A Monte Carlo study of grid performance in diagnostic radiology: task-dependent optimization for digital imaging.
A Monte Carlo computational model has been used to optimize grid design in digital radiography. The optimization strategy involved finding grid designs that, for a constant signal-to-noise ratio, resulted in the lowest mean absorbed dose in the patient. Different examinations were simulated to explore the dependence of the optimal scatter-rejection technique on the imaging situation. A large range of grid designs was studied, including grids with both aluminium and fibre interspaces and covers, and compared to a 20 cm air gap. The results show that the optimal tube potential in each examination does not depend strongly on the scatter-rejection technique. There is a significant dose reduction associated with the use of fibre-interspaced grids, particularly in paediatric radiography. The optimal grid ratio and strip width increase with increasing scattering volume. With increasing strip density, the optimal strip width decreases, and the optimal grid ratio increases. Optimal grid ratios are higher than those used today, particularly for grids with large strip density. It is, however, possible to identify grids of good performance for a range of strip densities and grid ratios provided the strip width is selected accordingly. The computational method has been validated by comparison with measurements with a caesium iodide image receptor.
Sandborg
M
M
Department of Radiation Physics, Faculty of Health Sciences, Linköping University, S-581 85 Linköping, Sweden.
Dance
D R
DR
Carlsson
G A
GA
Persliden
J
J
Tapiovaara
M J
MJ
D000465:Algorithms; D015233:Models, Statistical; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted