RT Journal Article
SR Electronic
T1 Noise Reduction in Oncology FDG PET Images by Iterative Reconstruction: A Quantitative Assessment
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1316
OP 1323
VO 42
IS 9
A1 Cyril Riddell
A1 Richard E. Carson
A1 Jorge A. Carrasquillo
A1 Steven K. Libutti
A1 David N. Danforth
A1 Millie Whatley
A1 Stephen L. Bacharach
YR 2001
UL http://jnm.snmjournals.org/content/42/9/1316.abstract
AB Tumor detection depends on the contrast between tumor activity and background activity and on the image noise in these 2 regions. The lower the image noise, the easier the tumor detection. Tumor activity contrast is determined by physiology. Noise, however, is affected by many factors, including the choice of reconstruction algorithm. Previous simulation and phantom measurements indicated that the ordered-subset expectation maximization (OSEM) algorithm may produce less noisy images than does the usual filtered backprojection (FBP) method, at equivalent resolution. To see if this prediction would hold in actual clinical situations, we quantified noise in clinical images reconstructed with both OSEM and FBP. Methods: Three patients (2 with colon cancer, 1 with breast cancer) were imaged with FDG PET using a “gated replicate” technique that permitted accurate measurement of noise at each pixel. Each static image was acquired as a gated image sequence, using a pulse generator with a 1-s period, yielding 40 replicate images over the 10- to 15-min imaging time. The images were or were not precorrected for attenuation and were reconstructed with both FBP and OSEM at comparable resolution. From these data, images of pixel mean, SD, and signal-to-noise ratio (S/N) could be produced, reflecting only noise caused by the statistical fluctuations in the emission process. Results: Noise did not vary greatly over each FBP image, even when image intensity varied greatly from one region to the next, causing S/N to be worse in low-activity regions than in high-activity regions. In contrast, OSEM had high noise in hot regions and low noise in cold regions. OSEM had a much better S/N than did FBP in cold regions of the image, such as the lungs (in the attenuation-corrected images), where improvements in S/N averaged 160%. Improvements with OSEM were less dramatic in hotter areas such as the liver (averaging 25% improvement in the attenuation-corrected images). In very hot tumors, FBP actually produced higher S/Ns than did OSEM. Conclusion: We conclude that OSEM reconstruction can significantly reduce image noise, especially in relatively low-count regions. OSEM reconstruction failed to improve S/N in very hot tumors, in which S/N may already be adequate for tumor detection.