Automated phantom analysis for gamma cameras and SPECT: A methodology for use in a clinical setting

We introduce an automated, quantitative image analysis package for gamma camera and single photon emission computed tomography quality control. Our focus was to produce consistent methods that are feasible in clinical settings and use standard phantoms.

Four gamma cameras were used to acquire planar images of four-quadrant bar phantoms and projection views of an American College of Radiology (ACR) phantom as part of a standard gamma camera quality control program. Images were sent to QC-Track® (Atirix Medical Systems, Inc., Minneapolis, MN, USA), which automatically placed predetermined regions of interest (ROIs) and performed analysis. For the bar phantom, a standard deviation (SD)-based modulation transfer function was calculated for a circular ROI in each quadrant. The bar widths at various MTF values were reported using linear interpolation as applicable. For the ACR phantom, the contrast-to-noise ratio (CNR) for each sphere, a modulation for each rods section, and a percent deviation for uniformity ROIs was calculated. Spheres corresponding to a CNR of 3, and the rod size at various modulations were also reported using linear interpolation. Visual analysis was performed by three medical physicists to evaluate interobserver variability and correlation to quantitative values.

Analysis of the bar phantom showed predictable differences with changes in matrix size and bar width and showed consistency over similar acquisitions over the course of the study. Analysis of the ACR Phantom showed increasing CNR and modulation with increasing sphere and rod diameter, as expected. For both phantoms, quantitative values from linear interpolation correlated well with visual analysis.

Our automated method for quantitative image analysis is consistent and shows increased precision and sensitivity when compared to standard visual methods. Thresholds correspond well with visual analysis and previous guidelines for observer visibility (e.g., Rose criterion), making our framework suitable for routine use in a nuclear medicine department.