Accuracy Assessment of SUV Measurements in SPECT/CT: A Phantom Study
Advances in iterative image reconstruction enable absolute quantification of SPECT/CT studies by incorporating compensations for collimator-detector response, attenuation, and scatter. This study aimed to assess the quantitative accuracy of SPECT/CT based on different levels of Tc activity (low/high...
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Veröffentlicht in: | Journal of nuclear medicine technology 2021-09, Vol.49 (3), p.250-255 |
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Sprache: | eng |
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Zusammenfassung: | Advances in iterative image reconstruction enable absolute quantification of SPECT/CT studies by incorporating compensations for collimator-detector response, attenuation, and scatter. This study aimed to assess the quantitative accuracy of SPECT/CT based on different levels of
Tc activity (low/high) using different SUV metrics (SUV
, SUV
, SUV
, and SUV
[the average values that include pixels greater than 60% and 75% of the SUV
in the volume of interest, respectively]).
A Jaszczak phantom equipped with 6 fillable spheres was set up with low and high activity ratios of 1:4 and 1:10 (background-to-sphere) on background activities of 10 and 60 kBq/mL, respectively. The fixed-size volume of interest based on the diameter of each sphere was drawn on SPECT images using various metrics for SUV quantification purposes.
The convergence of activity concentration was dependent on the number of iterations and application of postfiltering. For the background-to-sphere ratio of 1:10 with a low background activity concentration, the SUV
metric showed an underestimation of about 38% from the actual SUV, and SUV
exhibited an overestimation of about 24% for the largest sphere diameter. Meanwhile, bias reductions of as much as -6% and -7% for SUV
and SUV
, respectively, were observed. SUV
gave a more accurate reading than the others, although points that exceeded the actual value were detected. At 1:4 and 1:10 background activity of 10 kBq/mL, a low activity concentration attained a value close to the actual ratio. Use of 2 iterations and 10 subsets without postfiltering gave the most accurate values for reconstruction and the best image overall.
SUV
is the best metric in a high- or low-contrast-ratio phantom with at least 2 iterations, 10 subsets, and no postfiltering. |
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ISSN: | 0091-4916 1535-5675 |
DOI: | 10.2967/jnmt.120.259168 |