Charles R. Meyer, Ph.D. [1],
Peyton H. Bland, Ph.D. [1],
James Pipe, Ph.D. [1,2]
[1] Department of Radiology, University of Michigan, Ann Arbor, MI
[2] Currently: Harper Hospital, Wayne State University, Detroit, MI
Selected figures from the text...
Fig. 1. (54,977 bytes, b/w)
Images obtained using a flexible surface coil for MRI
breast imaging on a 1.5 T, GE Signa scanner. Uncorrected (left
column), LCJ segmented (middle column), and corrected (right column)
images are presented at a superior level of the breast (top row),
midbreast (middle row), and inferior level of the breast (bottom row).
Images in left and right columns are presented at identical contrast
settings.
Fig. 2. (30,110 bytes, color)
Axial MRI data sets acquired using a 1.5 T GE Signa
using a "bird cage" rf head coil; patient has a large left hemisphere
infarct. Three rows represent superior (top row), midbrain (middle
row, and base of cerebellum (lower row). Columns left of center are
associated with original data acquisition, and columns right of center
are associated with the corrected data. The central column displays
the difference between the two adjacent columns: immediately to the
left is the uncorrected, acquired, T1-weighted image set, while
immediately to the right of center is the corrected T1-weighted set.
The window/level settings for the three central gray scale columns are
exactly the same, with the exception that the level of the difference
images has been adjusted such that zero difference yields black
(cursor, central image). A narrow window was chosen to emphasize
differences. The outermost two columns display cluster labels
corresponding to "gray matter" (red) and CSF (green) obtained from a
global trivariate, histogram-based clustering (HICAP) of the
uncorrected (left-most column) and corrected (right-most column) data
volumes.
Fig. 3. (40,133 bytes, color)
Images obtained using a flexible surface coil for MRI
breast imaging on a 1.5 T, GE Signa scanner. The left column is from
the original data set, the middle column is the result of the
correction from Fig. 1, and the right column is an improved correction
as the result of merging clusters in the modeling process. Gray scale
images (top row) and corresponding grayscale coded uniformly
thresholded version (bottom row). In the original LCJ algorithm the
number of separate segments was determined by spatial separation, i.e.
each volume hypothesis that passed the uniformity check was assigned a
separate volume identification number for use in computing the
correction polynomial. Realizing that some larger volumes are
artificially divided into smaller ones by the segmentation process, we
noted that the stability of the correction algorithm could be improved
by merging segments corresponding to the same tissues and thus
reducing the degrees of freedom for the same mvo model. A
retrospective merging of segments was performed by combining segments
whose model constants differed by less than 2 times the model's
standard error of the estimate, followed again by computation of the
correction polynomial. The improved uniformity obtained in both
breasts is obvious in the right column's images. The grayscale images
of Fig. 3 are displayed at a higher contrast setting than that used
for Fig. 1.
Meyer, C.R., P.H. Bland, and J. Pipe: Retrospective correction of MRI amplitude inhomogeneities. Proceedings of CVRMed'95, Nice, FR, in Lecture Notes in Computer Science (1995: Springer-Verlag, Berlin) 905:513-522.
(See also the abstract or an overview on retrospective correction.)
This work was supported in part by DHHS PHS grants NIH 1R01CA52709 and 1F32CA09309.