Reducing Variability in Orthogonal Reformatted Image Quality Associated With Axial Long-z-Axis CT Angiography.

Abstract

The objective of our study was to reduce variation in image quality of orthogonal reformatted images generated from long-z-axis CT angiography (CTA) studies of the upper and lower extremities. Upper and lower extremity CTA studies were targeted at a single health care system. A correctly performed CTA examination was defined as one that met the following three criteria: Sagittal and coronal reformats were obtained, a high-resolution matrix greater than 512 × 512 was used, and reformatted images were available in a distance-measurable format. Baseline data were collected from February 1, 2014, through September 30, 2014. Corrective actions were implemented during three consecutive plan-do-check-act (PDCA) cycles from October 1, 2014, through July 31, 2015, that addressed human, technical, and systematic variations. A 3-month maintenance period followed in which no intervention was performed. Longitudinal data were analyzed monthly using a statistical process control chart (p-chart). The total number of long-z-axis extremity CTA studies analyzed was as follows: 351 CTA studies were analyzed at baseline, 94 at the first PDCA cycle, 92 at the second PDCA cycle, 114 at the third PDCA cycle, and 138 during the maintenance period. The monthly rate of correctly performed studies ranged from 7% to 51% (mean, 38% ± 13% [SD]) during the baseline period, 32-59% (mean, 46% ± 14%) during the first PDCA cycle, 40-81% (mean, 61% ± 21%) during the second PDCA cycle, and 80-82% (mean, 81% ± 0.9%) during the third PDCA cycle. The monthly rate improved to 90-91% (mean, 91% ± 0.5%) during the maintenance period. The upper and lower control limits of the p-chart were upshifted after the second and third PDCA cycles. Correcting systematic and technical variations led to the greatest improvements in reformat accuracy. Obtaining consistently and correctly reformatted images from long-z-axis CTA studies is achievable using iterative PDCA cycles.