Fifty-sixth annual meeting of the American association of physicists in medicine
SU-E-I-40: New Method for Measurement of Task-Specific, High-Resolution Detector System Performance
Although generalized linear system analytic metrics such as GMTF and GDQE can evaluate performance of the whole imaging system including detector, scatter and focal-spot, a simplified task-specific measured metric may help to better compare detector systems.
Low quantum-noise images of a neuro-vascular stent with a modified ANSI head phantom were obtained from the average of many exposures taken with the high-resolution Micro-Angiographic Fluoroscope (MAF) and with a Flat Panel Detector (FPD). The square of the Fourier Transform of each averaged image, equivalent to the measured product of the system GMTF and the object function in spatial-frequency space, was then divided by the normalized noise power spectra (NNPS) for each respective system to obtain a task-specific generalized signal-to-noise ratio. A generalized measured relative object detectability (GM-ROD) was obtained by taking the ratio of the integral of the resulting expressions for each detector system to give an overall metric that enables a realistic systems comparison for the given detection task.
The GM-ROD provides comparison of relative performance of detector systems from actual measurements of the object function as imaged by those detector systems. This metric includes noise correlations and spatial frequencies relevant to the specific object. Additionally, the integration bounds for the GM-ROD can be selected to emphasis the higher frequency band of each detector if high-resolution image details are to be evaluated. Examples of this new metric are discussed with a comparison of the MAF to the FPD for neuro-vascular interventional imaging.
The GM-ROD is a new direct-measured task-specific metric that can provide clinically relevant comparison of the relative performance of imaging systems.
Supported by NIH Grant: 2R01EB002873 and an equipment grant from Toshiba Medical Systems Corporation