Dataset Information

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

ACCESS DATA

Publications

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

Similar Datasets

Project description:PURPOSE:To evaluate the image quality of low-dose chest digital radiographic images obtained with a new spatial noise reduction algorithm, compared to a conventional de-noising technique. MATERIALS AND METHODS:In 69 patients, the dose reduction protocol was divided into A, B, and C test groups- 60% (n = 22), 50% (n = 23), and 40% (n = 24) of the baseline dose. In each patient, baseline dose radiographs were obtained with conventional image processing while low-dose images were acquired with new image processing. A set of baseline and low-dose radiographic images per patient was evaluated and scored on a 5-point scale over seven anatomical landmarks (radiolucency of unobscured lung, pulmonary vascularity, trachea, edge of rib, heart border, intervertebral disc space, and pulmonary vessels in the retrocardiac area) and three representative abnormal findings (nodule, consolidation, and interstitial marking) by two thoracic radiologists. A comparison of paired baseline and low-dose images was statistically analyzed using a non-inferiority test based on the paired t-test or the Wilcoxon signed-rank test. RESULTS:In A, B, and C test groups, the mean dose reduction rate of the baseline radiation dose was 63.4%, 53.9%, and 47.8%, respectively. In all test groups, the upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks and three representative abnormal findings, which suggested that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose. CONCLUSION:In our study, an image processing technique integrating a new noise reduction algorithm achieved dose reductions of approximately half without compromising image quality for abnormal lung findings and anatomical landmarks seen on chest radiographs. This feature-preserving, noise reduction algorithm adopted in the proposed engine enables a lower radiation dose boundary for the sake of patient's and radiography technologist's radiation safety in routine clinical practice, in compliance with regulatory guidelines.

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.

Project description:Feedback from radiologists indicated that differences in image appearance and noise impeded reading of post-contrast computed tomography (CT) scans from an updated CT scanner that was recently added to a fleet of existing scanners from the same vendor, despite using identically named reconstruction algorithms. The goals of this work were to quantify and possibly standardize image quality on the new and an existing scanner using phantom images. Three months of daily quality control images were analyzed to determine the mean CT number and noise magnitude in a water phantom. Next, subtraction images from the uniformity section of an American College of Radiology CT phantom were used to generate noise power spectra for both scanners. Then, a semi-anthropomorphic liver phantom was imaged with both scanners in triplicate using identical body protocols to quantify differences CT number and noise magnitude. Finally, the scanner dependence of CT number and noise magnitude on material attenuation was quantified using a multi-energy CT phantom with 15 material inserts. Significant differences between scanners were determined using a paired or Welch's t test as appropriate. In daily quality control images, the new scanner exhibited slightly higher CT number (0.697 vs. 0.412, P < 0.001, n = 85) and slightly lower noise magnitude (4.85 vs. 4.94, P < 0.001, n = 85). Measured NPS was not significantly different between the existing and new scanners. Interestingly, it was observed that the noise magnitude from the new scanner increased with increasing material attenuation in both the liver (P = 0.008) and multi-energy (P < 0.001) phantoms. Using an alternate reconstruction algorithm with the new scanner eliminated this deviation at high material attenuations. While standard noise evaluation in a water phantom was unable to discern differences between the scanners, more comprehensive testing with higher attenuation materials allowed for the characterization and homogenization of image quality.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure