Adaptive Statistical Iterative Reconstruction Algorithm Research Articles

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Abstract Introduction Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated the quality of DLIR images and compared it to the quality of images from the latest adaptive statistical iterative reconstruction (ASIR-V) algorithm in terms of noise-power spectrum (NPS) and modulation-transfer function (MTF). Methods We scanned a Revolution QA phantom (GE Healthcare, USA) and a 20 cm water phantom (GE Healthcare, USA) with our 512 multi-slice computed tomography (CT) scanner. Images of the tungsten wire within the Revolution QA phantom were reconstructed with a 50 mm field of view (FOV). The images were reconstructed with various ASIR-V strengths (i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%) and DLIRs (i.e. low, medium, and high) to assess the MTF. The images from the 20 cm water phantom were reconstructed with the same configuration to assess the NPS. Results The MTF was similar for both reconstruction algorithms of DLIR and ASiR-V. The peak frequency (fp) of the DLIR low was comparable to that from ASIR-V at 50, 60, 70%; the DLIR medium was comparable to ASIR-V at 80%; and the DLIR high was comparable to ASIR-V at 100%. The average frequency (fA) of the DLIR low was comparable to that from ASIR-V at 40%; the DLIR medium was comparable to ASIR-V at 50%; and the DLIR high was comparable to ASIR-V at 70%. Both the DLIR and ASIR-V were able to reduce noise, but they had a different texture. Conclusions The noise in the DLIR images was more homogenous at high and low frequencies, while in the ASIR-V images, the noise was more concentrated at high frequencies. The MTF was similar for both reconstruction algorithms. The DLIR method showed a better noise reduction than the ASIR-V reconstruction.

High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses

To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26kg/m2, in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group (n = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group (n = 50) with 120kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal-noise-ratio (SNR) for vessel) were compared between the two groups. Low-dose group used significantly reduced contrast dose (23.82 ± 3.69mL, 50.6% reduction) and radiation dose (0.75 ± 0.14mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38mL and 1.65 ± 0.66mSv, respectively) (all p < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p < 0.001) compared to the conventional group. The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. •The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol. •CCTA of normal size (BMI < 26kg/m2) patients acquired at sub-mSv radiation dose and 24mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement. •DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

PurposeTo compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.MethodsHead CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.ResultsDLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.ConclusionThe image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

CT Image Features under Reconstruction Algorithm in Analysis of the Effect of Probiotics Combined with Ursodeoxycholic Acid in Treatment of Intrahepatic Cholestasis of Pregnancy.

This research was to explore the adoption value of computed tomography (CT) images based on adaptive statistical iterative reconstruction (ASIR) algorithm in the evaluation of probiotics combined with ursodeoxycholic acid in the treatment of intrahepatic cholestasis of pregnancy (ICP). A total of 82 patients with ICP were selected as the research subjects and they were randomly rolled into experimental group (380 mg probiotics enteric-soluble capsule twice a day, combined with 90 mg ursodeoxycholic acid soft capsule three times a day) and control group (90 mg ursodeoxycholic acid soft capsule three times a day), with 41 cases in each. The treatment course was four months. The ASIR algorithm was constructed and applied to the CT image analysis and diagnosis of ICP patients. The effects of filtering back projection (FBP) reconstruction and ASIR algorithm on CT image quality, denoising degree, and artifacts of ICP patients were compared. Moreover, blood indicator levels of ICP patients before and after treatment were assessed. The results showed that the SD values of liver and gallbladder (20.77 Hu and 27.58 Hu) in the reconstructed image of the ASIR algorithm were significantly lower than those of the FBP algorithm (40.58 Hu and 45.63 Hu) (P < 0.05). The SNR values of the liver and gallbladder (3.68 and 2.05) of the reconstructed image were significantly higher than those of the FBP algorithm (1.91 and 1.19) (P < 0.05). The overall image quality after ASIR reconstruction (3.92 points) was significantly better than that of the FBP algorithm (2.36 points), and the image noise score (3.21 points) reconstructed by the FBP algorithm was higher than that by the ASIR algorithm (1.83 points). The artifact score of FBP reconstructed image (4.47 points) was greatly higher than that of ASIR algorithm (2.26 points) (P < 0.05). Before treatment, there was no remarkable difference in the indexes between the two groups of patients (P > 0.05). After treatment, the γ-glutamyltransferase (γ-GT) and alkaline phosphatase (ALP) levels (327.55 U/L and 778.15 μmol/L) of the experimental group of ICP patients were higher than those of the control group (248.63 U/L and 668.43 μmol/L), with substantial difference between the two groups (P < 0.05). The blood ammonia (BA) level (151.09 μmol/L) of the experimental group was lower than that of the control group (178.46 μmol/L), and the difference between the two groups was remarkable (P < 0.05). To sum up, the CT image denoising degree based on ASIR algorithm was high, with few artifacts and good overall quality. Probiotics combined with ursodeoxycholic acid in the treatment of ICP can effectively improve the liver function and intestinal flora of patients, which was of great significance in the clinical diagnosis and treatment of the disease.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

OBJECTIVE:To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose.METHODS:The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared.RESULTS:For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%).CONCLUSION:Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm.

This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower (P < 0.05). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

Evaluation of complex congenital heart disease with prospective ECG-gated cardiac CT in a single heartbeat at low tube voltage (70kV) and adaptive statistical iterative reconstruction in infants: a single center experience.

The purpose of this study is to evaluate the radiation dose, image quality, and diagnostic accuracy of prospective ECG-gated cardiac CT at 70kV and adaptive statistical iterative reconstruction (ASIR), with a single source, 512 slice MDCT in the diagnosis of complex congenital heart disease in infants. We retrospectively evaluated 47 infants (ages 1day to 353days) with prospective ECG-gated cardiac CT that was performed on a single source 512 slice CT at low tube voltage (70kV) using a wide detector aperture, adaptive statistical iterative reconstruction algorithm (ASIR), and specific reconstruction software reducing coronary motion artifacts (SnapShot Freeze). All cardiac images were obtained during the first pass of contrast material through the anatomic structures of interest and the targets for the center of the acquisition window were set 45% of the R-R interval during one cardiac cycle without sedation and breath-hold. The median effective dose measured in our study was 0.64 ± 0.16mSv. The average subjective overall image quality score was 4.34 ± 0.31 (range 3-5). For the determination of objective image quality, Mean Noise (HU), SNR, and CNR values ​​emerged as 20.8, 28.7(for pulmonary artery), and 27.1, respectively. Diagnostic accuracy was 100% for the main purposes for the main clinical indication. During cardiac CT examination, pathologies in addition to cardiac anomalies were found in 9/47 of cases (7 severe airway obstructions,1 posterior diaphragmatic hernia, 1 vertebral anomaly).Prospective ECG-gated cardiac CT scan at 70kV and ASIR in infants with complex CHD provides low radiation dose (submillisievert) in a single heartbeat with a good objective and subjective image quality. It also provides important benefits in the diagnosis of additional pathology.

Performance evaluation of using shorter contrast injection and 70 kVp with deep learning image reconstruction for reduced contrast medium dose and radiation dose in coronary CT angiography for children: a pilot study.

Iterative reconstruction algorithms are often used to reduce image noise in low-dose coronary computed tomography angiography (CCTA) but encounter limitations. The newly introduced deep learning image reconstruction (DLIR) algorithm may provide new opportunities. We assessed the image quality and diagnostic performance of DLIR in low radiation dose and contrast medium dose CCTA of pediatric patients with 70 kVp and a shortened injection protocol. This was a prospective study. A total of 27 consecutive arrhythmic pediatric patients were enrolled in the study group and underwent CCTA using a prospective ECG-triggered single-beat protocol: tube voltage 70 kVp, automatic tube current modulation for a noise index (NI) of 22, and contrast dose of 0.4-0.6 mL/kg. Images were reconstructed with DLIR. They were compared with 27 matched patients in the control group scanned with 80 kVp, a lower NI setting (NI =19), and a higher contrast dose (0.8-1.2 mL/kg). The images in the control group were reconstructed using the adaptive statistical iterative reconstruction (ASIR-V) algorithm. The image contrast, image quality, and diagnostic confidence were assessed by 2 experienced radiologists using a 5-point scale (1: nondiagnostic and 5: excellent). The CT value and standard deviation of the aorta and perivascular tissue were measured, and the contrast-to-noise ratio (CNR) for the aorta was calculated. The contrast medium and radiation doses were compared. The study and control groups had similar image contrast scores (4.75±0.57 vs. 4.78±0.42), image quality scores (3.67±0.47 vs. 3.44±0.51), and diagnostic confidence (4.74±0.44 vs. 4.74±0.45) (all P>0.05). There was an adequate enhancement in the aorta (614.74±127.73 vs. 705.89±111.20 HU) and similar CNR (20.34±4.64 vs. 20.99±4.14) in both groups. The image noise of the study group was lower in the aorta (30.61±3.88 vs. 34.77±3.49) and similar in perivascular tissue (27.66±6.24 vs. 27.55±3.33) compared with the control group. The study group reduced the total contrast medium dose by 53% to 15.07±3.68 mL and radiation dose by 36% to 0.57±0.31 mSv. The DLIR algorithm in CCTA for children using 70 kVp tube voltage with a shortened contrast medium injection protocol maintains image quality and diagnostic confidence while significantly reducing contrast medium dose and radiation dose compared with the use of the conventional CCTA protocol.

Efficacy of Low Dose Computed Tomography Using Adaptive Statistical Iterative Reconstruction in Lung Cancer Screening

Abstract Background: Lung cancer is the most common malignant tumor and the leading cause of cancer-related deaths all over the world. Early detection and treatment are important to increase survival rate. Therefore, screening of lung cancer should target those individuals at high risk. Aim of Study: To detect the efficacy of low dose computed tomography (LDCT) of the chest with adaptive statistical iterative reconstruction (ASIR) algorithmas a screening method in diagnosing early stages of lung cancer and thus decrease the disease related morbidity and mortality as itminimizes exposure to ionizing radiation while maintaining sufficient image quality. Material and Methods: Ninety-six high risk cases (current smokers or ex-smokers), 94 males and 2 females, withage range from 50-78 yearswere included in this study. All cases underwent CT of the chest using low dose protocol with-ASIR algorithm with different blending levels of reconstruction (40%, 60%) for lung cancer screening. All images were interpreted using the International Early Lung Cancer Action Program (I-ELCAP) diagnostic protocol for lung nodule diagnosis and management. Results: From 96 cases included in the study, 82 cases had normal chest CT and 14 cases showed abnormal findings in chest CT (4 of them showed nodular lung lesions and the other 10 cases showed other findings related to smoking as bullae, bronchial wall thickening, emphysema, honey combing and bronchiectasis). Among the 6 nodules detected in the 4 cases, one nodule was >!15mm, and 5 nodules were <15mm. According to I-ELCAP diagnostic protocol, one nodule was considered positive and 5 nodules were considered semi-positive. LDCT chest using ASIR algorithm for lung cancer screening revealed high sensitivity and specificity for detecting lesions at cut off point size 6 mm (84.9% and 100% respec-tively). The area under receiver operating characteristic (ROC) curve for prediction of nodule with cutoff point size 6mm was 0.938 with 95% confidence interval (0.887-0.990). Conclusion: LDCT of the chest using ASIR algorithm is a promising and efficient tool for lung cancer screening with significant minimization of ionizing radiation exposure as well as preserved optimum image quality.

Imaging pediatric acute head trauma using 100-kVp low dose CT with adaptive statistical iterative reconstruction (ASIR-V) in single rotation on a 16 cm wide-detector CT.

To demonstrate the ability of achieving low dose and high-quality head CT images for children with acute head trauma using 100 kVp and adaptive statistical iterative reconstruction (ASIR-V) algorithm in single rotation on a 16 cm wide-detector system. We retrospectively analyzed the CT dose index (CTDI) and image quality of 104 children aged 0-6 years with acute head trauma (1 hour -3 days) in two groups: Group 1(n = 50) on a 256-row CT with single rotation at a reduced-dose of 100 kVp/240 mA and reconstructed using ASIR-V at 70%level; Group 2(n = 54) on a 64-row CT with multiple rotations at a standard dose of 120 kVp/ 180mA and reconstructed using a conventional filtered back-projection (FBP). Both groups used the 0.5 s/r axial scan mode. CT dose index (CTDI) and quantitative image quality measurements were compared using the Student t test; qualitative image quality comparison was carried out using Mann-Whitney rank test and the inter-reviewer agreement was evaluated using Kappa test. The exposure time was 0.5 s for Group 1 and 3.27±0.29 s for Group 2. The CTDI in Group 1 was 9.74±0.86mGy, 36.38%lower than the 15.31mGy in Group 2 (p < 0.001). Group 1 and Group 2 had similar artifact index (2.06±1.06 vs. 2.37±1.18) in the cerebellar hemispheres, and similar contrast-to-noise ratio (2.32±0.83 vs. 1.69±0.68), (1.47±0.72 vs. 1.10±0.43) respectively for cerebellum and thalamus (p > 0.05). Image quality was acceptable for diagnosis, and motion artifacts were reduced in Group 1 (p < 0.001). Single rotation CT with 100 kVp and 70%ASIR-V on 16 cm wide-detector CT reduces radiation dose and motion artifacts for children with acute head trauma without compromising diagnostic quality as compared with standard dose protocol. Thus, it provides a novel imaging method in management of pediatric acute head trauma.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

A phantom study using dual-energy spectral computed tomography imaging: Comparison of image quality between two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms for evaluating ground-glass nodules of the lung.

To assess the image quality of the two adaptive statistical iterative reconstruction (ASiR and ASiR-V) algorithms for evaluating ground-glass nodules (GGNs) of the lung. The chest phantom with ground-glass simulated pulmonary nodules was scanned by dual-energy spectral computed tomography (CT) using gemstone spectral imaging mode. The image was reconstructed with ASiR and ASiR-V from 0 (FBP) to 90% blending percentages, respectively. The average noise and signal-to-noise ratio (SNR) of reconstruction images were calculated. The data were statistically analyzed. Compared with FBP, as the percentage of ASiR and ASiR-V increased from 10 to 90%, image noise reduced by 3.7%-45.2% and 14.1%-73.8%, respectively, and the SNR increased accordingly. There was significantly higher SNR value of ASiR-V images as the percentages of IR increased to 50% or greater, compared to those of ASiR. Subjective image quality scores of ASiR and ASiR-V improved significantly as percentage increased from 10 to 80% for ASiR and ASiR-V (peaked at 60% for both of them). Both ASiR and ASiR-V can reduce the image noise and improve the objective image quality for presenting pulmonary nodules on dual-energy spectral CT imaging compared with FBP. ASiR-V provided further image quality improvement than ASiR, and both ASiR and ASiR-V 60% had the highest image quality score.

Adaptive statistical iterative reconstruction for computed tomography of the spine

IntroductionThe utility of evaluating a sagittal view of CT of the spine is well-known. In many clinical cases, the sagittal view includes noise generated from surrounding objects and may degrade the image quality. Iterative reconstruction (IR) techniques are useful for noise reduction; however, they can reduce spatial resolution. The aim of this study was to evaluate the effectiveness of the adaptive statistical iterative reconstruction (ASiR) for generating sagittal CT images of the spine when compared to filtered back projection (FBP). MethodsThe image quality of clinical images from 25 patients were subjectively assessed. Three radiologists rated spatial resolution, image noise, and overall image quality using a five-point scale. For objective assessment, z-direction modulation transfer function (z-MTF) was measured using a custom-made phantom. Additionally, z-axis noise power spectrum (z-NPS) was measured using a water phantom. An improved adaptive statistical iterative reconstruction algorithm called ASiR-V was used in this study. Blending levels were 50%, and 100% (ASiR-V50, ASiR-V100, respectively). ResultsFor subjective assessments, images using ASiR-V100 were determined to have the best overall image quality, despite having received the worst score in the assessment of spatial resolution. For objective assessments, the image using ASiR-V50 and ASiR-V100 curves were slightly degraded in terms of low contrast z-MTF when compared to FBP. ConclusionASiR-V was effective to improve the image quality when compared with FBP when reviewing sagittal reformats of the spine. Implications for practiceThis study suggests that high resolution is not the only thing that is key when reviewing sagittal CT spinal reformats. Such images should be provided as part of routine CT spine protocols, where available.

Increasing iterative reconstruction strength at low tube voltage in coronary CT angiography protocols using 3D-printed and Catphan® 500 phantoms.

PurposeThe purpose of this study was to investigate the effect of increasing iterative reconstruction (IR) algorithm strength at different tube voltages in coronary computed tomography angiography (CCTA) protocols using a three‐dimensional (3D)‐printed and Catphan® 500 phantoms.MethodsA 3D‐printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal–noise ratio (SNR), contrast–noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed.ResultsThere was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp.ConclusionsChanging the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D‐printed and Catphan® 500 phantoms could be used to perform CT dose optimization protocols.

Optimal Monochromatic Energy Levels in Dual-Energy Spectral CT Pulmonary Angiography with Low Contrast Medium Dosage

Background: The contrast medium (CM) in CT pulmonary angiography may induce adverse effects to patients, and higher CM is associated with higher rates of contrast-induced-nephropathy and mortality. Reduction of CM dosage through improvement of examination techniques may help reduce the occurrence of CM-induced adverse effects and healthcare costs. Objectives: To determine the optimal monochromatic energy levels in dual-energy spectral CT pulmonary angiography (CTPA) with low contrast dosage. Patients and Methods: Thirty patients with suspected pulmonary embolism (PE) underwent dual-energy spectral CTPA with low radiation and low contrast doses with scanning protocol of GSI-36 with 260 mA, and 25 mL contrast (350 mgI/mL) with 4.0ml/s injection speed. The monochromatic images from 60 - 80 keV (interval 5 keV) were reconstructed using a 50% adaptive statistical iterative reconstruction (50% ASiR) algorithm at 1.25 mm slice thickness. The CT attenuation and standard deviation (SD) values of the main, right, left, right lower and left lower pulmonary arteries and the back muscle at the same level were measured on 60 – 80 keV images, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and analyzed. The subjective image quality was evaluated by two experienced radiologists using a 5-level scoring method independently. Measurements were analyzed using IBM SPSS Statistics for Windows, version 25.0. (Armonk, NY: IBM Corp.). Results: CT attenuation values of the pulmonary arteries decreased with the increase of energy level in five-energy groups, with values greater than 300 HU at 60 keV - 70 keV energies. The 65 keV image had the highest SNR, CNR and lowest SD, with significant differences compared with those of other image sets (P &lt; 0.05). The subjective quality scores for the 65 keV image was judged to be the highest by the two radiologists, but it was not significantly different from 60 keV and 70 keV (all P &gt; 0.05). Conclusion: The 65 keV monochromatic images provided the highest SNR, CNR and subjective scores with the lowest image noise in dual-energy spectral CTPA with low contrast dosage.

Experimental study on the effect of adaptive statistical iterative reconstruction algorithms on image quality and radiation dose in paranasal sinus CT

Objective To explore the effect of pre- and post-adaptive statistical iterative reconstruction-Veo (ASiR-V) on image quality and radiation dose in paranasal sinus CT, and to find the best combinations. Methods One head specimen was scanned with the routine spiral CT scanning parameters [noise index (NI)=8] and different levels of pre-ASiR-V (0—100%, with an interval of 10%). The images were reconstructed with different post-ASiR-V (0—100%, with an interval of 10%) for the bone algorithm and standard algorithm. All of 242 thin-layer images of paranasal sinuses were obtained. The region of interest (ROI) was selected to measure the CT value to calculate the contrast to noise ratio (CNR) and figure of merit (FOM). The volume CT dose index (CTDIvol) and Smart mA were recorded. The linear regression was conducted to analyze the relationship between CTDIvol, SmartmA, CNR and FOM. And with the same pose-ASiR-V level, the CNR of images which reconstructed by bone and soft algorithms were compared with pair-wise t test. The image quality was subjectively evaluated by three independent experienced radiologists using a 4-point scale (4 was the best). Results As the pre-ASiR-V levels (0—100%) increased, Smart mA and CTDIvol were reduced with a linear negative correlation (r=-0.981, -0.976, both P<0.001). The Smart mA decreased by 72.05% and CTDIvol by 71.22%. Keeping the same pre-ASiR-V level,the CNR increased with the increase of post-ASiR-V level (for the bone algorithm images:R2=0.976, 0.992, 0.982, 0.982, 0.975, 0.975, 0.979, 0.996, 0.952, 0.978, 0.965;for the standard algorithm images: R2=0.944, 0.990, 0.988, 0.993, 0.996, 0.987, 0.984, 0.996, 0.996, 0.990, 0.965).Under the same level of post-ASiR-V, the CNR and FOM fluctuated with the pre-ASiR-V level (for the bone algorithm images:R2=0.335, 0.341, 0.344, 0.364, 0.385, 0.405, 0.418, 0.429, 0.455, 0.474, 0.516; for the standard algorithm images: R2=0.223, 0.278, 0.327, 0.285, 0.309, 0.329, 0.325, 0.346, 0.360, 0.390, 0.380). All subjective image quality could meet the diagnostic requirements (the score≥3). Conclusion At NI=8, for the bone algorithm, the best combination is 80% pre-ASiR-V and 100% post-ASiR-V; for the standard algorithm, the best iteration combination is 100% and 100%. The appropriate choice of pre- and post-ASiR-V levels in paranasal sinus CT scan can effectively reduce the radiation dose under the premise of maintaining the image quality that meets the diagnostic needs. Key words: Paranasal sinuses; Tomography, X-ray computed; Radiation dosage; Image processing, computer-assisted; Image quality

Feasibility study of using one-tenth mSv radiation dose in young children chest CT with 80 kVp and model-based iterative reconstruction

CT has become a routine imaging modality based on its excellent ability of displaying lung structures and diseases. But, how to reduce radiation dose of routine CT examination is a concern for radiologists. Our study aimed to evaluate the feasibility of using 80kVp and a model-based iterative reconstruction (MBIR) algorithm to achieve one-tenth mSv dose chest CT in infants and young children. Thirty-two cases (study group, average age 1.71 ± 1.01 years) underwent non-contrast chest CT examination at low dose with 80 kV, 4mAs and was reconstructed with MBIR (LD-MBIR) and the standard adaptive statistical iterative reconstruction (ASIR) algorithm (LD-ASIR); another group (control group) of 32 children underwent routine-dose chest CT with 100 kV and was reconstructed with ASIR only (RD-ASIR). The subjective and objective image quality of the three groups were measured and statistically compared. The radiation dose for the low dose scan was 0.09 ± 0.02 mSv, 6% of the routine dose. All LD-MBIR images were diagnostically acceptable. Compared with the RD-ASIR images, the LD-MBIR images were similar in noise in the left ventricle, muscles, lung field, on-par in displaying large airways, lung lucency and mediastinum, but were inferior in displaying lung marking, small airways and mediastinum. Thus, MBIR images with low dose in pediatric chest CT can be used in the diagnosis for lung field and air way disorders in infants and young children.

Application of 640-slice CT wide-detector volume scan in low-dose CT pulmonary angiography.

Computed tomography (CT) pulmonary angiography (CTPA) examination has been frequently applied in detecting suspected pulmonary embolism (PE). How to reduce radiation dose to patients is also of concern. To assess the value of using 640-slice CT wide-detector volume scan with adaptive statistical iterative reconstruction (ASIR) algorithm in low-dose CTPA. Fifty-eight patients who performed with CTPA were divided into two groups randomly. In the first experimental group (n = 30), ASIR combined with volume scan were performed on the patients, while in the second conventional group (n = 28), patients received ASIR combined with conventional spiral scan. General data including age and body mass index, image quality, pulmonary arterial phase, and radiation dose were analyzed by t test in the two groups. In both groups, all images revealed the 5-order or higher pulmonary arterial branches and fully met the needs for clinical diagnosis. There was no statistical difference in general data between the two groups. In terms of pulmonary phase accuracy, compared with the conventional group, images at pulmonary arterial phase could be captured more accurately in the experimental group. CTDI in the experimental group decreased by 30% compared with that in the conventional group. The actual radiation dose in the experimental group was 1.5 mSv, which is reduced by 53% compared to that in the conventional group. Compared with the conventional spiral scan, using 640-slice CT volume scan with ASIR in CTPA is more accurate in scanning phase and has lower radiation dose. There is no significant difference in image quality between the two groups.

Evaluation of Adaptive Statistical Iterative Reconstruction-V Reconstruction Algorithm vs Filtered Back Projection in the Detection of Hypodense Liver Lesions: Reader Performance and Preferences.

The aim of the study was to evaluate diagnostic accuracy and readers' experience in the detection of focal liver lesions on computed tomography with Adaptive Statistical Iterative Reconstruction-V (ASIR-V) reconstruction compared with filtered back projection (FBP) scans. Fifty-five patients with liver lesions had FBP and ASIR-V scans. Two radiologists independently reviewed both sets of computed tomography scans, identifying and characterizing liver lesions. Adaptive Statistical Iterative Reconstruction-V scans had a reduction in dose length product (P < 0.0001) with no difference in image contrast (P = 0.1805); image noise was less for the ASIR-V scans (P < 0.0001) and contrast-to-noise ratio was better for ASIR-V (P = 0.0002). Both readers found more hypodense liver lesions on the FBP (P = 0.01) scans. Multiple subjective imaging scores were significantly less for the ASIR-V scans for both readers. Although ASIR-V scans were objectively better, our readers performed worse in lesion detection on them, suggesting a need for better education/experience with this technology during implementation.

Influence of Adaptive Statistical Iterative Reconstruction (ASIR) algorithm on Dose Reduction and Image Quality in CT Chest with contrast Examination compared with the FBP techniques

Influence of Adaptive Statistical Iterative Reconstruction (ASIR) algorithm on Dose Reduction and Image Quality in CT Chest with contrast Examination compared with the FBP techniques