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.

Effects of Adaptive Statistical Iterative Reconstruction-V Technology on the Image Quality and Radiation Dose of Unenhanced and Enhanced CT Scans of the Piglet Abdomen

To investigate the optimal pre- and post-adaptive statistical iterative reconstruction-V (ASiR-V) levels in pediatric abdominal computed tomography (CT) to minimize radiation exposure and maintain image quality using an animal model. A total of 10 standard piglets were selected and scanned to obtain unenhanced and enhanced images under different pre-ASiR-V conditions. The corresponding images were obtained using ASiR-V algorithm at different post-ASiR-V levels. CT value, signal-to-noise ratio (SNR), contrast noise ratio (CNR) of abdominal tissues, subjective image score, and radiation dose of unenhanced and enhanced scans were analyzed. With the increase of pre-ASiR-V level, the radiation dose in piglets gradually decreased (P &lt; 0.05). Within the same group of pre-ASiR-V, the image noise was decreased (P &lt; 0.05) by increasing post-ASiR-V level. There was no statistical difference between SNR and CNR values. In unenhanced CT, the subjective score of the images with the combination of 40% pre- and 60% post-ASiR-V levels had no statistical difference compared to the combination of 0% pre- and 60% post-ASiR-V levels, while the radiation dose decreased by 31.6%. In the enhanced CT, the subjective image score with the 60% pre- and 60% post-ASiR-V combination had no statistical difference compared to the 0% pre- and 60% post-ASiR-V combination, while the radiation dose was reduced by 48.9%. The combined use of pre- and post-ASiR-V maintains image quality at the reduced radiation dose. The optimal level for unenhanced CT is 40% pre-combined with 60% post-ASiR-V, while that for enhanced CT is 60% pre- combined with 60% post-ASiR-V in pediatric abdominal CT.

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.

Opportunities to reduce the radiation exposure during computed tomography to assess the changes in the lungs in patients with COVID-19: use of adaptive statistical iterative reconstruction

BACKGROUND: Several COVID-19 patients are subjected to multiple imaging examinations during hospitalization, the cumulative effect of which can significantly increase the total dose of radiation received. The effective radiation dose can be reduced by lowering the current and voltage of the X-ray tube, but this reduces image quality. One possible solution is to use adaptive statistical iterative reconstruction technology on the raw CT data. Recently, data on the efficacy of low-dose CT (LDCT) in the diagnosis of COVID-19 have appeared in the literature. AIM: To analyze the quality and diagnostic value of LDCT images of the lungs after applying an iterative processing algorithm and to assess the possibility of reducing the radiation load on the patient when diagnosing COVID-19. MATERIALS AND METHODS: Patients from the Infectious Diseases Department of the Moscow State University Hospital participated in the prospective study. CT examinations were performed at the time of patient admission and discharge and were repeated as needed during hospitalization. In the first study, a standard CT protocol with a tube voltage of 120 kV and automatic current modulation in the range of 200400 mA was used; in repeated CT scans, the LDKT protocol was used with reduced tube voltage parameters (100 or 110 kV) and automatic current modulation in the range of 40120 mA. To assess the diagnostic value of LDCT in comparison with standard CT, a survey was conducted among doctors from the Department of Radiation Diagnostics at Moscow State University Hospital. The questionnaire included a comparison of the two methods for identifying the following pathological processes: ground-glass opacities, compaction of the lung tissue with reticular changes, areas of lung tissue consolidation, and lymphadenopathy. RESULTS: The study included 151 patients. The average age was 5814.2 years, with men accounting for 53.6% of the population. During LDCT the radiation load was reduced by 2.96 times on average, CTDI by 2.6 times, DLP by 3.1 times, the current on the tube by 1.83 times, and the voltage on the tube by 1.2 times. The results indicate that the effectiveness of detecting the main signs of viral pneumonia and assessing the dynamics of the patients condition does not differ significantly from CT performed according to the standard protocol. CONCLUSIONS: The results of a comparison of standard and low-dose CT show that there is no significant loss of diagnostic information and image quality as the radiation load is reduced. Thus, chest LDCT can be used to successfully diagnose COVID-19 in routine practice.

Influence of Deep Learning Image Reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on Coronary Artery Calcium Quantification

Deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) has been used for cardiac computed tomography imaging. However, DLIR and ASIR-V may influence the quantification of coronary artery calcification. This study aimed to investigate the effects of DLIR and ASIR-V on coronary calcium quantification compared to traditional filtered back projection (FBP). CT images of 96 patients were reconstructed by FBP, ASIR-V 50%, and three levels of DLIR (low [L], medium [M], and high [H], respectively). Image noise decreased significantly with ASIR-V 50% and increasing DLIR levels from L to H in comparison with FBP (all P < 0.001). There is a significantly decline with ASIR-V 50% and incremental DLIR levels in Agatston calcium score, volume score and mass score as compared to FBP (all P < 0.001). For all CAC score risk categories, Severity classification shows no significant differences among five reconstructions (all P > 0.05). DLIR-L has the minimal effect on coronary calcium quantification as compared to ASIR-V and DLIR at medium and high levels. it may be considered as an alternative to FBP for routine clinical use.

CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning–based reconstruction

Objectives The aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared. Methods A temperature-regulating water circuit system was developed for the systematic evaluation of the correlation between temperature and Hounsfield units (HU). The measurements were performed on a Revolution CT with gemstone spectral imaging technology (GSI). Complementary measurements were performed without GSI (voltage 120 kVp, current 130–545 mA). The measured object was a tissue equivalent phantom in a temperature range of 18 to 50°C. The evaluation was carried out for VMI at 40 to 140 keV and polychromatic 120-kVp images. Results The regression analysis showed a significant inverse linear dependency between temperature and average HU regardless of ASIR-V and DLIR. VMI show a higher temperature sensitivity compared to polychromatic images. The temperature sensitivities were 1.25 HU/°C (120 kVp) and 1.35 HU/°C (VMI at 140 keV) for fat, 0.38 HU/°C (120 kVp) and 0.47 HU/°C (VMI at 40 keV) for muscle and 1.15 HU/°C (120 kVp) and 3.58 HU/°C (VMI at 50 keV) for bone. Conclusions Dual-energy with VMI enables a higher temperature sensitivity for fat, muscle and bone. The reconstruction with ASIR-V and DLIR has no significant influence on CT-based thermometry, which opens up the potential of drastic dose reductions. Key Points • Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images. • With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images. • The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning–based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.