Molecular imaging of pulmonary diseases

Dimastromatteo, Julien; Charles, Eric J.; Laubach, Victor E.

doi:10.1186/s12931-018-0716-0

Cited by 18 publications

(13 citation statements)

References 78 publications

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“…Many tracers have been used and more are being identified. 149,150 In studying lung injury, studies focusing on ventilation use inhaled or injected […”

Section: Measurements and Usesmentioning

confidence: 99%

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Imaging the Injured Lung

et al. 2019

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Acute respiratory distress syndrome (ARDS) consists of acute hypoxemic respiratory failure characterized by massive and heterogeneously distributed loss of lung aeration caused by diffuse inflammation and edema present in interstitial and alveolar spaces. It is defined by consensus criteria, which include diffuse infiltrates on chest imaging—either plain radiography or computed tomography. This review will summarize how imaging sciences can inform modern respiratory management of ARDS and continue to increase the understanding of the acutely injured lung. This review also describes newer imaging methodologies that are likely to inform future clinical decision-making and potentially improve outcome. For each imaging modality, this review systematically describes the underlying principles, technology involved, measurements obtained, insights gained by the technique, emerging approaches, limitations, and future developments. Finally, integrated approaches are considered whereby multimodal imaging may impact management of ARDS.

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“…Many tracers have been used and more are being identified. 149,150 In studying lung injury, studies focusing on ventilation use inhaled or injected […”

Section: Measurements and Usesmentioning

confidence: 99%

“…Application in oncology has advanced the search for new tracers, and with this, the ability to image almost any biologic process seems likely in the near future. 149…”

Section: Future Developmentsmentioning

confidence: 99%

Imaging the Injured Lung

et al. 2019

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“…In this study, we demonstrated that efficient machine learning classifier can accurately distinguish COVID-19 CXR images from normal cases and also pneumonia caused by other viruses. Although different imaging modalities have been applied for lung screening [16][17][18], X-ray remains the fastest and widely used tool for population-based lung disease screening. However, a large number of suspicious lung lesions can result in misclassification of cases.…”

Section: Discussionmentioning

confidence: 99%

COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images

Khuzani

Heidari

Shariati

2020

Preprint

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Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

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“…Current clinical molecular imaging implements SPECT and PET. SPECT allows simultaneous imaging of multiple different molecular probes with different radiotracers; however, it has lower resolution compared with PET [ 11 ]. Newer PET techniques regarding simultaneous imaging of different radiopharmaceuticals are under investigation; however, further research is needed [ 12 ].…”

Section: Imaging Modalitiesmentioning

confidence: 99%

Current Applications for Nuclear Medicine Imaging in Pulmonary Disease

2020

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Purpose of Review The main goal of the article is to familiarize the reader with commonly and uncommonly used nuclear medicine procedures that can significantly contribute to improved patient care. The article presents examples of specific modality utilization in the chest including assessment of lung ventilation and perfusion, imaging options for broad range of infectious and inflammatory processes, and selected aspects of oncologic imaging. In addition, rapidly developing new techniques utilizing molecular imaging are discussed. Recent Findings The article describes nuclear medicine imaging modalities including gamma camera, SPECT, PET, and hybrid imaging (SPECT/CT, PET/CT, and PET/MR) in the context of established and emerging clinical applications. Areas of potential future development in nuclear medicine are discussed with emphasis on molecular imaging and implementation of new targeted tracers used in diagnostics and therapeutics (theranostics). Summary Nuclear medicine and molecular imaging provide many unique and novel options for the diagnosis and treatment of pulmonary diseases. This article reviews current applications for nuclear medicine and molecular imaging and selected future applications for radiopharmaceuticals and targeted molecular imaging techniques.

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Molecular imaging of pulmonary diseases

Cited by 18 publications

References 78 publications

Imaging the Injured Lung

Imaging the Injured Lung

COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images

Current Applications for Nuclear Medicine Imaging in Pulmonary Disease

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