Image quality comparison between model-based iterative reconstruction and adaptive statistical iterative reconstruction chest computed tomography in cystic fibrosis patients

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ  =  0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.

State-of-the-art review of lung imaging in cystic fibrosis with recommendations for pulmonologists and radiologists from the "iMAging managEment of cySTic fibROsis" (MAESTRO) consortium

OBJECTIVE

Imaging represents an important noninvasive means to assess cystic fibrosis (CF) lung disease, which remains the main cause of morbidity and mortality in CF patients. While the development of new imaging techniques has revolutionised clinical practice, advances have posed diagnostic and monitoring challenges. The authors aim to summarise these challenges and make evidence-based recommendations regarding imaging assessment for both clinicians and radiologists.

STUDY DESIGN

A committee of 21 experts in CF from the 10 largest specialist centres in Italy was convened, including a radiologist and a pulmonologist from each centre, with the overall aim of developing clear and actionable recommendations for lung imaging in CF. An a priori threshold of at least 80% of the votes was required for acceptance of each statement of recommendation.

RESULTS

After a systematic review of the relevant literature, the committee convened to evaluate 167 articles. Following five RAND conferences, consensus statements were developed by an executive subcommittee. The entire consensus committee voted and approved 28 main statements.

CONCLUSIONS

There is a need for international guidelines regarding the appropriate timing and selection of imaging modality for patients with CF lung disease; timing and selection depends upon the clinical scenario, the patient's age, lung function and type of treatment. Despite its ubiquity, the use of the chest radiograph remains controversial. Both computed tomography and magnetic resonance imaging should be routinely used to monitor CF lung disease. Future studies should focus on imaging protocol harmonisation both for computed tomography and for magnetic resonance imaging. The introduction of artificial intelligence imaging analysis may further revolutionise clinical practice by providing fast and reliable quantitative outcomes to assess disease status. To date, there is no evidence supporting the use of lung ultrasound to monitor CF lung disease.

Best Practices: Imaging Strategies for Reduced-Dose Chest CT in the Management of Cystic Fibrosis-Related Lung Disease

OBJECTIVE. Cystic fibrosis (CF) is a multisystemic life-limiting disorder. The leading cause of morbidity in CF is chronic pulmonary disease. Chest CT is the reference standard for detection of bronchiectasis. Cumulative ionizing radiation limits the use of CT, particularly as treatments improve and life expectancy increases. The purpose of this article is to summarize the evidence on low-dose chest CT and its effect on image quality to determine best practices for imaging in CF. CONCLUSION. Low-dose chest CT is technically feasible, reduces dose, and renders satisfactory image quality. There are few comparison studies of low-dose chest CT and standard chest CT in CF; however, evidence suggests equivalent diagnostic capability. Low-dose chest CT with iterative reconstructive algorithms appears superior to chest radiography and equivalent to standard CT and has potential for early detection of bronchiectasis and infective exacerbations, because clinically significant abnormalities can develop in patients who do not have symptoms. Infection and inflammation remain the primary causes of morbidity requiring early intervention. Research gaps include the benefits of replacing chest radiography with low-dose chest CT in terms of improved diagnostic yield, clinical decision making, and patient outcomes. Longitudinal clinical studies comparing CT with MRI for the monitoring of CF lung disease may better establish the complementary strengths of these imaging modalities.

Quantitative inspiratory-expiratory chest CT to evaluate pulmonary involvement in pediatric hematopoietic stem-cell transplantation patients

Pulmonary complications following allogeneic hematopoietic stem-cell transplantation (HSCT) are a significant source of morbidity and complications may arise from a myriad of infectious and noninfectious sources. These complications may occur soon or many months post-transplantation and can have a broad range of outcomes. Surveillance for pulmonary involvement in the pediatric HSCT population can be challenging due to poor compliance with clinical pulmonary function testing, primarily spirometry, and there may be a role for clinical imaging to provide an additional means of monitoring, particularly in the era of clinical low-dose computed tomography (CT) protocols. In this single-site, retrospective study, a review of our institution's radiological and HSCT databases was conducted to assess the utility of a quantitative CT algorithm to describe ventilation abnormalities on high-resolution chest CT scans of pediatric HSCT patients. Thirteen non-contrast enhanced chest CT examinations acquired both in inspiration and expiration, from 12 deceased HSCT patients (median age at HSCT 10.4 years, median days of CT 162) were selected for the analysis. Also, seven age-matched healthy controls (median age 15.5) with non-contrast-enhanced inspiration-expiration chest CT were selected for comparison. We report that, compared to healthy age-matched controls, HSCT patients had larger percentages of poorly ventilated (median, 13.5% vs. 2.3%, p < .001) and air trapped (median 12.3% vs. 0%, p < .001) regions of lung tissue, suggesting its utility as a potential screening tool. Furthermore, there was wide variation within individual HSCT patients, supporting the use of multivolume CT and quantitative analysis to describe and phenotype post-transplantation lung involvement.

Perfusion in hand arthritis on dynamic contrast-enhanced computed tomography: a randomized prospective study using MRI as a standard of reference

OBJECTIVE

To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference.

MATERIALS AND METHODS

In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II-V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen's d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test.

RESULTS

The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01-0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv.

CONCLUSION

DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis.

Ultra-low-dose chest CT in adult patients with cystic fibrosis using a third-generation dual-source CT scanner

INTRODUCTION

Chest computed tomography (CT) examinations are performed routinely in some cystic fibrosis (CF) centers in order to evaluate lung disease progression in CF patients. Continuous CT technological advancement in theory could allows a lower radiation exposure of CF patients during chest CT examinations without an image quality reduction, and this could become increasingly important over time in order to reduce the cumulative radiation dose effects given the continuous increase of CF patients predicted median survival.

OBJECTIVE

The aim of this study was to compare objective and subjective image quality and radiation dose between low-dose chest CT examinations performed in adult CF patients using a third-generation DSCT scanner and a 64-slices single-source CT (SSCT) scanner.

MATERIALS AND METHODS

Between January 2016 and August 2019, 81 CF patients underwent low-dose chest CT examinations using both a 64-slices SSCT scanner (2016-2017) and a third-generation DSCT scanner (2018-2019). Objective image noise standard deviation (INSD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall subjective image quality (OSIQ), subjective image noise (SIN), subjective evaluation of streaking artifacts (SA), movement artifacts (MA) and edge resolution (ER), dose-length product (DLP), volume computed tomography dose index (CTDIvol) and effective radiation dose (ERD) were compared between DSCT and SSCT examinations. DSCT examinations consisted in spiral inspiratory end expiratory acquisitions. SSCT examinations consisted in spiral inspiratory acquisitions and five axial expiratory ones.

RESULTS

DSCT protocol showed statistically significant lower spiral inspiratory phase mean DLP, CTDIvol and ERD than SSCT protocol, with a 25% DLP, CTDIvol and ERD reduction. DSCT protocol showed statistically significant higher overall (inspiratory and expiratory phases) mean DLP, CTDIvol and ERD than SSCT protocol, with a 40% DLP, CTDIvol and ERD increase. Objective image quality (INSD, SNR and CNR) and SIN differences were not statistically significant, but subjective evaluation of DSCT images showed statistically significant better OSIQ and ER, as well as statistically significant lower SA and MA with respect to SSCT images.

CONCLUSIONS

To our knowledge, this is the first study evaluating chest CT image quality and radiation dose in adult CF patients using a third-generation DSCT scanner, and it showed that technological advancements could be used in order to reduce radiation exposure of volumetric examinations. The spiral inspiratory dose reduction can be obtained with concomitant improvements in subjective image quality with comparable objective quality. This will probably allow a wider use of this imaging modality in order to assess bronchiectasis and will probably foster spiral expiratory acquisition for small airways disease evaluation.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

OBJECTIVE

Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).

MATERIALS AND METHODS

This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.

RESULTS

Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).

CONCLUSION

DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.