMR relaxation times: animal liver. , 1993, Magnetic Resonance Imaging.

[35]  J. Haselgrove,et al.  An algorithm for compensation of surface-coil images for sensitivity of the surface coil , 1986 .

[36]  D Wyper,et al.  MR Relaxation Times of Cerebrospinal Fluid , 1987, Journal of computer assisted tomography.

[37]  T. Foster,et al.  A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. , 1984, Medical physics.

[38]  L P Clarke,et al.  Composite and classified color display in MR imaging of the female pelvis. , 1992, Magnetic resonance imaging.

[39]  W. Tourtellotte,et al.  Some spaces and barriers in postmortem multiple sclerosis. , 1968, Progress in brain research.

[40]  L. Pitts,et al.  Nuclear magnetic resonance imaging and spectroscopy in experimental brain edema in a rat model. , 1986, Journal of neurosurgery.

[41]  B. Condon,et al.  Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction. , 1987, The British journal of radiology.

[42]  R. L. Butterfield,et al.  Multispectral analysis of magnetic resonance images. , 1985, Radiology.

[43]  E. Hahn,et al.  An Accurate Nuclear Magnetic Resonance Method for Measuring Spin-Lattice Relaxation Times , 1949 .

[44]  J. Groen,et al.  Fast Field Echo imaging: an overview and contrast calculations. , 1988, Magnetic resonance imaging.

[45]  G J Barker,et al.  Correction of Nonuniformity in Images of the Spine and Optic Nerve from Fixed Receive‐Only Surface Coils at 1.5 T , 1994, Journal of computer assisted tomography.

[46]  S. Arridge,et al.  Sources of intensity nonuniformity in spin echo images at 1.5 T , 1994, Magnetic resonance in medicine.

[47]  K. Lim,et al.  Segmentation of MR Brain Images into Cerebrospinal Fluid Spaces, White and Gray Matter , 1989, Journal of computer assisted tomography.

[48]  R L Kamman,et al.  Nuclear magnetic resonance relaxation in experimental brain edema: Effects of water concentration, protein concentration, and temperature , 1988, Magnetic resonance in medicine.

[49]  N M Hylton,et al.  Signal to Noise in Derived NMR Images , 1984, Magnetic resonance in medicine.

[50]  J. Olesen,et al.  In vivo determination of T1 and T2 in the brain of patients with severe but stable multiple sclerosis , 1988, Magnetic resonance in medicine.

[51]  J N Lee,et al.  Synthesized MR images: comparison with acquired images. , 1985, Radiology.

[52]  J N Lee,et al.  Cerebral magnetic resonance image synthesis. , 1985, AJNR. American journal of neuroradiology.

[53]  P B Hoffer,et al.  Computerized three-dimensional segmented human anatomy. , 1994, Medical physics.

[54]  G. C. Levy,et al.  Characterization of normal brain tissue using seven calculated MRI parameters and a statistical analysis system , 1989, Magnetic resonance in medicine.

[55]  R P Velthuizen,et al.  MRI: stability of three supervised segmentation techniques. , 1993, Magnetic resonance imaging.

[56]  R. Kikinis,et al.  Three-dimensional segmentation of MR images of the head using probability and connectivity. , 1990, Journal of computer assisted tomography.

[57]  L Kaufman,et al.  Analytical tools for magnetic resonance imaging. , 1984, Radiology.

[58]  M. Imme.  A noise peak elimination filter , 1991, CVGIP Graph. Model. Image Process..

[59]  G J Barker,et al.  Improvements to the quality of MRI cluster analysis. , 1994, Magnetic resonance imaging.

[60]  G Johnson,et al.  Precise relaxation time measurements of normal‐appearing white matter in inflammatory central nervous system disease , 1989, Magnetic resonance in medicine.