Physical validation of UF‐RIPSA: A rapid in‐clinic peak skin dose mapping algorithm for fluoroscopically guided interventions

Abstract

PurposeThe purpose of this study was to experimentally validate UF‐RIPSA, a rapid in‐clinic peak skin dose mapping algorithm developed at the University of Florida using optically stimulated luminescent dosimeters (OSLDs) and tissue‐equivalent phantoms.MethodsThe OSLDs used in this study were InLightTM Nanodot dosimeters by Landauer, Inc. The OSLDs were exposed to nine different beam qualities while either free‐in‐air or on the surface of a tissue equivalent phantom. The irradiation of the OSLDs was then modeled using Monte Carlo techniques to derive correction factors between free‐in‐air exposures and more complex irradiation geometries. A grid of OSLDs on the surface of a tissue equivalent phantom was irradiated with two fluoroscopic x ray fields generated by the Siemens Artis zee bi‐plane fluoroscopic unit. The location of each OSLD within the grid was noted and its dose reading compared with UF‐RIPSA results.ResultsWith the use of Monte Carlo correction factors, the OSLD's response under complex irradiation geometries can be predicted from its free‐in‐air response. The predicted values had a percent error of −8.7% to +3.2% with a predicted value that was on average 5% below the measured value. Agreement within 9% was observed between the values of the OSLDs and RIPSA when irradiated directly on the phantom and within 14% when the beam first traverses the tabletop and pad.ConclusionsThe UF‐RIPSA only computes dose values to areas of irradiated skin determined to be directly within the x ray field since the algorithm is based upon ray tracing of the reported reference air kerma value, with subsequent corrections for air‐to‐tissue dose conversion, x ray backscatter, and table/pad attenuation. The UF‐RIPSA algorithm thus does not include the dose contribution of scatter radiation from adjacent fields. Despite this limitation, UF‐RIPSA is shown to be fairly robust when computing skin dose to patients undergoing fluoroscopically guided interventions.

Review of skin dose calculation software in interventional cardiology

“…In the definition of their calculation formula, Jones and Pasciak [30] did not include the scatter radiation from adjacent field. Neither does any of the reviewed software; however, it has a very limited influence [20].…”

Feasibility of dosimetric measurements using Al2O3:C OSL dosimeter during fluoroscopy-guided procedures

“…However, using cumulative air kerma as a surrogate for PSD is limited because it is estimated without considering the height of the table, distribution of the radiation dose caused by tube angulation, attenuation by the table, and backscatter radiation [8,9,46]. Recently, skin dose mapping software have been studied vigorously and several studies have reported promising results [7,[10][11][12][13][14][15]47]. It has the advantage of providing PSD and dose map is automatically calculated considering distribution of the radiation dose without interfering with the clinical practice.…”

Validation of a dose tracking software for skin dose map calculation in interventional radiology

“…In the literature [52], there are some analyses on dosimetric outcomes. For different software applications validated on phantoms (PMMA, water or anthropomorphic phantom), the differences between calculated and measured doses range from 5% to 25% [27][28][29][32][33][34][36][37][38]. Most of these validations were performed only with a single field or in simple setups, for example using no more than four irradiation events and a limited number of beam projections, except for two recent studies that described a complete validation of their software [29,32].…”

“…However, in those centers where software for skin dose map evaluation software is not available or for older equipment, K a,r and KAP remain useful PSD estimators [19]. It is possible to evaluate the PSD through different methods of direct measurements including thermoluminescent dosimeters (TLDs) [20], semiconductor dosimeters [21], MOSFETs [22] and radiochromic films procedures using offline tools such as RDM by MedSquare [29], DOSE by Qaelum [30], the DIDo system implemented in the software DOLIR [31], Skin Dose Map® tool integrated in DoseWatch® by GE Healthcare [32], em.dose from Esprimed [33,34], OpenSkin software provided by the OpenREM project [35], the UF-RIPSA algorithm developed at the University of Florida [36], the software developed by the Mayo Clinic [37], and two software (FDEIR and MC-GPU), based on Monte Carlo simulations [38]. These software can provide a dose map and the PSD value retrospectively; some of them are integrated into Radiation Dose Index Monitoring (RDIM) systems [29][30][31][32] and others are stand-alone products [33][34][35][36][37][38].…”