Dataset Information

Metal implants influence CT scan parameters leading to increased local radiation exposure: A proposal for correction techniques.

ABSTRACT: Metal implants not only deteriorate image quality, but also increase radiation exposure. The purpose of this study was to evaluate the effect of metal hip prosthesis on absorbed radiation dose and assess the efficacy of organ dose modulation (ODM) and metal artifact reduction (MAR) protocols on dose reduction. An anthropomorphic phantom was scanned with and without bilateral metal hip prostheses, and surface and deep level radiation doses were measured at the abdomen and pelvis. Finally, the absorbed radiation doses at pelvic and abdominal cavities in the reference, ODM, and two MAR scans (Gemstone spectral imaging, GE) were compared. The Mann Whitney-U test and Kruskal-Wallis test were performed to compare the volume CT dose index (CTDIvol) and mean absorbed radiation doses. Unilateral and bilateral metal hip prostheses increased CTDIVOL by 14.4% and 30.5%, respectively. MAR protocols decreased absorbed radiation doses in the pelvis. MAR showed the most significant dose reduction in the deep pelvic cavity followed by ODM. However, MAR protocols increased absorbed radiation doses in the upper abdomen. ODM significantly reduced absorbed radiation in the pelvis and abdomen. In conclusion, metal hip implants increased radiation doses in abdominopelvic CT scans. MAR and ODM techniques reduced absorbed radiation dose in abdominopelvic CT scans with metal hip prostheses.

SUBMITTER: Song OK

PROVIDER: S-EPMC6707604 | biostudies-other | 2019

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Metal implants influence CT scan parameters leading to increased local radiation exposure: A proposal for correction techniques.

Song Ok Kyu OK Chung Yong Eun YE Seo Nieun N Baek Song-Ee SE Choi Jin-Young JY Park Mi-Suk MS Kim Myeong-Jin MJ

PloS one 20190823 8

Metal implants not only deteriorate image quality, but also increase radiation exposure. The purpose of this study was to evaluate the effect of metal hip prosthesis on absorbed radiation dose and assess the efficacy of organ dose modulation (ODM) and metal artifact reduction (MAR) protocols on dose reduction. An anthropomorphic phantom was scanned with and without bilateral metal hip prostheses, and surface and deep level radiation doses were measured at the abdomen and pelvis. Finally, the abs ...[more]

PMID: 31442288

Similar Datasets

Project description:The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (-630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). In -630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (p ≤ 0.05). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25 mm, respectively (p ≤ 0.05). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in -630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.

Project description:ObjectivesCardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.MethodsOn a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020-December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test.ResultsThe network replicated the radiologists' scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine.ConclusionsAutomatic delimitation of the scan range can result in a radiation dose reduction to the patient.Clinical relevance statementFully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers.Key points• Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network. • Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists. • Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.

Project description:PurposeAs computed tomography (CT) imaging is the most commonly used modality for treatment planning in radiation therapy, metal artifacts in the planning CT images may complicate the target delineation and reduce the dose calculation accuracy. Although current CT scanners do provide certain correction steps, it is a common understanding that there is not a universal solution yet to the metal artifact reduction (MAR) in general. Particularly noting the importance of MAR for radiation treatment planning, we propose a novel MAR method in this work that recruits an additional tilted CT scan and synthesizes nearly metal-artifact-free CT images.MethodsThe proposed method is based on the facts that the most pronounced metal artifacts in CT images show up along the x-ray beam direction traversing multiple metallic objects and that a tilted CT scan can provide complementary information free of such metal artifacts in the earlier scan. Although the tilted CT scan would contain its own metal artifacts in the images, the artifacts may manifest in a different fashion leaving a chance to concatenate the two CT images with the metal artifacts much suppressed. We developed an image processing technique that uses the structural similarity (SSIM) for suppressing the metal artifacts. On top of the additional scan, we proposed to use an existing MAR method for each scan if necessary to further suppress the metal artifacts.ResultsThe proposed method was validated by a simulation study using the pelvic region of an XCAT numerical phantom and also by an experimental study using the head part of the Rando phantom. The proposed method was found to effectively reduce the metal artifacts. Quantitative analyses revealed that the proposed method reduced the mean absolute percentages of the error by up to 86% and 89% in the simulation and experimental studies, respectively.ConclusionsIt was confirmed that the proposed method, using complementary information acquired from an additional tilted CT scan, can provide nearly metal-artifact-free images for the treatment planning.

Project description:Applications of volumetric CT (VCT) are hampered by shading and streaking artifacts in the reconstructed images. These artifacts are mainly due to strong x-ray scatter signals accompanied with the large illumination area within one projection, which lead to CT number inaccuracy, image contrast loss and spatial nonuniformity. Although different scatter correction algorithms have been proposed in literature, a standard solution still remains unclear. Measurement-based methods use a beam blocker to acquire scatter samples. These techniques have unrivaled advantages over other existing algorithms in that they are simple and efficient, and achieve high scatter estimation accuracy without prior knowledge of the imaged object. Nevertheless, primary signal loss is inevitable in the scatter measurement, and multiple scans or moving the beam blocker during data acquisition are typically employed to compensate for the missing primary data. In this paper, we propose a new measurement-based scatter correction algorithm without primary compensation for full-fan VCT. An accurate reconstruction is obtained with one single-scan and a stationary x-ray beam blocker, two seemingly incompatible features which enable simple and efficient scatter correction without increase of scan time or patient dose.Based on the CT reconstruction theory, we distribute the blocked data over the projection area where primary signals are considered approximately redundant in a full scan, such that the CT image quality is not degraded even with primary loss. Scatter is then accurately estimated by interpolation and scatter-corrected CT images are obtained using an FDK-based reconstruction algorithm.The proposed method is evaluated using two phantom studies on a tabletop CBCT system. On the Catphan©600 phantom, our approach reduces the reconstruction error from 207 Hounsfield unit (HU) to 9 HU in the selected region of interest, and improves the image contrast by a factor of 2.0 in the high-contrast regions. On an anthropomorphic head phantom, the reconstruction error is reduced from 97 HU to 6 HU in the soft tissue region and image spatial nonuniformity decreases from 27% to 5% after correction.Our method inherits the main advantages of measurement-based methods while avoiding their shortcomings. It has the potential to become a practical scatter correction solution widely implementable on different VCT systems.

Dataset Information

Metal implants influence CT scan parameters leading to increased local radiation exposure: A proposal for correction techniques.

Publications

Metal implants influence CT scan parameters leading to increased local radiation exposure: A proposal for correction techniques.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets