An Algorithm for Intelligent Sorting of CT-Related Dose Parameters

Abstract

Imaging centers nationwide are seeking innovative means to record and monitor computed tomography (CT)-related radiation dose in light of multiple instances of patient overexposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival, and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose length product (DLP)--an indirect estimate of radiation dose--requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, "arterial" could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.

Role of dual energy spectral computed tomography in characterization of hepatocellular carcinoma: Initial experience from a tertiary liver care institute

“…[28,30] A range of keV value has been recommended in studies published in literature to study data sets for the best contrast-to-noise ratio (CNR) between liver lesions and underlying liver parenchyma as well as for lesion conspicuity. [7,8] 80 kVp images were also documented to be superior for the distinction of metastases in normal liver tissue. [27] Low keV spectral CT and iodine density images are able to delineate diffuse infiltrative masses which may be virtually indistinguishable on a routine CT, especially since these tumors are apparently hypovascular on routine CT and hence may not show adequate washout.…”

“…The advent of spectral CT is based on the principle that this technique involves scanning at distinctly different energies (most commonly used energy levels are 70 and 140 kVp) using a setup of single or dual x-ray tubes with detectors operated at two distinct energies or rapidly switching between two different energies depending on the manufacturer. [6][7][8][9] This is based on the physics principle of 'Photoelectric effect' which, is used to obtain additional information regarding tissue composition. The photoelectric effect essentially utilizes the difference in the K-edges of two elements.…”