1 / 10

By Eric Sorensen, Larry Zeng

Comparison of Planar and SPECT Images For Lesion Detection Using Channelized Hotelling Observer and Human Observers. By Eric Sorensen, Larry Zeng. High noise 3D Better Contrast. Status quo Low noise 2D. SPECT vs. Planar.

adlai
Télécharger la présentation

By Eric Sorensen, Larry Zeng

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Comparison of Planar and SPECT Images For Lesion Detection Using Channelized Hotelling Observer and Human Observers By Eric Sorensen, Larry Zeng

  2. High noise 3D Better Contrast Status quo Low noise 2D SPECT vs. Planar We hope to demonstrate that SPECT imaging yields images with a higher lesion delectability using both Human Observes and Channelized Hotelling Observers SPECT PLANAR

  3. Human Observer (HO) • Accurate assessment of image quality • Subjects rate images on the probability that specific signal is present • Lesion location is known to the subject • Rating scale [1:10] • Random image order • We are currently developing software program to carry out the test and record results • Using the accuracy of Human Observer a comparison between the two modalities can be achieved along with comparison of Channelized Hotelling Observer • Start planning your excuses now, we might ask YOU to be an observer Is there a lesion or isn’t there???

  4. Spatial-frequency selective channels Models human visual system Information within frequency bands is summed Observer “assessment” calculated from feature vector statistics v = Ug CHO = (oCHO)Tv v : feature vector (Lx1) U : channel operator (LxN) oCHO : observer vector (Lx1) Channelized Hotelling Observer (CHO) An example of a Hotelling Channel Operator

  5. Image Generation • NCAT phantom • Projection using GPRC and scatter modelling • Poisson noise • Corrected to simulate clinical scanning times • SPECT images reconstructed using OSEM algorithm with both GPRC and scatter correction • Planar images generated with a single projection

  6. PLANAR 20 Lesion Locations 3 Noise Levels 4 Contrast Levels 2 Phantom Models (Man and Woman) 5 Noise seeds 2400 TOTAL IMAGES  19megs Number of Images Generated • SPECT • 20 Lesion Locations • 3 Noise Levels • 4 Contrast Levels • 2 Phantom Models (Man and Woman) • 5 Noise seeds • 5 Different Iterations • 12000 TOTAL IMAGES  98megs

  7. Results Of Channelized Hotelling Observer • Comparison of SPECT vs. Planar • NOT SO GOOD • Planar images show higher  or rating value for most scenarios • Planar images much less sensitive to noise and contrast • Generally  decreases as contrast decrease • Generally  increases with decreasing noise levels 0.75 SPECT Planar 0.7 0.65 LAMBDA 0.6 0.55 1 2 3 4 5 6 7 8 9 TRIAL NOISE LEVEL H M L H M L H M L CONTRAST H H H M M M L L L

  8. Conclusion and Further Work • For the time being Planar images are “winning” • Human Observer Study still in development • Need to find optimal channels for the Channelized Hotelling Observer • Using Observers find in what locations SPECT outperforms planar imaging

  9. Special Thanks To…. • Scott Karen  Image Generation • Todd Ovard  Image Generation and SPECT CHO data • Ken Scott  Image Generation • Ben Holt  Initial Human Observer Software Development • Larry Zeng  Guidance, Wisdom and for being such a incomparable WUSS • All the girls I’ve loved before

  10. Fin

More Related