CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction

Model-based type iterative reconstruction algorithms with fast reconstruction times are now available. The clinical feasibility of their reconstruction has not been evaluated adequately. To investigate the effects of model-based type iterative reconstruction, i.e. iterative model reconstruction (IMR), with fast reconstruction time on the qualitative and quantitative image quality at low-dose chest computed tomography (CT). Thirty-one patients undergoing low-dose screening chest CT were enrolled. Images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (HIR), and IMR algorithms. The CT attenuation and image noise for all reconstructions were calculated at the lung apex, middle, and base. Using a 4-point scale, two reviewers visually evaluated the image quality with respect to vessel sharpness, streak artifact, the mediastinum, and the overall image quality of each reconstruction method. The mean estimated effective dose was 1.0 ± 0.3 mSv. There was no significant difference in the CT attenuation among the three reconstructions. The mean image noise of FBP, HIR, and IMR images was 124.3 ± 57.3, 34.8 ± 10.2, and 22.9 ± 5.8 HU, respectively. There were significant differences for all comparison combinations among the three methods (P < 0.01). The best subjective overall image quality for the lung and mediastinum was obtained with IMR (P < 0.01). The reconstruction time for IMR was within 3 min in all cases. At low-dose chest CT, IMR can improve the qualitative and quantitative visualization of both lung and mediastinal structures especially in the lung apex at a clinically acceptable reconstruction time. Its application may improve diagnostic performance.

ObjectiveTo evaluate the impact of iterative reconstruction (IR) on the assessment of diffuse interstitial lung disease (DILD) using CT.Materials and MethodsAn American College of Radiology (ACR) phantom (module 4 to assess spatial resolution) was scanned with 10–100 effective mAs at 120 kVp. The images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), with blending ratios of 0%, 30%, 70% and 100%, and model-based iterative reconstruction (MBIR), and their spatial resolution was objectively assessed by the line pair structure method. The patient study was based on retrospective interpretation of prospectively acquired data, and it was approved by the institutional review board. Chest CT scans of 23 patients (mean age 64 years) were performed at 120 kVp using 1) standard dose protocol applying 142–275 mA with dose modulation (high-resolution computed tomography [HRCT]) and 2) low-dose protocol applying 20 mA (low dose CT, LDCT). HRCT images were reconstructed with FBP, and LDCT images were reconstructed using FBP, ASIR, and MBIR. Matching images were randomized and independently reviewed by chest radiologists. Subjective assessment of disease presence and radiological diagnosis was made on a 10-point scale. In addition, semi-quantitative results were compared for the extent of abnormalities estimated to the nearest 5% of parenchymal involvement.ResultsIn the phantom study, ASIR was comparable to FBP in terms of spatial resolution. However, for MBIR, the spatial resolution was greatly decreased under 10 mA. In the patient study, the detection of the presence of disease was not significantly different. The values for area under the curve for detection of DILD by HRCT, FBP, ASIR, and MBIR were as follows: 0.978, 0.979, 0.972, and 0.963. LDCT images reconstructed with FBP, ASIR, and MBIR tended to underestimate reticular or honeycombing opacities (-2.8%, -4.1%, and -5.3%, respectively) and overestimate ground glass opacities (+4.6%, +8.9%, and +8.5%, respectively) compared to the HRCT images. However, the reconstruction methods did not differ with respect to radiologic diagnosis.ConclusionThe diagnostic performance of LDCT with MBIR was similar to that of HRCT in typical DILD cases. However, caution should be exercised when comparing disease extent, especially in follow-up studies with IR.

Image Quality Assessment in Torso Phantom Comparing Effects of Varying Automatic Current Modulation with Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction Techniques in CT

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

Purpose: To determine the potential for dose reduction with adaptive statistical iterative reconstruction (ASiR) and model‐based iterative reconstruction (MBIR) as compared to conventional filtered back‐projection (FBP) reconstruction methods. Methods: The low‐contrast module of a Catphan 600 phantom was scanned using a GE Discovery HD750, at 120 kVp, 0.8 second rotation time, and pitch factors of 0.516, 0.984 and 1.375. The mA was selected for each of the three pitch factors to achieve CTDIvol values of 24, 18, 12, 6, 3, 2 and 1 mGy. Images were reconstructed at 2.5mm thickness with FBP; 20%, 40% and 70% ASiR; and MBIR. The 1% contrast, 15mm diameter supra‐slice object and adjacent background were selected to determine the noise and CNR. To minimize statistical variations in noise, for each scan condition, images from 10 independent acquisitions were analyzed. The results were confirmed in the anthropomorphic Kyoto Kagaku abdomen phantom at 6, 3, 2 and 1 mGy. Results: Compared to FBP, MBIR considerably reduced noise and improved CNR at all dose levels. At low doses in particular, a five‐fold decrease in noise and four‐fold increase in CNR were observed. ASiR also improved image quality compared to FBP across the range of dose values investigated, but to a lesser degree than MBIR. At 70% ASiR, up to a two‐fold increase in CNR and 50% noise reduction were achieved. The quantitative results over 3 different pitch factors were similar. By maintaining image quality at the level of 1 mGy, dose reduction was estimated for MBIR and ASiR compared to FBP. Up to 97% dose saving was achieved with MBIR and up to 15% for ASiR (70%). Conclusion: Both MBIR and ASiR maintained image quality at substantially reduced dose levels as compared to FBP. MBIR demonstrated the feasibility for low‐dose patient imaging at sub‐mGy dose levels.

This study aimed to explore the diagnostic value of chest computed tomography (CT) based on adaptive statistical iterative reconstruction (ASiR) in the optimal weight range for children with community-acquired pneumonia (CAP) and find the differences between chest CT and ultrasound in the diagnosis of CAP. 106 children who were diagnosed as CAP were selected as the research objects. All objects underwent CT and ultrasound scans to measure the noise of CT images after iterative reconstruction and the contrast-to-noise ratio (CNR) of the thoracic aorta and the back muscles. The correlation between ASiR weight, noise, and CNR was analyzed and compared further. In addition, the differences in imaging manifestations of lung consolidation, abnormal pleural line, alveolar interstitial syndrome, and pleural effusion were observed under the ultrasound and CT scans of the patients. The sensitivity, specificity, positive predicted value (PV), and negative PV of the CT scan and ultrasound were analyzed further for the diagnosis of CAP in children. The results showed that ASiR weight and image noise were extremely and negatively correlated ( P &lt; 0.001 ) and that CNR and ASiR weight were extremely and positively correlated ( P &lt; 0.001 ). 40–60% of ASiR image noise was in the middle position, and the muscle space and subcutaneous fat in CT images were clearer when ASiR was at 40% and 60%. 60.38% of children were diagnosed as CAP. The proportions of children with lung consolidation, interstitial changes, pleural effusion, and atelectasis after ultrasound and CT examination were not greatly different ( P &gt; 0.05 ). After ASiR, the sensitivity of CT in detecting the abnormal pleural line + B-line/consolidation feature was the highest, 93.75%. The results of ultrasound diagnosis were consistent with those of chest CT diagnosis ( P &lt; 0.01 ). This indicated that the sensitivity, specificity, positive PV, and negative PV of ASiR-based chest CT in the diagnosis of CAP were close to those of ultrasound.

Purpose To determine if model-based iterative reconstruction (MBIR) can improve visualization of the Adamkiewicz artery on multi–detector row computed tomographic (CT) images compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). Materials and Methods This retrospective study was approved by the institutional review board, and written informed consent for the CT examination was obtained. Thirty-three patients underwent contrast material–enhanced 64-section multi–detector row CT for assessment of aortic aneurysm or dissection. Helical data were reconstructed by using FBP, ASIR, and MBIR. The signal-to-noise ratio of the aorta and contrast-to-noise ratio of the anterior spinal artery relative to the spinal cord were measured on multiplanar reformatted images. Visualization of the Adamkiewicz artery and its continuity with the intercostal or lumbar artery were evaluated by using a four-point scale. All image analyses were performed by two blinded, independent observers. The one-way analysis of variance and the Wilcoxon signed-rank test were used for statistical analysis. Results MBIR showed significantly better signal-to-noise and contrast-to-noise ratios than did ASIR and FBP (P < .05 for all comparisons) with good interobserver agreement (intraclass correlation coefficient of 0.93 for signal-to-noise ratio and 0.75 for contrast-to-noise ratio). The visualization score of the Adamkiewicz artery was also significantly better when MBIR was used (3.4 ± 0.8 and 3.6 ± 0.7 for observers A and B, respectively) than when ASIR (2.7 ± 1.1 and 3.0 ± 1.0, respectively) or FBP (2.5 ± 1.2 and 3.1 ± 0.9, respectively) was used. Conclusion Use of the MBIR algorithm led to improved multi–detector row CT visualization of the Adamkiewicz artery when compared with the use of ASIR and FBP. © RSNA, 2013

BackgroundIterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).PurposeTo examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.Material and MethodsThe LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.ResultsThe image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).ConclusionAn ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction

Radiation dose reduction in CT with adaptive statistical iterative reconstruction (ASIR) for patients with bronchial carcinoma and intrapulmonary metastases

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79–13.2 mGy, 0.90–4.40 mGy, and 0.20–0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66–6.60 mSv, 0.50–0.80 mGy, and 0.39–0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2–4, whereas ULD reduced the dose reduction by a factor of 8–13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

Model-based iterative reconstruction in CT of paranasal sinuses in cystic fibrosis

We evaluated the feasibility of using quantitatively measured thoracic components, as compared to body mass index (BMI), for predicting the image noise of coronary computed tomography angiography (CCTA). One hundred subjects (M:F=64:36; mean age, 55±8.8years) who underwent prospective electrocardiography-gated CCTA and low-dose chest computed tomography (CT) were analyzed retrospectively. The image noise of the CCTA was determined by the standard deviation of the attenuation value in a region of interest on the aortic root level. On the low-dose chest CT, the areas of the thoracic components were measured at the aortic root level. An auto-segmentation technique with the following threshold levels was used: quantitatively measured area of total thorax [QMAtotal: -910 to 1000 Hounsfield units (HU)], lung (QMAlung: -910 to -200 HU), fat (QMAfat: -200 to 0 HU), muscle (QMAmuscle: 0-300 HU), soft tissue (fat+muscle, QMAsoft tissue: -200 to 300 HU), bone (QMAbone: 300-1000 HU) and solid tissue (fat+muscle+bone, QMAsolid tissue: -200 to 1000 HU). The relationship between image noise and variable biometric parameters including QMA was analyzed, and the linear correlation coefficients were used as indicators of the strength of association. Among the variable biometric parameters, including BMI, QMAsolid tissue showed the highest correlation coefficient with image noise in all subjects (r=0.804), males (r=0.716), females (r=0.889), the overweight (r=0.556), and the non-overweight subgroups (r=0.783). QMAsolid tissue can be used as a potential surrogate predictor of the image noise level in low tube voltage CCTA.

To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs. CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDIvol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP). Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP. Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDIvol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDIvol: 0.10 mGy).

Low-dose chest CT for preoperative screening for SARS-CoV-2 infection