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Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise.

ABSTRACT: To determine a comprehensive method for the implementation of adaptive statistical iterative reconstruction (ASIR) for maximal radiation dose reduction in pediatric computed tomography (CT) without changing the magnitude of noise in the reconstructed image or the contrast-to-noise ratio (CNR) in the patient.The institutional review board waived the need to obtain informed consent for this HIPAA-compliant quality analysis. Chest and abdominopelvic CT images obtained before ASIR implementation (183 patient examinations; mean patient age, 8.8 years ± 6.2 [standard deviation]; range, 1 month to 27 years) were analyzed for image noise and CNR. These measurements were used in conjunction with noise models derived from anthropomorphic phantoms to establish new beam current-modulated CT parameters to implement 40% ASIR at 120 and 100 kVp without changing noise texture or magnitude. Image noise was assessed in images obtained after ASIR implementation (492 patient examinations; mean patient age, 7.6 years ± 5.4; range, 2 months to 28 years) the same way it was assessed in the pre-ASIR analysis. Dose reduction was determined by comparing size-specific dose estimates in the pre- and post-ASIR patient cohorts. Data were analyzed with paired t tests.With 40% ASIR implementation, the average relative dose reduction for chest CT was 39% (2.7/4.4 mGy), with a maximum reduction of 72% (5.3/18.8 mGy). The average relative dose reduction for abdominopelvic CT was 29% (4.8/6.8 mGy), with a maximum reduction of 64% (7.6/20.9 mGy). Beam current modulation was unnecessary for patients weighing 40 kg or less. The difference between 0% and 40% ASIR noise magnitude was less than 1 HU, with statistically nonsignificant increases in patient CNR at 100 kVp of 8% (15.3/14.2; P = .41) for chest CT and 13% (7.8/6.8; P = .40) for abdominopelvic CT.Radiation dose reduction at pediatric CT was achieved when 40% ASIR was implemented as a dose reduction tool only; no net change to the magnitude of noise in the reconstructed image or the patient CNR occurred.

SUBMITTER: Brady SL

PROVIDER: S-EPMC4489555 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise.

Brady Samuel L SL Moore Bria M BM Yee Brian S BS Kaufman Robert A RA

Radiology 20131028 1

<h4>Purpose</h4>To determine a comprehensive method for the implementation of adaptive statistical iterative reconstruction (ASIR) for maximal radiation dose reduction in pediatric computed tomography (CT) without changing the magnitude of noise in the reconstructed image or the contrast-to-noise ratio (CNR) in the patient.<h4>Materials and methods</h4>The institutional review board waived the need to obtain informed consent for this HIPAA-compliant quality analysis. Chest and abdominopelvic CT ...[more]

PMID: 23901128

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Project description:ObjectiveTo assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction.Materials and methodsWe retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction).ResultsObjective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons.ConclusionApplication of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.

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Dataset Information

Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise.

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Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise.

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