3 Tesla MRI in diagnosis and follow up of children with pneumonia

Yücel, Serap; Ayçiçek, Tuğba; Bilgici, Meltem Ceyhan; Dincer, Oguz Salih; Tomak, Leman

doi:10.1016/j.clinimag.2021.05.027

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Currently, chest X-ray (CXR) [ 6 ], computed tomography (CT) [ 7 ], and magnetic resonance imaging (MRI) [ 8 ] are commonly used in hospitals for the diagnosis of pneumonia. Doctors generally make a comprehensive diagnosis of COVID-19 by combining antigen testing, nucleic-acid testing, CXR, CT, and MRI.…”

Section: Introductionmentioning

confidence: 99%

Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

Li,

Xin,

et al. 2024

Vis. Comput. Ind. Biomed. Art

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Pneumonia is a serious disease that can be fatal, particularly among children and the elderly. The accuracy of pneumonia diagnosis can be improved by combining artificial-intelligence technology with X-ray imaging. This study proposes X-ODFCANet, which addresses the issues of low accuracy and excessive parameters in existing deep-learning-based pneumonia-classification methods. This network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution (ODConv) module, leveraging the residual module for feature extraction from X-ray images. The feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate feature information from different spatial directions. Additionally, the ODConv module extracts and fuses feature information in four dimensions: the spatial dimension of the convolution kernel, input and output channel quantities, and convolution kernel quantity. The experimental results demonstrate that the proposed method can effectively improve the accuracy of pneumonia classification, which is 3.77% higher than that of ResNet18. The model parameters are 4.45M, which was reduced by approximately 2.5 times. The code is available at https://github.com/limuni/X-ODFCANET.

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Section: Introductionmentioning

confidence: 99%

Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

Li,

Xin,

et al. 2024

Vis. Comput. Ind. Biomed. Art

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“…These abnormalities, caused by pneumonia, exhibit higher proton signals than adjacent natural lung parenchyma on MRI sequences. These sequences are characterized by high fluid/interstitium or cellular ratio [ 8 , 24 ]. The visualization quality of these lesions differed among the three sequences.…”

Section: Discussionmentioning

confidence: 99%

“…The manifestations of pediatric pneumonia are diverse and variable, and imaging assessments of the pulmonary structure play a crucial role in its diagnosis and treatment. Although chest radiography is commonly used as the initial radiologic investigation [ 6 , 7 ], it has substantial interobserver variability of interpretation [ 8 , 9 ] and limitations in depicting the extent of pneumonia and its complications [ 10 ]. As a result, CT has been considered the preferred imaging modality [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional ultrashort echo time magnetic resonance imaging in pediatric patients with pneumonia: a comparative study

Sun,

Chen,

et al. 2023

BMC Med Imaging

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Background UTE has been used to depict lung parenchyma. However, the insufficient discussion of its performance in pediatric pneumonia compared with conventional sequences is a gap in the existing literature. The objective of this study was to compare the diagnostic value of 3D-UTE with that of 3D T1-GRE and T2-FSE sequences in young children diagnosed with pneumonia. Methods Seventy-seven eligible pediatric patients diagnosed with pneumonia at our hospital, ranging in age from one day to thirty-five months, were enrolled in this study from March 2021 to August 2021. All patients underwent imaging using a 3 T pediatric MR scanner, which included three sequences: 3D-UTE, 3D-T1 GRE, and T2-FSE. Subjective analyses were performed by two experienced pediatric radiologists based on a 5-point scale according to six pathological findings (patchy shadows/ground-glass opacity (GGO), consolidation, nodule, bulla/cyst, linear opacity, and pleural effusion/thickening). Additionally, they assessed image quality, including the presence of artifacts, and evaluated the lung parenchyma. Interrater agreement was assessed using intraclass correlation coefficients (ICCs). Differences among the three sequences were evaluated using the Wilcoxon signed-rank test. Results The visualization of pathologies in most parameters (patchy shadows/GGO, consolidation, nodule, and bulla/cyst) was superior with UTE compared to T2-FSE and T1 GRE. The visualization scores for linear opacity were similar between UTE and T2-FSE, and both were better than T1-GRE. In the case of pleural effusion/thickening, T2-FSE outperformed the other sequences. However, statistically significant differences between UTE and other sequences were only observed for patchy shadows/GGO and consolidation. The overall image quality was superior or at least comparable with UTE compared to T2-FSE and T1-GRE. Interobserver agreements for all visual assessments were significant and rated “substantial” or “excellent.” Conclusions In conclusion, UTE MRI is a useful and promising method for evaluating pediatric pneumonia, as it provided better or similar visualization of most imaging findings compared with T2-FSE and T1-GRE. We suggest that the UTE MRI is well-suited for pediatric population, especially in younger children with pneumonia who require longitudinal and repeated imaging for clinical care or research and are susceptible to ionizing radiation.

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“…Denina et al [ 49 ] performed a follow-up on 25 paediatric patients 4 months after the acute infection and reported that LUS artifacts regressed over time. Recent studies used magnetic resonance imaging (MRI) to overcome the limitations of ionisation radiations [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. Heiss et al [ 40 ] investigated morphologic and functional changes in lung parenchyma by using low-field MRI in children with Long COVID compared with healthy controls.…”

Section: Long Covid Assessment In Childrenmentioning

confidence: 99%

Long COVID in Children: A Multidisciplinary Review

Sansone¹,

Pellegrino

Caronni

et al. 2023

Diagnostics

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Long COVID syndrome has emerged as a long-lasting consequence of acute SARS-CoV-2 infection in adults. In addition, children may be affected by Long COVID, with potential clinical issues in different fields, including problems in school performance and daily activities. Yet, the pathophysiologic bases of Long COVID in children are largely unknown, and it is difficult to predict who will develop the syndrome. In this multidisciplinary clinical review, we summarise the latest scientific data regarding Long COVID and its impact on children. Special attention is given to diagnostic tests, in order to help the physicians to find potential disease markers and quantify impairment. Specifically, we assess the respiratory, upper airways, cardiac, neurologic and motor and psychological aspects. Finally, we also propose a multidisciplinary clinical approach.

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3 Tesla MRI in diagnosis and follow up of children with pneumonia

Cited by 6 publications

References 25 publications

Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

Three-dimensional ultrashort echo time magnetic resonance imaging in pediatric patients with pneumonia: a comparative study

Long COVID in Children: A Multidisciplinary Review

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