Implementation of a spatio-temporal figure of merit for new automatic dose rate control regimes in dynamic x-ray imaging

Abstract

This work describes a new approach to automatic dose rate control (ADRC) for dynamic x-ray imaging, utilizing a spatial frequency weighted signal difference to noise ratio (SDNR(u)). Three aspects of ADRC programming using SDNR(u), which contains information on the target material, velocity and size, were examined. First, whether SDNR(u) can be held constant at the requested level over some patient thickness range for five materials relevant to interventional imaging (iron, gadolinium, platinum, bismuth, and tantalum). Second, the efficiency of the new ADRC was compared to the current settings using a figure of merit (FOM), defined as SDNR(u)²/reference air kerma rate for iron and platinum, over a range of simulated patient thicknesses. Third, the ability of the new ADRC to optimize exposure parameters for iron, iodine, gadolinium, tantalum, platinum and bismuth was examined. A phantom of 20 mm PMMA and 2 mm Al sheets was used to simulate patient equivalent thicknesses between 25 mm to 375 mm. The relevant metal foil targets were placed at the phantom centre and imaged on a Siemens Artis Q cardio-angiography system. SDNR(u) and reference air kerma were measured, along with the FOM for the relevant conditions. The optimal exposure factor study was made for patient equivalent thicknesses of 100 mm, 200 mm and 300 mm. The new ADRC regulation held SDNR(u) constant versus phantom thickness within 5%, for the five materials studied. FOM increase compared to the current regulation used on the Artis Q ranged between 18% and 296% (averaged over all thicknesses), and depended on acquisition mode and material. Material optimization via the new ADRC increased FOM by 68%, 165%, 164% and 32% for gadolinium, tantalum, platinum and bismuth respectively, corresponding to potential dose savings of 40%, 62%, 62% and 24% for the same target SDNR(u). An SDNR(u) driven approach to the ADRC logic of dynamic imaging systems is a viable alternative to current programming, with a resulting improvement in imaging efficiency and corresponding dose reduction.

Comparison of a contrast‐to‐noise ratio‐driven exposure control and a regular detector dose‐driven exposure control in abdominal imaging in a clinical angiography system

“…Compared to Dehairs et al we did not measure the skin dose directly using a dosemeter. 10,18 Instead,we used the air kerma rate at the international electrotechnical commission (IEC) reference point, 13 which is given by the ionization chamber of the X-ray tube setup. The air kerma at the reference point is commonly used as a surrogate of the incident skin dose.…”

“…Second, we compare the incident air kerma when DEC and CEC achieve nearly identical CNR values for DEC with NQ 0.3 . 13,18 Identical CNR values cannot be achieved due to the stepwise adjustment of the acquisition parameters. To compensate for the remaining differences in CNR, we compared K a,r with the assistance of the commonly used dose-weighted CNR as a figure of merit (FOM):…”

Signal-to-Noise Ratio Rate Measurement in Fluoroscopy for Quality Control and Teaching Good Radiological Imaging Technique

“…Dehairs ( 18 ) implemented a spatio-temporal FOM [SdNR(u)] with a new ADRC strategy in dynamic imaging aiming to maintain the signal-to-noise level for a range of patient thicknesses. Contrary to what is found, for example in our Figure 4 , using a conventional ADRC-system (where SNR 2 rate decreases with increasing phantom thickness), their ADRC strategy keeps signal-to-noise constant from ~10 cm to 25 cm tissue-bone equivalent thickness and still results in an increase in their FOM, SdNR(u) 2 /AKR ref compared with conventional ADRC; AKR ref being the air kerma rate at the reference point.…”

Tantalum-specific contrast-to-noise ratio or conventional detector dose-driven exposure control in angiography: radiation dose and image quality evaluation in a porcine model

“…DEC is not designed to consider the material of interest and does not differentiate between scattered and direct imaging x-rays, both contributing to the detector dose. Hence, there a limited means to optimise image quality (IQ) and radiation exposure for a specific diagnostic task [ 2 , 3 ].…”

“…CEC estimates first the patient equivalent thickness in terms of water equivalent thickness (WET). Based on WET, CEC selects the image acquisition parameters precalculated using Monte Carlo simulation providing the requested CNR at the lowest possible incident air kerma at the interventional reference point ( K a,r ) rate [ 2 , 3 , 7 ].…”