MRI evaluation of pulmonary lesions and lung tissue changes induced by tuberculosis

AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI's crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Thoracic MRI in pleural infection - a feasibility study from patients' and radiographers' perspectives

BACKGROUND

Pleural infections present significant clinical challenges, particularly in elderly or immunosuppressed patients, leading to prolonged hospital stay, high morbidity and high mortality. While CT and X-ray are standard imaging modalities, MRI's potential remain unexplored due to historical limitations in scan duration and patient discomfort. Advances in MRI technology, however, may enable its broader use in thoracic imaging. The study aimed to explore the feasibility of thoracic MRI from the radiographers' and patients' perspectives.

METHODS

A prospective feasibility study was conducted involving thirteen patients with pleural infections who underwent thoracic MRI as an add-on within 48 h of the conventional contrast-enhanced chest CT. Feasibility was assessed on technical success, and scan duration. Patients and radiographers experiences were evaluated through questionnaires and qualitative comments.

RESULTS

Technical success was high as all thirteen patients completed the scans. The mean in-room time was 30.7±5.5 minutes and the mean scan time was 23 ± 5.4 minutes. Radiographers reported the MRI scans as feasible with few patients requiring breaks or assistance. Most patients found the MRI experience manageable though two reported difficulties with breath-hold instructions. No patients were challenged by lying in supine position and no patients felt very anxious. No significant movement- or breathing artefacts were identified on MRI.

CONCLUSION

Thoracic MRI is feasible with high technical success, acceptable scan time, and good patient experience in patients with pleural infections offering potential as a radiation-free imaging modality. Furthermore, compared to CT, the use of MRI showed potential advantages in identifying pleural effusion septations.

Tuberculosis in pregnancy and assisted reproductive technology

Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis infection. In the world, tuberculosis is an important factor affecting women's reproductive health, which can cause reproductive tract anatomy abnormalities, embryo implantation obstacles, ovarian reserve and ovulation dysfunction, leading to female infertility. This group of women usually need to seek assisted reproductive technology to conceive. Latent tuberculosis infection during pregnancy has no clinical manifestation, but may develop into active tuberculosis, leading to adverse pregnancy outcomes. Most pregnant women do not need to be treated for latent tuberculosis infection, unless they are combined with high-risk factors for tuberculosis progress, but they need close follow-up. Early diagnosis and treatment of active tuberculosis in pregnancy can reduce the incidence rate and mortality of pregnant women and newborns, and treatment needs multidisciplinary cooperation.

Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images

Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist's time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.

Ultrasound and magnetic resonance imaging in thoracic tuberculosis in the pediatric population: moving beyond conventional radiology

Imaging is crucial in the diagnostic work-up and follow-up after treatment in children with thoracic tuberculosis (TB). Despite various technological advances in imaging modalities, chest radiography is the primary imaging modality for initial care and in emergency settings, especially in rural areas and where resources are limited. Ultrasonography (US) of the thorax in TB is one of the emerging applications of US as a radiation-free modality in children. Magnetic resonance imaging (MRI) is the ideal radiation-free, emerging imaging modality for thoracic TB in children. However, only limited published data is available regarding the utility of MRI in thoracic TB. In this pictorial review, we demonstrate the use of US and rapid lung MRI in evaluating children with thoracic TB, specifically for mediastinal lymphadenopathy and pulmonary complications of TB.

Deep learning-based lung image registration: A review

Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study

BACKGROUND

Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.

PURPOSE

Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center.

STUDY TYPE

Retrospective.

POPULATION

A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.

FIELD STRENGTH/SEQUENCE

1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI.

ASSESSMENT

2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.

STATISTICAL TESTS

Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant.

RESULTS

The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data.

DATA CONCLUSION

The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

Surveillance of Daughter Micronodule Formation Is a Key Factor for Vaccine Evaluation Using Experimental Infection Models of Tuberculosis in Macaques

Tuberculosis (TB) is still a major worldwide health problem and models using non-human primates (NHP) provide the most relevant approach for vaccine testing. In this study, we analysed CT images collected from cynomolgus and rhesus macaques following exposure to ultra-low dose Mycobacterium tuberculosis (Mtb) aerosols, and monitored them for 16 weeks to evaluate the impact of prior intradermal or inhaled BCG vaccination on the progression of lung disease. All lesions found (2553) were classified according to their size and we subclassified small micronodules (<4.4 mm) as 'isolated', or as 'daughter', when they were in contact with consolidation (described as lesions ≥ 4.5 mm). Our data link the higher capacity to contain Mtb infection in cynomolgus with the reduced incidence of daughter micronodules, thus avoiding the development of consolidated lesions and their consequent enlargement and evolution to cavitation. In the case of rhesus, intradermal vaccination has a higher capacity to reduce the formation of daughter micronodules. This study supports the 'Bubble Model' defined with the C3HBe/FeJ mice and proposes a new method to evaluate outcomes in experimental models of TB in NHP based on CT images, which would fit a future machine learning approach to evaluate new vaccines.

Free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence for distinguishing lung cancer from benign pulmonary nodules or masses: A pilot study

OBJECTIVE

Diffusion Weighted Imaging (DWI) can be used to differentiate benign and malignant pulmonary nodules or masses, while T2WI is also of great value in the differential diagnosis of them. For example, T2WI can be used to differentiate abscess from lung cancer. The study aims to quantitatively evaluate the efficacy of free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence (BLADE T2WI) for differentiating lung cancer (LC) and benign pulmonary nodule or mass (BPNM).

METHODS

A total of 291 patients with LC (197 males, 94 females; mean age 63.2 years) and 74 BPNM patients (53 males, 21 females; mean age 62.8 years) who underwent BLADE T2WI at 3-T MRI between November 2016 and May 2022were included in this retrospective study. Two radiologists independently blinded observed the MR images and measured the T2 contrast ratio (T2CR). Mann-Whitney U test was used to compare T2CR values between the two groups, ROC curves were used to evaluate the diagnostic efficacy of BLADE T2WI.

RESULTS

The two radiologists had good inter-observer consistency for T2CR (ICC = 0.958). The T2CR of BPNM was significantly higher than LC (all p < 0.001); the cut-off value of T2CR was 2.135, and the sensitivity, specificity, and accuracy of diagnosis were 75.6%, 63.5%, and 73.2%, respectively. Moreover, T2CR correctly diagnosed 220 LC cases (220/291 = 75.6%) and 47 BPNM cases (47/74 = 63.5%).

CONCLUSION

The T2CR value of MR non-enhanced BLADE T2WI can be easily obtained and can quantitatively distinguish BPNM from LC, thus avoiding misdiagnosis caused by lack of work experience.

Insight into diagnosis of pleural tuberculosis with special focus on nucleic acid amplification tests

INTRODUCTION

Pleural tuberculosis (TB) is the archetype of extrapulmonary TB (EPTB), which mainly affects the pleural space and leads to exudative pleural effusion. Diagnosis of pleural TB is a difficult task predominantly due to atypical clinical presentations and sparse bacillary load in clinical specimens.

AREA COVERED

We reviewed the current literature on the globally existing conventional/latest modalities for diagnosing pleural TB. Bacteriological examination (smear/culture), tuberculin skin testing/interferon-γ release assays, biochemical testing, imaging and histopathological/cytological examination are the main modalities. Moreover, nucleic acid amplification tests (NAATs), i.e. loop-mediated isothermal amplification, PCR/multiplex-PCR, nested-PCR, real-time PCR and GeneXpert® MTB/RIF are being utilized. Currently, GeneXpert Ultra, Truenat MTB^TM, detection of circulating Mycobacterium tuberculosis (Mtb) cell-free DNA by NAATs, aptamer-linked immobilized sorbent assay and immuno-PCR (I-PCR) have also been exploited.

EXPERT OPINION

Routine tests are not adequate for effective pleural TB diagnosis. The latest molecular/immunological tests as discussed above, and the other tools, i.e. real-time I-PCR/nanoparticle-based I-PCR and identification of Mtb biomarkers within urinary/serum extracellular vesicles being utilized for pulmonary TB and other EPTB types may also be exploited to diagnose pleural TB. Reliable diagnosis and early therapy would reduce the serious complications associated with pleural TB, i.e. TB empyema, pleural fibrosis, etc.

Diagnostic accuracy of imaging approaches for early tumor detection in children with Li-Fraumeni syndrome

BACKGROUND

The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide.

OBJECTIVE

To assess the diagnostic accuracy of the imaging used in this protocol.

MATERIALS AND METHODS

We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140).

RESULTS

Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI.

CONCLUSION

Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance.

Practical protocol for lung magnetic resonance imaging and common clinical indications

Imaging speed, spatial resolution and availability have made CT the favored cross-sectional imaging modality for evaluating various respiratory diseases of children - but only for the price of a radiation exposure. MRI is increasingly being appreciated as an alternative to CT, not only for offering three-dimensional (3-D) imaging without radiation exposure at only slightly inferior spatial resolution, but also for its superior soft-tissue contrast and exclusive morpho-functional imaging capacities beyond the scope of CT. Continuing technical improvements and experience with this so far under-utilized modality contribute to a growing acceptance of MRI for an increasing number of indications, in particular for pediatric patients. This review article provides the reader with practical easy-to-use protocols for common clinical indications in children. This is intended to encourage pediatric radiologists to appreciate the new horizons for applications of this rapidly evolving technique in the field of pediatric respiratory diseases.

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

T2-weighted Lung Imaging Using a 0.55-T MRI System

Purpose

To assess a 0.55-T MRI system for imaging lung disease and to compare image quality with clinical CT scans.

Materials and Methods

In this prospective study conducted between November 2018 and December 2019, respiratory-triggered T2-weighted turbo spin-echo MRI at 0.55 T was compared with clinical CT scans in 24 participants (mean age, 59 years ± 16 [standard deviation]; 18 women) with common lung abnormalities. MR images were reviewed and scored by experienced readers. Abnormal findings identified with MRI and CT were compared using the Cohen κ statistic.

Results

High-quality structural pulmonary MR images were attained with an average acquisition time of 11 minutes ± 3. MRI generated sufficient image quality to robustly detect bronchiectasis (κ = 0.61), consolidative opacities (κ = 1.00), cavitary lesions (κ = 1.00), effusion (κ = 0.64), mucus plug (κ = 0.68), and solid scattered nodularity (κ = 0.82). Diffuse disease, including ground-glass opacities (κ = 0.57) and tree-in-bud nodules (κ = 0.48), were the findings that were most difficult to discern using MRI, with false readings in four of 18 patients for each feature. Nodule size, which was measured independently at CT and MRI, was strongly correlated (R ² = 0.99) for nodules with a measurement of 10 mm ± 5 (range, 5-23 mm).

Conclusion

This initial study indicates that high-performance 0.55-T MRI holds promise in the evaluation of common lung disease.Clinical trials registration no. NCT03331380Supplemental material is available for this article. Keywords: MRI, Pulmonary, Technology Assessment© RSNA, 2021.

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

PURPOSE

To investigate the utilization of 3-Tesla (3 T) magnetic resonance imaging (MRI) in detection of pulmonary abnormalities in children with pneumonia.

MATERIALS AND METHODS

Forty-seven children with pneumonia prospectively underwent 3 T thoracic MRI and posteroanterior (PA) chest radiography (CR). Of these, 15 patients also underwent contrast-enhanced thorax computed tomography (CT) or high-resolution CT (HRCT). The MRI protocol included axial and coronal T2-weighted spectral presaturation with inversion recovery (SPIR) Multivane-XD and axial echo-planar diffusion-weighted imaging (EPI DWI) with respiratory gating. Kappa statistics, Cochran Q, and McNemar tests were used to investigate the results.

RESULTS

Agreement between CR and MRI was substantial in detecting consolidation/infiltration (k = 0.64), peribronchial thickening (k = 0.64), and bronchiectasis (k = 1); moderate in detecting cavity (k = 0.54) and pleural effusion (k = 0.44); and fair in detecting empyema (0.32) and bilateral involvement of lungs (k = 0.23). MRI was superior to CR in detecting bilateral involvement (p < 0.001), lymph node (p < 0.001), pleural effusion (p < 0.001), and empyema (p = 0.003). MRI detected all the consolidation/infiltration also detected on CT imaging. A kappa test showed moderate agreement between MRI and CT in detecting pleural effusion and ground-glass opacity (GGO), and substantial or almost perfect agreement for all other pathologies. No statistically significant difference was observed between MRI and CT for detecting pneumonia-associated pathologies by the McNemar test.

CONCLUSION

Thoracic 3 T MRI is an accurate and effective technique for evaluating children with pneumonia. MRI detected more pathologies than CR and had similar results to those of thorax CT.

Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble

Mycobacterium Tuberculosis (TB) is an infectious bacterial disease. In 2018, about 10 million people has been diagnosed with tuberculosis (TB) worldwide. Early diagnosis of TB is necessary for effective treatment, higher survival rate, and preventing its further transmission. The gold standard for tuberculosis diagnosis is sputum culture. Nevertheless, posterior-anterior chest radiographs (CXR) is an effective central method with low cost and a relatively low radiation dose for screening TB with immediate results. TB diagnosis from CXR is a challenging task requiring high level of expertise due to the diverse presentation of the disease. Significant intra-class variation and inter-class similarity in CXR images makes TB diagnosis from CXR a more challenging task. The main aim of this study is tuberculosis recognition from CXR images for reducing the disease burden. For this purpose, a novel multi-instance classification model is proposed in this study which is based on CNNs, complex networks and stacked ensemble (CCNSE). A main advantage of CCNSE is not requiring an accurate lung segmentation to localize the suspicious regions. Several overlapping patches are extracted from each CXR image. Features describing each patch are obtained by CNNs and then the feature vectors are clustered. Local complex networks (LCN) and global ones (GCN) of the cluster representatives are formed and feature engineering on LCN (GCN) generates other features at image-level (patch-level and image-level). Global clustering on these feature sets is performed for all patches. Each patch is assigned the purity score of its corresponding cluster. Patch-level features and purity scores are aggregated for each image. Finally, the images are classified with a proposed stacked ensemble classifier to normal and TB classes. Two datasets are used in this study including Montgomery County CXR set (MC) and Shenzhen dataset (SZ). MC/SZ includes 138/662 chest X-rays (CXR) from which 80 and 58/326 and 336 images belong to normal/TB classes, respectively. The experimental results show that the proposed method with AUC of 99.00 ± 0.28/98.00 ± 0.16 for MC/SZ and accuracy of 99.26 ± 0.40/99.22 ± 0.32 for MC/SZ with fivefold cross validation strategy is superior than the compared ones for diagnosis of TB from CXR images. The proposed method can be used as a computer-aided diagnosis system to reduce the manual time, effort and dependency to specialist's expertise level.

New Insight into Laboratory Tests and Imaging Modalities for Fast and Accurate Diagnosis of COVID-19: Alternative Suggestions for Routine RT-PCR and CT-A Literature Review

The globally inimitable and unremitting outbreak of COVID-19 infection confirmed the emergency need for critical detection of human coronavirus infections. Laboratory diagnostic tests and imaging modalities are two test groups used for the detection of COVID-19. Nowadays, real-time polymerase chain reaction (RT-PCR) and computed tomography (CT) have been frequently utilized in the clinic. Some limitations that confront with these tests are false-negative results, tests redone for follow-up procedure, high cost, and unable to do for all patients. To overcome these limitations, modified and alternative tests must be considered. Among these tests, RdRp/Hel RT-PCR assay had the lowest diagnostic limitation and highest sensitivity and specificity for the detection of SARS-CoV-2 RNA in both respiratory tract and nonrespiratory tract clinical specimens. On the other hand, lung ultrasound (LUS) and magnetic resonance imaging (MRI) are CT-alternative imaging modalities for the management, screening, and follow-up of COVID-19 patients.

Chest imaging in paediatric pulmonary TB

Tuberculosis (TB) remains a significant cause of death from an infectious disease worldwide. The diagnosis of pulmonary TB in children is often challenging as children present with non-specific clinical symptoms, have difficulties providing specimens and have a low bacillary load. Radiological imaging supports a clinical diagnosis of pulmonary TB in children, can assess response to treatment and evaluate complications of TB. However, radiological signs on plain radiographs are often non-specific and inter-observer variability in the interpretation contribute to the difficulties in radiological interpretation and diagnosis. The goal of this review is to discuss the advantages and features of cross-sectional imaging such as ultrasound, Computed tomography (CT) and Magnetic resonance imaging (MRI) in diagnosing pulmonary TB (PTB) and its complications in children.

The current status and further prospects for lung magnetic resonance imaging in pediatric radiology

Lung MRI makes it possible to replace up to 90% of CT examinations with radiation-free magnetic resonance diagnostics of the lungs without suffering any diagnostic loss. The individual radiation exposure can thus be relevantly reduced. This applies in particular to children who repeatedly require sectional imaging of the lung, e.g., in tumor surveillance or in chronic lung diseases such as cystic fibrosis. In this paper we discuss various factors that favor the establishment of lung MRI in the clinical setting. Among the many sequences proposed for lung imaging, respiration-triggered T2-W turbo spin-echo (TSE) sequences have been established as a good standard for children. Additional sequences are mostly dispensable. The most important pulmonary findings are demonstrated here in the form of a detailed pictorial essay. T1-weighted gradient echo sequences with ultrashort echo time are a new option. These sequences anticipate signal loss in the lung and deliver CT-like images with high spatial resolution. When using self-gated T1-W ultrashort echo time 3-D sequences that acquire iso-voxel geometry in the sub-millimeter range, secondary reconstructions are possible.

An Inhalable Theranostic System for Local Tuberculosis Treatment Containing an Isoniazid Loaded Metal Organic Framework Fe-MIL-101-NH2-From Raw MOF to Drug Delivery System

The theranostic approach to local tuberculosis treatment allows drug delivery and imaging of the lungs for a better control and personalization of antibiotic therapy. Metal-organic framework (MOF) Fe-MIL-101-NH2 nanoparticles were loaded with isoniazid. To optimize their functionality a 2³ factorial design of spray-drying with poly(lactide-co-glycolide) and leucine was employed. Powder aerodynamic properties were assessed using a twin stage impinger based on the dose emitted and the fine particle fraction. Magnetic resonance imaging (MRI) contrast capabilities were tested on porous lung tissue phantom and ex vivo rat lungs. Cell viability and uptake studies were conducted on murine macrophages RAW 246.9. The final product showed good aerodynamic properties, modified drug release, easier uptake by macrophages in relation to raw isoniazid-MOF, and MRI contrast capabilities. Starting from raw MOF, a fully functional inhalable theranostic system with a potential application in personalized tuberculosis pulmonary therapy was developed.