Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: A pediatric oncology perspective

Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT

Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-based denoising (DLD) and standard-dose CT (SDCT, 100% dose) with model-based iterative reconstruction (MBIR). Materials and Methods In this prospective, multicenter, noninferiority study, individuals referred for liver CT scans were enrolled from three tertiary referral hospitals between February 2021 and August 2022. All liver CT scans were conducted using a dual-source scanner with the dose split into tubes A (67% dose) and B (33% dose). Blended images from tubes A and B were created using MBIR to produce SDCT images, whereas LDCT images used data from tube B and were reconstructed with DLD. The noise in liver images was measured and compared between imaging techniques. The diagnostic performance of each technique in detecting malignant liver tumors was evaluated by three independent radiologists using jackknife alternative free-response receiver operating characteristic analysis. Noninferiority of LDCT compared with SDCT was declared when the lower limit of the 95% CI for the difference in figure of merit (FOM) was greater than -0.10. Results A total of 296 participants (196 men, 100 women; mean age, 60.5 years ± 13.3 [SD]) were included. The mean noise level in the liver was significantly lower for LDCT (10.1) compared with SDCT (10.7) (P < .001). Diagnostic performance was assessed in 246 participants (108 malignant tumors in 90 participants). The reader-averaged FOM was 0.880 for SDCT and 0.875 for LDCT (P = .35). The difference fell within the noninferiority margin (difference, -0.005 [95% CI: -0.024, 0.012]). Conclusion Compared with SDCT with MBIR, LDCT using 33% of the standard radiation dose had reduced image noise and comparable diagnostic performance in detecting malignant liver tumors. Keywords: CT, Abdomen/GI, Liver, Comparative Studies, Diagnosis, Reconstruction Algorithms Clinical trial registration no. NCT05804799 © RSNA, 2024 Supplemental material is available for this article.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms

BACKGROUND

The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied.

OBJECTIVE

This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms.

MATERIAL AND METHODS

In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels.

RESULTS

The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels.

CONCLUSION

A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions

OBJECTIVES

To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction (MBIR).

METHODS

In this retrospective study, CT images of 80 patients with hepatic focal lesions were included. For noninferiority analysis of overall image quality, a margin of - 0.5 points (scored in a 5-point scale) for the difference between scan protocols was pre-defined. Other quantitative or qualitative image quality assessments were performed. Additionally, detectability of significant liver lesions was compared, with 64 pairs of CT, using the jackknife alternative free-response ROC analysis, with noninferior margin defined by the lower limit of 95% confidence interval (CI) of the difference of figure-of-merit less than - 0.1.

RESULTS

The mean overall image quality scores with LDCT and SDCT were 3.77 ± 0.38 and 3.94 ± 0.34, respectively, demonstrating a difference of - 0.17 (95% CI: - 0.21 to - 0.12), which did not cross the predefined noninferiority margin of - 0.5. Furthermore, LDCT showed significantly superior quantitative results of liver lesion contrast to noise ratio (p < 0.05). However, although LDCT scored higher than the average score in qualitative image quality assessments, they were significantly lower than those of SDCT (p < 0.05). Figure-of-merit for lesion detection was 0.859 for LDCT and 0.878 for SDCT, showing noninferiority (difference: - 0.019, 95% CI: - 0.058 to 0.021).

CONCLUSION

LDCT using DLD with 67% radiation dose reduction showed non-inferior overall image quality and lesion detectability, compared to SDCT.

KEY POINTS

• Low-dose liver CT using deep learning denoising (DLD), at 67% dose reduction, provided non-inferior overall image quality compared to standard-dose CT using model-based iterative reconstruction (MBIR). • Low-dose CT using DLD showed significantly less noise and higher CNR _{lesion to liver} than standard-dose CT using MBIR and demonstrated at least average image quality score among all readers, albeit with lower scores than standard-dose CT using MBIR. • Low-dose liver CT showed noninferior detectability for malignant and pre-malignant liver lesions, compared to standard-dose CT.

Quality control of radiomic features using 3D-printed CT phantoms

Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting. Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes. Results: The structural similarity index between the segment used from the patients' DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was < 1.0 % across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with pDV > ± 15 % . Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (d'_NPWE), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for edge definition, quantum noise level, and object conspicuity. Analysis of variance and Tukey honest significant difference post hoc tests were used to analyze differences between reconstruction algorithms. Results Images from 19 patients (mean age, 11 years ± 5 [standard deviation]; 10 female patients) were evaluated. Compared with FBP, SBIR, and MBIR, DLR demonstrated improved object detectability by 51% (16.5 of 10.9), 18% (16.5 of 13.9), and 11% (16.5 of 14.8), respectively. DLR reduced image noise without noise texture effects seen with MBIR. Radiologist ratings were 7 ± 1 (DLR), 6.2 ± 1 (MBIR), 6.2 ± 1 (SBIR), and 4.6 ± 1 (FBP); two-way analysis of variance showed a difference on the basis of reconstruction type (P < .001). Radiologists consistently preferred DLR images (intraclass correlation coefficient, 0.89; 95% CI: 0.83, 0.93). DLR demonstrated 52% (1 of 2.1) greater dose reduction than SBIR. Conclusion The DLR algorithm improved image quality and dose reduction without sacrificing noise texture and spatial resolution. © RSNA, 2020 Online supplemental material is available for this article.

Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques

Background:

The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy.

Objective:

This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR) on CT scanner and its comparison to the filtered back-projection (FBP).

Material and Methods:

In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom. The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32 cm² was drawn and used to measure HU and noise values. The R² of the relation between mass densities vs. HU values was used to measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant.

Results:

The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05). There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05). The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R² value (> 0.999).

Conclusion:

AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.

Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low-contrast detectability in abdomen CT

Purpose

The utilization of iterative reconstruction makes it difficult to identify the dose‐noise relationship, resulting in empirical design of scan protocols and inconsistent conclusions on dose reduction for consistent image quality. This study was to quantitatively determine the dose and the blending fraction of adaptive statistical iterative reconstruction (ASIR) based on the specified low‐contrast detectability (LCD).

Methods

A tissue equivalent abdomen phantom and a GE discovery 750 HD computed tomography (CT) were utilized. The normality of the noise distribution was tested at various spatial scales (2.1–9.8 mm) in the presence of ASIR (10–100%) with a wide range of doses (2.24–38 mGy). The statically defined minimum detectable contrast (MDC) was used as the image quality metric. The parametric model decomposed the MDC into two terms: one with and the other without ASIR, each was related to the dose in the form of power law with factors and indices dependent on spatial scales. The parameters were identified by least‐square fitting to the experimental data. By considering the constraint of the blending fraction in the range of [0, 1], the dose and ASIR blending fraction were determined for any specified low‐contrast detectability (LCD), quantified by the MDC at the concerned lesion size.

Results

It was verified that noise distribution is normal in the presence of ASIR. It was also found that the noises obtained from the subtractions of adjacent slices had an underestimate of 20% as compared to the ground truth noises, regardless of the spatial scale, pitch, or ASIR blending fraction. The least‐square fitting for the parametric model resulted in correlation coefficients from 0.905 to 0.996. The root‐mean‐square errors ranged from 1.27% to 7.15%.

Conclusion

The parametric model can be used to form a look‐up‐table for dose and ASIR blending fraction. The dose choices may be substantially limited in some cases depending on the required LCD.

Comparison of image noise and image quality between full-dose abdominal computed tomography scans reconstructed with weighted filtered back projection and half-dose scans reconstructed with improved sinogram-affirmed iterative reconstruction (SAFIRE*)

PURPOSE

To retrospectively compare the image noise, signal-to-noise ratio (SNR), and subjective image quality between CT images acquired with a dual-source, split-dose imaging protocol reconstructed at full and half doses with weighted filtered back projection (wFBP) and an improved sinogram-affirmed iterative reconstruction algorithm (SAFIRE*).

METHODS

Fifty-three consecutive patients underwent contrast-enhanced CT of the abdomen using a standardized dual-source, single energy CT protocol. Half-dose images were retrospectively generated using data from one detector only. Full-dose datasets were reconstructed with wFBP, while half-dose datasets were reconstructed with wFBP and SAFIRE* strengths 1-5. Region of interest analysis was performed to assess SNR and noise. Diagnostic acceptability, subjective noise, and spatial resolution were graded on a 10-point scale by two readers. Statistical analysis was carried out with repeated measures analysis of variance, Wilcoxon signed rank test, and Cohen's κ test.

RESULTS

With the increasing strengths of SAFIRE*, a progressive reduction in noise and increase in SNR (p < 0.01) was observed. There was a statistically significant decrease in objective noise and increase in SNR in half-dose SAFIRE* strength 4 and 5 reconstructions compared to full-dose reconstructions using wFBP (p < 0.01). Qualitative analysis revealed a progressive increase in diagnostic acceptability, decrease in subjective noise and increase in spatial resolution for half-dose images reconstructed with the increasing strengths of SAFIRE* (p < 0.01).

CONCLUSIONS

Half-dose CT images reconstructed with SAFIRE* at strength 4 and 5 have superior image quality compared to full-dose images reconstructed with wFBP. SAFIRE* potentially allows dose reductions in the order of 50% over wFBP.

Optimization of hybrid iterative reconstruction level and evaluation of image quality and radiation dose for pediatric cardiac computed tomography angiography

BACKGROUND

Hybrid iterative reconstruction can reduce image noise and produce better image quality compared with filtered back-projection (FBP), but few reports describe optimization of the iteration level.

OBJECTIVE

We optimized the iteration level of iDose⁴ and evaluated image quality for pediatric cardiac CT angiography.

MATERIALS AND METHODS

Children (n = 160) with congenital heart disease were enrolled and divided into full-dose (n = 84) and half-dose (n = 76) groups. Four series were reconstructed using FBP, and iDose⁴ levels 2, 4 and 6; we evaluated subjective quality of the series using a 5-grade scale and compared the series using a Kruskal-Wallis H test. For FBP and iDose⁴-optimal images, we compared contrast-to-noise ratios (CNR) and size-specific dose estimates (SSDE) using a Student's t-test. We also compared diagnostic-accuracy of each group using a Kruskal-Wallis H test.

RESULTS

Mean scores for iDose⁴ level 4 were the best in both dose groups (all P < 0.05). CNR was improved in both groups with iDose⁴ level 4 as compared with FBP. Mean decrease in SSDE was 53% in the half-dose group. Diagnostic accuracy for the four datasets were in the range 92.6-96.2% (no statistical difference).

CONCLUSION

iDose⁴ level 4 was optimal for both the full- and half-dose groups. Protocols with iDose⁴ level 4 allowed 53% reduction in SSDE without significantly affecting image quality and diagnostic accuracy.

The Detection of Focal Liver Lesions Using Abdominal CT: A Comparison of Image Quality Between Adaptive Statistical Iterative Reconstruction V and Adaptive Statistical Iterative Reconstruction

RATIONALE AND OBJECTIVES

To investigate image quality characteristics of abdominal computed tomography (CT) scans reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) vs currently using applied adaptive statistical iterative reconstruction (ASIR).

MATERIALS AND METHOD

This institutional review board-approved study included 35 consecutive patients who underwent CT of the abdomen. Among these 35 patients, 27 with focal liver lesions underwent abdomen CT with a 128-slice multidetector unit using the following parameters: fixed noise index of 30, 1.25 mm slice thickness, 120 kVp, and a gantry rotation time of 0.5 seconds. CT images were analyzed depending on the method of reconstruction: ASIR (30%, 50%, and 70%) vs ASIR-V (30%, 50%, and 70%). Three radiologists independently assessed randomized images in a blinded manner. Imaging sets were compared to focal lesion detection numbers, overall image quality, and objective noise with a paired sample t test. Interobserver agreement was assessed with the intraclass correlation coefficient.

RESULTS

The detection of small focal liver lesions (<10 mm) was significantly higher when ASIR-V was used when compared to ASIR (P <0.001). Subjective image noise, artifact, and objective image noise in liver were generally significantly better for ASIR-V compared to ASIR, especially in 50% ASIR-V. Image sharpness and diagnostic acceptability were significantly worse in 70% ASIR-V compared to various levels of ASIR.

CONCLUSION

Images analyzed using 50% ASIR-V were significantly better than three different series of ASIR or other ASIR-V conditions at providing diagnostically acceptable CT scans without compromising image quality and in the detection of focal liver lesions.

Nuclear Medicine Procedures in Children: Special Considerations

Nuclear medicine imaging in children is best accomplished when a child-friendly environment is provided for patients and parents. An approach that minimizes patient anxiety and fear is described. International guidelines for administered activity should be used to minimize absorbed radiation doses from radiopharmaceuticals. CT exposure parameters may be reduced to pediatric best practice for diagnostic CT and further reduced when CT images are needed only for localization purposes.

Objective performance assessment of five computed tomography iterative reconstruction algorithms

OBJECTIVE

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction.

METHODS

Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners.

RESULTS

Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR.

CONCLUSIONS

The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with VEO™. This study is useful in that it provides performance assessment of the iterative algorithms available from several mainstream CT manufacturers.

A STUDY OF THE IMAGE QUALITY OF COMPUTED TOMOGRAPHY ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTED BRAIN IMAGES USING SUBJECTIVE AND OBJECTIVE METHODS

The recent reintroduction of iterative reconstruction in computed tomography has facilitated the realisation of major dose saving. The aim of this article was to investigate the possibility of achieving further savings at a site with well-established Adaptive Statistical iterative Reconstruction (ASiR™) (GE Healthcare) brain protocols. An adult patient study was conducted with observers making visual grading assessments using image quality criteria, which were compared with the frequency domain metrics, noise power spectrum and modulation transfer function. Subjective image quality equivalency was found in the 40-70% ASiR™ range, leading to the proposal of ranges for the objective metrics defining acceptable image quality. Based on the findings of both the patient-based and objective studies of the ASiR™/tube-current combinations tested, 60%/305 mA was found to fall within all, but one, of these ranges. Therefore, it is recommended that an ASiR™ level of 60%, with a noise index of 12.20, is a viable alternative to the currently used protocol featuring a 40% ASiR™ level and a noise index of 11.20, potentially representing a 16% dose saving.

Full Dose-Reduction Potential of Statistical Iterative Reconstruction for Head CT Protocols in a Predominantly Pediatric Population

BACKGROUND AND PURPOSE

A statistical iterative reconstruction algorithm provides an effective approach to reduce patient dose by compensating for increased image noise in CT due to reduced radiation output. However, after a point, the degree to which a statistical iterative algorithm is used for image reconstruction changes the image appearance. Our aim was to determine the maximum level of statistical iterative reconstruction that can be used to establish dose-reduced head CT protocols in a primarily pediatric population while maintaining similar appearance and level of image noise in the reconstructed image.

MATERIALS AND METHODS

Select head examinations (brain, orbits, sinus, maxilla, and temporal bones) were investigated. Dose-reduced head protocols using an adaptive statistical iterative reconstruction were compared for image quality with the original filtered back-projection reconstructed protocols in a phantom by using the following metrics: image noise frequency (change in perceived appearance of noise texture), image noise magnitude, contrast-to-noise ratio, and spatial resolution. Dose-reduction estimates were based on CT dose index values. Patient volume CT dose index and image noise magnitude were assessed in 737 pre- and post-dose-reduced examinations.

RESULTS

Image noise texture was acceptable for up to 60% adaptive statistical iterative reconstruction for the soft reconstruction kernel (at both 100 and 120 kV[peak]) and up to 40% adaptive statistical iterative reconstruction for the standard reconstruction kernel. Implementation of 40% and 60% adaptive statistical iterative reconstruction led to an average reduction in the volume CT dose index of 43% for brain, 41% for orbit, 30% for maxilla, 43% for sinus, and 42% for temporal bone protocols for patients between 1 month and 26 years of age, while maintaining an average noise magnitude difference of 0.1% (range, -3% to 5%), improving the contrast-to-noise ratio of low-contrast soft-tissue targets and the spatial resolution of high-contrast bony anatomy, compared with filtered back-projection.

CONCLUSIONS

The methodology in this study demonstrates maximizing patient dose reduction and maintaining image quality by using statistical iterative reconstruction for a primarily pediatric population undergoing head CT examinations.

Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction - a phantom study

The purpose of this study was to characterize image quality and dose performance with GE CT iterative reconstruction techniques, adaptive statistical iterative reconstruction (ASiR), and model‐based iterative reconstruction (MBIR), over a range of typical to low‐dose intervals using the Catphan 600 and the anthropomorphic Kyoto Kagaku abdomen phantoms. The scope of the project was to quantitatively describe the advantages and limitations of these approaches. The Catphan 600 phantom, supplemented with a fat‐equivalent oval ring, was scanned using a GE Discovery HD750 scanner at 120 kVp, 0.8 s rotation time, and pitch factors of 0.516, 0.984, and 1.375. The mA was selected for each pitch factor to achieve CTDIvol values of 24, 18, 12, 6, 3, 2, and 1 mGy. Images were reconstructed at 2.5 mm thickness with filtered back‐projection (FBP); 20%, 40%, and 70% ASiR; and MBIR. The potential for dose reduction and low‐contrast detectability were evaluated from noise and contrast‐to‐noise ratio (CNR) measurements in the CTP 404 module of the Catphan. Hounsfield units (HUs) of several materials were evaluated from the cylinder inserts in the CTP 404 module, and the modulation transfer function (MTF) was calculated from the air insert. The results were confirmed in the anthropomorphic Kyoto Kagaku abdomen phantom at 6, 3, 2, and 1 mGy. MBIR reduced noise levels five‐fold and increased CNR by a factor of five compared to FBP below 6 mGy CTDIvol, resulting in a substantial improvement in image quality. Compared to ASiR and FBP, HU in images reconstructed with MBIR were consistently lower, and this discrepancy was reversed by higher pitch factors in some materials. MBIR improved the conspicuity of the high‐contrast spatial resolution bar pattern, and MTF quantification confirmed the superior spatial resolution performance of MBIR versus FBP and ASiR at higher dose levels. While ASiR and FBP were relatively insensitive to changes in dose and pitch, the spatial resolution for MBIR improved with increasing dose and pitch. Unlike FBP, MBIR and ASiR may have the potential for patient imaging at around 1 mGy CTDIvol. The improved low‐contrast detectability observed with MBIR, especially at low‐dose levels, indicate the potential for considerable dose reduction.

PACS number(s): 87.57.Q‐, 87.57,nf, 87.57.C‐, 87.57.cj, 87.57.cf, 87.57.cm, 87.57.uq

Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction

PURPOSE

To develop ultralow dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT).

METHODS

A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultralow doses (10-35 mA s). CT quantitation: noise, low-contrast resolution, and CT numbers for 11 tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% volume computed tomography dose index (0.39/3.64; mGy) from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET images were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUVbw) of various diameter targets (range 8-37 mm), background uniformity, and spatial resolution. Radiation dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative dose reduction and noise control.

RESULTS

CT numbers were constant to within 10% from the nondose reduced CTAC image for 90% dose reduction. No change in SUVbw, background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols was found down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62% and 86% (3.2/8.3-0.9/6.2). Noise magnitude in dose-reduced patient images increased but was not statistically different from predose-reduced patient images.

CONCLUSIONS

Using ASiR allowed for aggressive reduction in CT dose with no change in PET reconstructed images while maintaining sufficient image quality for colocalization of hybrid CT anatomy and PET radioisotope uptake.

Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium

BACKGROUND

Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium.

OBJECTIVE

To investigate the influence of ASIR on calcium quantification in comparison to FBP.

METHODS

In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques.

RESULTS

Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P < 0.001). Use of ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size.

CONCLUSION

In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes.

Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms

Dose reduction in children undergoing CT scanning is an important priority for the radiology community and public at large. Drawbacks of radiation reduction are increased image noise and artifacts, which can affect image interpretation. Iterative reconstruction techniques have been developed to reduce noise and artifacts from reduced-dose CT examinations, although reconstruction algorithm, magnitude of dose reduction and effects on image quality vary. We review the reconstruction principles, radiation dose potential and effects on image quality of several iterative reconstruction techniques commonly used in clinical settings, including 3-D adaptive iterative dose reduction (AIDR-3D), adaptive statistical iterative reconstruction (ASIR), iDose, sinogram-affirmed iterative reconstruction (SAFIRE) and model-based iterative reconstruction (MBIR). We also discuss clinical applications of iterative reconstruction techniques in pediatric abdominal CT.

The role of chest computed tomography (CT) as a surveillance tool in children with high-risk neuroblastoma

BACKGROUND

Standardization of imaging obtained in children with neuroblastoma is not well established. This study examines chest CT in pediatric patients with high-risk neuroblastoma.

PROCEDURE

Medical records and imaging from 88 patients with high-risk neuroblastoma, diagnosed at St. Jude Children's Research Hospital between January, 2002 and December, 2009, were reviewed. Surveillance imaging was conducted through 2013. Ten patients with thoracic disease at diagnosis were excluded. Event free survival (EFS) and overall survival (OS) were estimated. Size specific dose estimates for CT scans of the chest, abdomen, and pelvis were used to estimate absolute organ doses to 23 organs. Organ dosimetry was used to calculate cohort effective dose.

RESULTS

The 5 year OS and EFS were 51.9% ± 6.5% and 42.6% ± 6.5%, respectively. Forty-six (58.9%) patients progressed/recurred and 41 (52.6%) died of disease. Eleven patients (14%) developed thoracic disease progression/recurrence identified by chest CT (1 paraspinal mass, 1 pulmonary nodules, and 9 nodal). MIBG (metaiodobenzylguanidine) scans identified thoracic disease in six patients. Five of the 11 had normal chest MIBG scans; three were symptomatic and two were asymptomatic with normal chest MIBG scans but avid bone disease. The estimated radiation dose savings from surveillance without CT chest imaging was 42%, 34% when accounting for modern CT acquisition (2011-2013).

CONCLUSIONS

Neuroblastoma progression/recurrence in the chest is rare and often presents with symptoms or is identified using standard non-CT imaging modalities. For patients with non-thoracic high-risk neuroblastoma at diagnosis, omission of surveillance chest CT imaging can save 35-42% of the radiation burden without compromising disease detection.

Hybrid and model-based iterative reconstruction techniques for pediatric CT

OBJECTIVE. Radiation exposure from CT examinations should be reduced to a minimum in children. Iterative reconstruction (IR) is a method to reduce image noise that can be used to improve CT image quality, thereby allowing radiation dose reduction. This article reviews the use of hybrid and model-based IRs in pediatric CT and discusses the possibilities, advantages, and disadvantages of IR in pediatric CT and the importance of radiation dose reduction for CT of children. CONCLUSION. IR is a promising and potentially highly valuable technique that can be used to substantially reduce the amount of radiation in pediatric imaging. Future research should determine the maximum achievable radiation dose reduction in pediatric CT that is possible without a loss of diagnostic image quality.

Radiation dose and image quality in pediatric chest CT: effects of iterative reconstruction in normal weight and overweight children

BACKGROUND

New CT reconstruction techniques may help reduce the burden of ionizing radiation.

OBJECTIVE

To quantify radiation dose reduction when performing pediatric chest CT using a low-dose protocol and 50% adaptive statistical iterative reconstruction (ASIR) compared with age/gender-matched chest CT using a conventional dose protocol and reconstructed with filtered back projection (control group) and to determine its effect on image quality in normal weight and overweight children.

MATERIALS AND METHODS

We retrospectively reviewed 40 pediatric chest CT (M:F = 21:19; range: 0.1-17 years) in both groups. Radiation dose was compared between the two groups using paired Student's t-test. Image quality including noise, sharpness, artifacts and diagnostic acceptability was subjectively assessed by three pediatric radiologists using a four-point scale (superior, average, suboptimal, unacceptable).

RESULTS

Eight children in the ASIR group and seven in the control group were overweight. All radiation dose parameters were significantly lower in the ASIR group (P < 0.01) with a greater than 57% dose reduction in overweight children. Image noise was higher in the ASIR group in both normal weight and overweight children. Only one scan in the ASIR group (1/40, 2.5%) was rated as diagnostically suboptimal and there was no unacceptable study.

CONCLUSION

In both normal weight and overweight children, the ASIR technique is associated with a greater than 57% mean dose reduction, without significantly impacting diagnostic image quality in pediatric chest CT examinations. However, CT scans in overweight children may have a greater noise level, even when using the ASIR technique.

Radiation dose reduction at MDCT with iterative reconstruction for prenatal diagnosis of skeletal dysplasia: preliminary study using normal fetal specimens

OBJECTIVE

The purpose of this study was to investigate to what degree the radiation dose can be reduced without affecting the ability to evaluate normal fetal bones at MDCT with iterative reconstruction.

MATERIALS AND METHODS

Fifteen normal fetal specimens immersed in containers (30- and 35-cm diameter) were scanned with a 64-MDCT scanner, with tube voltage of 100 kVp and tube current of 600, 300, 150, 100, and 50 mA. Images were subjected to adaptive statistical iterative reconstruction (ASIR). The fetal dose was measured using glass dosimeters. We calculated the relative ratio of the dose at 600 mA. Image quality was evaluated on maximum-intensity-projection and volume-rendering images. Two radiologists recorded the visualization scores of five regions. Images at 600 mA were considered to be standard.

RESULTS

With the 30-cm-diameter container, the fetal dose was 10.15 mGy (relative ratio, 100%) at a tube current of 600, 51% at 300, 25% at 150, 17% at 100, and 9% at 50 mA. With the 35-cm-diameter container the fetal dose was 10.01 mGy (relative ratio, 100%) at 600, 47% at 300, 24% at 150, 17% at 100, and 8% at 50 mA. Visual evaluation showed that in both containers, with ASIR 90%, there was a statistically significant difference between 50-and 600-mA images (p<0.01) but not between 600-mA images and those acquired at 100, 150, and 300 mA (p=0.08-1.00).

CONCLUSION

The fetal radiation dose for the evaluation of normal fetal bones can be reduced by 83% with ASIR 90%.

Size-specific dose estimate (SSDE) provides a simple method to calculate organ dose for pediatric CT examinations

PURPOSE

To investigate the correlation of size-specific dose estimate (SSDE) with absorbed organ dose, and to develop a simple methodology for estimating patient organ dose in a pediatric population (5-55 kg).

METHODS

Four physical anthropomorphic phantoms representing a range of pediatric body habitus were scanned with metal oxide semiconductor field effect transistor (MOSFET) dosimeters placed at 23 organ locations to determine absolute organ dose. Phantom absolute organ dose was divided by phantom SSDE to determine correlation between organ dose and SSDE. Organ dose correlation factors (CF(organ)(SSDE)) were then multiplied by patient-specific SSDE to estimate patient organ dose. The [CF(organ)(SSDE)) were used to retrospectively estimate individual organ doses from 352 chest and 241 abdominopelvic pediatric CT examinations, where mean patient weight was 22 kg ± 15 (range 5-55 kg), and mean patient age was 6 yrs ± 5 (range 4 months to 23 yrs). Patient organ dose estimates were compared to published pediatric Monte Carlo study results.

RESULTS

Phantom effective diameters were matched with patient population effective diameters to within 4 cm; thus, showing appropriate scalability of the phantoms across the entire pediatric population in this study. Individual CF(organ)(SSDE) were determined for a total of 23 organs in the chest and abdominopelvic region across nine weight subcategories. For organs fully covered by the scan volume, correlation in the chest (average 1.1; range 0.7-1.4) and abdominopelvic region (average 0.9; range 0.7-1.3) was near unity. For organ/tissue that extended beyond the scan volume (i.e., skin, bone marrow, and bone surface), correlation was determined to be poor (average 0.3; range: 0.1-0.4) for both the chest and abdominopelvic regions, respectively. A means to estimate patient organ dose was demonstrated. Calculated patient organ dose, using patient SSDE and CF(organ)(SSDE), was compared to previously published pediatric patient doses that accounted for patient size in their dose calculation, and was found to agree in the chest to better than an average of 5% (27.6/26.2) and in the abdominopelvic region to better than 2% (73.4/75.0).

CONCLUSIONS

For organs fully covered within the scan volume, the average correlation of SSDE and organ absolute dose was found to be better than ± 10%. In addition, this study provides a complete list of organ dose correlation factors (CF(organ)(SSDE)) for the chest and abdominopelvic regions, and describes a simple methodology to estimate individual pediatric patient organ dose based on patient SSDE.

Quantitative analysis of the effect of iterative reconstruction using a phantom: determining the appropriate blending percentage

PURPOSE

To investigate the optimal blending percentage of adaptive statistical iterative reconstruction (ASIR) in a reduced radiation dose while preserving a degree of image quality and texture that is similar to that of standard-dose computed tomography (CT).

MATERIALS AND METHODS

The CT performance phantom was scanned with standard and dose reduction protocols including reduced mAs or kVp. Image quality parameters including noise, spatial, and low-contrast resolution, as well as image texture, were quantitatively evaluated after applying various blending percentages of ASIR. The optimal blending percentage of ASIR that preserved image quality and texture compared to standard dose CT was investigated in each radiation dose reduction protocol.

RESULTS

As the percentage of ASIR increased, noise and spatial-resolution decreased, whereas low-contrast resolution increased. In the texture analysis, an increasing percentage of ASIR resulted in an increase of angular second moment, inverse difference moment, and correlation and in a decrease of contrast and entropy. The 20% and 40% dose reduction protocols with 20% and 40% ASIR blending, respectively, resulted in an optimal quality of images with preservation of the image texture.

CONCLUSION

Blending the 40% ASIR to the 40% reduced tube-current product can maximize radiation dose reduction and preserve adequate image quality and texture.

Effects of adaptive statistical iterative reconstruction on radiation dose reduction and diagnostic accuracy of pediatric abdominal CT

BACKGROUND

Since children are more radio-sensitive than adults, there is a need to minimize radiation exposure during CT exams.

OBJECTIVE

To evaluate the effects of adaptive statistical iterative reconstruction (ASIR) on radiation dose reduction, image quality and diagnostic accuracy in pediatric abdominal CT.

MATERIALS AND METHODS

We retrospectively reviewed the abdominal CT examinations of 41 children (24 boys and 17 girls; mean age: 10 years) with a low-dose radiation protocol and reconstructed with ASIR (the ASIR group). We also reviewed routine-dose abdominal CT examinations of 41 age- and sex-matched controls reconstructed with filtered-back projection (control group). Image quality was assessed objectively as noise measured in the liver, spleen and aorta, as well as subjectively by three pediatric radiologists for diagnostic acceptability using a four-point scale. Radiation dose and objective image qualities of each group were compared with the paired t-test. Diagnostic accuracy was evaluated by reviewing follow-up imaging studies and medical records in 2012 and 2013.

RESULTS

There was 46.3% dose reduction of size-specific dose estimates in ASIR group (from 13.4 to 7.2 mGy) compared with the control group. Objective noise was higher in the liver, spleen and aorta of the ASIR group (P < 0.001). However, the subjective image quality was average or superior in 84-100% of studies. Only one image was subjectively rated as unacceptable by one reviewer. There was only one case with interpretational error in the control group and none in the ASIR group.

CONCLUSION

Use of the ASIR technique resulted in greater than a 45% reduction in radiation dose without impairing subjective image quality or diagnostic accuracy in pediatric abdominal CT, despite increased objective image noise.

Contrast agent and radiation dose reduction in abdominal CT by a combination of low tube voltage and advanced image reconstruction algorithms

OBJECTIVES

To assess image quality in abdominal CT at low tube voltage combined with two types of iterative reconstruction (IR) at four reduced contrast agent dose levels.

METHODS

Minipigs were scanned with standard 320 mg I/mL contrast concentration at 120 kVp, and with reduced formulations of 120, 170, 220 and 270 mg I/mL at 80 kVp with IR. Image quality was assessed by CT value, dose normalized contrast and signal to noise ratio (CNRD and SNRD) in the arterial and venous phases. Qualitative analysis was included by expert reading.

RESULTS

Protocols with 170 mg I/mL or higher showed equal or superior CT values: aorta (278-468 HU versus 314 HU); portal vein (205-273 HU versus 208 HU); liver parenchyma (122-146 HU versus 115 HU). In the aorta, all 170 mg I/mL protocols or higher yielded equal or superior CNRD (15.0-28.0 versus 13.7). In liver parenchyma, all study protocols resulted in higher SNRDs. Radiation dose could be reduced from standard CTDIvol = 7.8 mGy (6.2 mSv) to 7.6 mGy (5.2 mSv) with 170 mg I/mL.

CONCLUSION

Combining 80 kVp with IR allows at least a 47 % contrast agent dose reduction and 16 % radiation dose reduction for images of comparable quality.

KEY POINTS

• There is a balance between image quality, contrast dose and radiation dose. • Iterative reconstruction has a major, positive impact on this balance. • Both contrast dose and radiation dose can be reduced in abdominal CT. • The trade-off can be quantitatively described by a 3D model. • Contrast and radiation dose can be tailored according to specific safety concerns.

Development of pediatric CT protocols for specific scanners: why bother?

When determining a strategy for pediatric CT scanning, clinical staff can either elect to adjust routine adult-protocol parameter settings on a case-by-case basis or rely on pre-set pediatric protocol parameters. The advantages of the latter approach are the topic of this manuscript. This paper outlines specific options to consider, including the need for regular protocol review.

Iterative reconstruction: how it works, how to apply it

Computed tomography acquires X-ray projection data from multiple angles though an object to generate a tomographic rendition of its attenuation characteristics. Filtered back projection is a fast, closed analytical solution to the reconstruction process, whereby all projections are equally weighted, but is prone to deliver inadequate image quality when the dose levels are reduced. Iterative reconstruction is an algorithmic method that uses statistical and geometric models to variably weight the image data in a process that can be solved iteratively to independently reduce noise and preserve resolution and image quality. Applications of this technology in a clinical setting can result in lower dose on the order of 20-40% compared to a standard filtered back projection reconstruction for most exams. A carefully planned implementation strategy and methodological approach is necessary to achieve the goals of lower dose with uncompromised image quality.

Computed tomography depiction of small pediatric vessels with model-based iterative reconstruction

BACKGROUND

Computed tomography (CT) is extremely important in characterizing blood vessel anatomy and vascular lesions in children. Recent advances in CT reconstruction technology hold promise for improved image quality and also reductions in radiation dose. This report evaluates potential improvements in image quality for the depiction of small pediatric vessels with model-based iterative reconstruction (Veo™), a technique developed to improve image quality and reduce noise.

OBJECTIVE

To evaluate Veo™ as an improved method when compared to adaptive statistical iterative reconstruction (ASIR™) for the depiction of small vessels on pediatric CT.

MATERIALS AND METHODS

Seventeen patients (mean age: 3.4 years, range: 2 days to 10.0 years; 6 girls, 11 boys) underwent contrast-enhanced CT examinations of the chest and abdomen in this HIPAA compliant and institutional review board approved study. Raw data were reconstructed into separate image datasets using Veo™ and ASIR™ algorithms (GE Medical Systems, Milwaukee, WI). Four blinded radiologists subjectively evaluated image quality. The pulmonary, hepatic, splenic and renal arteries were evaluated for the length and number of branches depicted. Datasets were compared with parametric and non-parametric statistical tests.

RESULTS

Readers stated a preference for Veo™ over ASIR™ images when subjectively evaluating image quality criteria for vessel definition, image noise and resolution of small anatomical structures. The mean image noise in the aorta and fat was significantly less for Veo™ vs. ASIR™ reconstructed images. Quantitative measurements of mean vessel lengths and number of branches vessels delineated were significantly different for Veo™ and ASIR™ images. Veo™ consistently showed more of the vessel anatomy: longer vessel length and more branching vessels.

CONCLUSION

When compared to the more established adaptive statistical iterative reconstruction algorithm, model-based iterative reconstruction appears to produce superior images for depiction of small pediatric vessels on computed tomography.

Optimization of hybrid iterative reconstruction level in pediatric body CT

OBJECTIVE

The objective of our study was to attempt to optimize the level of hybrid iterative reconstruction (HIR) in pediatric body CT.

MATERIALS AND METHODS

One hundred consecutive chest or abdominal CT examinations were selected. For each examination, six series were obtained: one filtered back projection (FBP) and five HIR series (iDose(4)) levels 2-6. Two pediatric radiologists, blinded to noise measurements, independently chose the optimal HIR level and then rated series quality. We measured CT number (mean in Hounsfield units) and noise (SD in Hounsfield units) changes by placing regions of interest in the liver, muscles, subcutaneous fat, and aorta. A mixed-model analysis-of-variance test was used to analyze correlation of noise reduction with the optimal HIR level compared with baseline FBP noise.

RESULTS

One hundred CT examinations were performed of 88 patients (52 females and 36 males) with a mean age of 8.5 years (range, 19 days-18 years); 12 patients had both chest and abdominal CT studies. Radiologists agreed to within one level of HIR in 92 of 100 studies. The mean quality rating was significantly higher for HIR than FBP (3.6 vs 3.3, respectively; p < 0.01). HIR caused minimal (0-0.2%) change in CT numbers. Noise reduction varied among structures and patients. Liver noise reduction positively correlated with baseline noise when the optimal HIR level was used (p < 0.01). HIR levels were significantly correlated with body weight and effective diameter of the upper abdomen (p < 0.01).

CONCLUSION

HIR, such as iDose(4), improves the quality of body CT scans of pediatric patients by decreasing noise; HIR level 3 or 4 is optimal for most studies. The optimal HIR level was less effective in reducing liver noise in children with lower baseline noise.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.