Clinical Evaluation of Protective Garments with Respect to Garment Characteristics and Manufacturer Label Information.
Chet R. Rees M.D.
Lichliter, A., V. Weir, R. E. Heithaus, S. Gipson, A. Syed, J. West and C. Rees (2016). “Clinical evaluation of protective garments with respect to garment characteristics and manufacturer label information.” J Vasc Interv Radiol: 2016 Oct [Epub ahead of print].
PURPOSE: To test operator exposures inside radiation protection garments in a simulated clinical setup, examining trends related to multiple characteristics. MATERIALS AND METHODS: Sixteen garment models containing lead or nonlead materials and a suspended device (Zero-Gravity) were tested for operator exposure from X rays scattered from an acrylic patient phantom. Weight and surface area were determined. The operator phantom was a wooden frame containing a dosimeter in its cavity. Garments were draped over the frame, and the setup was placed in a typical working position. RESULTS: There was substantial variability in exposures for all garments, ranging from 0.52 to 13.8 microSv/h (mean, 5.39 microSv/h +/- 3.82), with a 12-fold difference for garments labeled 0.5 mm Pb equivalent. Most of the especially poor protectors were nonlead, even when not lightweight. Nonlead models were not more protective per weight overall. For closed-back garments labeled 0.5 mm Pb equivalent, mean exposures were lower for lead than for nonlead materials (mean, 1.48 microSv/h +/- 0.434 vs 6.26 microSv/h +/- 5.13, respectively). Density per exposure-1 was lower for lead than nonlead materials in the 0.5-mm Pb equivalent group, counter to advertised claims. Open-back configurations were lighter than closed (3.3 kg vs 6.0 kg, respectively), with similar mean exposures (5.30 microSv/h vs 5.39 microSv/h, respectively). The lowest exposure was 0.52 microSv/h (9.8% of the mean of all garments) for the suspended device. CONCLUSIONS: Operator exposure in a realistic interventional setup is highly variable for similarly labeled protective garments, highlighting the necessity of internal validation when considering nonlead and lightweight models.