Performance evaluation of a modular gamma camera using a detectability index.
The performance of a modular gamma camera for the task of detecting signals in random noisy backgrounds was evaluated experimentally. The results were compared with a theoretical computer simulation.


METHODS
The camera uses a 10 x 10 cm thallium-doped sodium iodide crystal, a 2 x 2 array of 53 x 53 mm photomultiplier tubes, and a parallel-hole collimator (1.5-mm bore width, 23.6-mm bore length). The camera was positioned to look down into a 10-cm-deep water bath that filled its field of view (FOV). The top surface of the water was 5 cm from the front face of the camera. The camera has 3-mm intrinsic spatial resolution (SR) in the center of its FOV and 9-mm system SR for objects 5 cm below the top surface of the water. Uniform and nonuniform random background data were collected by imaging the bath containing 740 MBq (20 mCi) (99m)Tc. Nonuniformities were created by placing water-filled objects in the bath. Each signal dataset was collected by imaging a water-filled plastic sphere, injected with (99m)Tc and set at a specific depth (Z) in the bath. Data were collected for many signal diameters (D) (4, 7, 10, 13, 16, 28 mm) at 1 depth (5 cm) and for 1 signal diameter (10 mm) at several depths (1, 3, 5, 7, 9 cm). Sets of signal-present/signal-absent image pairs (380 pairs, 10(5) events per image) for known contrasts (C) were generated for use in ideal-observer studies in which the detectability (d') was calculated. Contrast-detail (log C vs. log D) plots were created. The theoretical simulation, developed for uniform backgrounds, provided data for comparison.


RESULTS
The detectability increased linearly with C and decreased nonlinearly with decreasing D or increasing Z. The C required to achieve a specific d' increased sharply for D < SR. For C = 5, D = 10 mm, and d' = 1.2, the camera consistently detected signals for Z < 6 cm. Similar results were found for nonuniform backgrounds. The theoretical simulation verified the results for uniform backgrounds.


CONCLUSIONS
The methodology presented here provides a way of evaluating gamma cameras on the basis of signal-detection performance for specified lesions, with particular application to scintimammography.
Sain
John D
JD
Optical Sciences Center, University of Arizona, Tucson, Arizona, USA. sain1@llnl.gov
Barrett
Harrison H
HH
D000465:Algorithms; D003198:Computer Simulation; D019544:Equipment Failure Analysis / Q000379:methods*; D015902:Gamma Cameras; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D015233:Models, Statistical; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes