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A novel method to evaluate gamma camera rotational uniformity and sensitivity variation
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10.1118/1.3125642
/content/aapm/journal/medphys/36/6/10.1118/1.3125642
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/6/10.1118/1.3125642

Figures

Image of FIG. 1.
FIG. 1.

The source-to-detector distances estimated from the curvature-correction fit to the point-source image plotted against the true distances at four different distances for two different detectors.

Image of FIG. 2.
FIG. 2.

The maximum sensitivity variation calculated with the point-source method plotted against that for the conventional method. The error bars shown are the precision estimates for each of the two methods (Sec. III C and Table I). (a) Paired points for the measured data from nine different detectors. The dashed line plotted is the line of equality with slope of unity and intercept of zero. (b) Paired points for the measured data combined with simulated data. The dashed line plotted is the fit of the data to a straight line.

Image of FIG. 3.
FIG. 3.

A Bland-Altman plot of agreement in MSV calculated with both point-source and conventional methods. The graph plots the mean of the two measurements as the abscissa and the difference between them (point-source minus conventional) as the ordinate. The data plotted include MSV for the nine detectors (identified by square boxes) and the simulated data. The mean difference (bias) in the point-source method and the SD limits are also indicated.

Image of FIG. 4.
FIG. 4.

A demonstration of the curvature-correction algorithm for a near-field point-source image as developed in the Appendix: (a) The uncorrected raw image, (b) the corresponding curvature-corrected image generated after fitting the image to the point-source image model, and (c) the center profiles through the raw and the curvature-corrected images.

Image of FIG. 5.
FIG. 5.

A demonstration of the use of curvature-corrected images for the qualitative inspection of structured, nonrandom patterns in subtracted images from two detectors. Note that neither detector has any real artifacts that should be seen. The differences (0° image subtracted from the 90°, 180°, 270°, and 360° images) for the point-source method are shown: [(a) and (c)] Without curvature correction prior to subtraction and [(b) and (d)] with curvature correction prior to subtraction.

Image of FIG. 6.
FIG. 6.

A schematic of the point-source imaging geometry for the proposed point-source methodology for testing rotational uniformity and sensitivity variations. Distances and variables shown are discussed at depth in the Appendix. The distances are not shown to scale but adjusted for clarity (e.g., ).

Tables

Generic image for table
TABLE I.

The five separate and independent measurements of the maximum sensitivity variation (MSV%) for the same detector using both point-source and the conventional methods together with their mean values, SD, and coefficient of variation (COV).

Generic image for table
TABLE II.

The ten measurements of the maximum sensitivity variation (MSV%) for six different detectors [A–D: 9.5 mm crystal; E–F: 15.9 mm crystals] using the point-source method together with their mean values, SD, COV, variance, minimum, and maximum values.

Generic image for table
TABLE III.

The maximum sensitivity variation (MSV%) for two different detectors (A and B) with the point-source method using image sequences with , , and counts per image. The SD and range of MSV calculated are also shown.

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/content/aapm/journal/medphys/36/6/10.1118/1.3125642
2009-05-05
2014-04-23
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A novel method to evaluate gamma camera rotational uniformity and sensitivity variation
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/6/10.1118/1.3125642
10.1118/1.3125642
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