Cargando…

Deploying Clinical Process Improvement Strategies to Reduce Motion Artifacts and Expiratory Phase Scanning in Chest CT

We hypothesized that clinical process improvement strategies can reduce frequency of motion artifacts and expiratory phase scanning in chest CT. We reviewed 826 chest CT to establish the baseline frequency. Per clinical process improvement guidelines, we brainstormed corrective measures and priority...

Descripción completa

Detalles Bibliográficos
Autores principales:	Doda Khera, Ruhani, Singh, Ramandeep, Homayounieh, Fatemeh, Stone, Evan, Redel, Travis, Savage, Cristy A., Stockton, Katherine, Shepard, Jo-Anne O., Kalra, Mannudeep K., Digumarthy, Subba R.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2019
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694170/ https://www.ncbi.nlm.nih.gov/pubmed/31413297 http://dx.doi.org/10.1038/s41598-019-48423-7

Ejemplares similares

Use of radiomics to differentiate left atrial appendage thrombi and mixing artifacts on single-phase CT angiography
por: Ebrahimian, Shadi, et al.
Publicado: (2021)

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography
por: Chao, Hanqing, et al.
Publicado: (2021)

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
por: Homayounieh, Fatemeh, et al.
Publicado: (2020)

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?
por: Tabari, Azadeh, et al.
Publicado: (2019)

Deep learning in chest radiography: Detection of findings and presence of change
por: Singh, Ramandeep, et al.
Publicado: (2018)

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
por: Yoo, Hyunsuk, et al.
Publicado: (2020)

Association of AI quantified COVID-19 chest CT and patient outcome
por: Fang, Xi, et al.
Publicado: (2021)

Is Weight-Based Adjustment of Automatic Exposure Control Necessary for the Reduction of Chest CT Radiation Dose?
por: Prakash, Priyanka, et al.
Publicado: (2010)

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
por: Homayounieh, Fatemeh, et al.
Publicado: (2021)

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
por: Dasegowda, Giridhar, et al.
Publicado: (2023)

CT texture analysis of histologically proven benign and malignant lung lesions
por: Digumarthy, Subba R., et al.
Publicado: (2018)

Radiation dose reduction in chest dual-energy computed tomography: effect on image quality and diagnostic information
por: Canellas, Rodrigo, et al.
Publicado: (2018)

Chest CT practice and protocols for COVID-19 from radiation dose management perspective
por: Kalra, Mannudeep K., et al.
Publicado: (2020)

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia
por: Homayounieh, Fatemeh, et al.
Publicado: (2020)

Process improvement for reducing side discrepancies in radiology reports
por: Digumarthy, Subba Rao, et al.
Publicado: (2018)

Clinical and imaging features predict mortality in COVID-19 infection in Iran
por: Homayounieh, Fatemeh, et al.
Publicado: (2020)

Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer
por: Padole, Atul, et al.
Publicado: (2020)

Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
por: Kaviani, Parisa, et al.
Publicado: (2022)

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
por: Kaviani, Parisa, et al.
Publicado: (2022)

An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study
por: Homayounieh, Fatemeh, et al.
Publicado: (2021)

Multifactorial Analysis of Mortality in Screening Detected Lung Cancer
por: Digumarthy, Subba R., et al.
Publicado: (2018)

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?
por: Digumarthy, Subba R., et al.
Publicado: (2019)

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
por: Ebrahimian, Shadi, et al.
Publicado: (2021)

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?
por: Digumarthy, Subba R., et al.
Publicado: (2019)

Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
por: Kaviani, Parisa, et al.
Publicado: (2022)

Integrative analysis for COVID-19 patient outcome prediction
por: Chao, Hanqing, et al.
Publicado: (2021)

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
por: Hillis, James M., et al.
Publicado: (2022)

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
por: Ahn, Jong Seok, et al.
Publicado: (2022)

Semiquantitative Chest Computed Tomography Assessment Identifies Expiratory Central Airway Collapse in Symptomatic Military Personnel Deployed to Iraq and Afghanistan
por: Krefft, Silpa D., et al.
Publicado: (2022)

Validation pipeline for machine learning algorithm assessment for multiple vendors
por: Bizzo, Bernardo C., et al.
Publicado: (2022)

Radiation exposure from Chest CT: Issues and Strategies
por: Kalra, Mannudeep K., et al.
Publicado: (2004)

Effects of manual chest compression on expiratory flow bias during the positive end-expiratory pressure-zero end-expiratory pressure maneuver in patients on mechanical ventilation
por: Oliveira, Ana Carolina Otoni, et al.
Publicado: (2019)

Potential Pitfall in the Assessment of Lung Cancer with FDG-PET/CT: Talc Pleurodesis Causes Intrathoracic Nodal FDG Avidity
por: Wang, Yingbing, et al.
Publicado: (2013)

Pitfalls and artifacts in the interpretation of oncologic PET/CT of the chest
por: Meirelles, Gustavo de Souza Portes, et al.
Publicado: (2017)

In-Plane Shielding for CT: Effect of Off-Centering, Automatic Exposure Control and Shield-to-Surface Distance
por: Kalra, Mannudeep K., et al.
Publicado: (2009)

Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis
por: Konietzke, Philip, et al.
Publicado: (2018)

Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD
por: Hersh, Craig P, et al.
Publicado: (2013)

Time to Exhale: Additional Value of Expiratory Chest CT in Chronic Obstructive Pulmonary Disease
por: Gawlitza, Joshua, et al.
Publicado: (2018)

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans
por: , Nillmani, et al.
Publicado: (2022)

Effects of chest wall compression on expiratory flow rates in patients with chronic obstructive pulmonary disease
por: Nozoe, Masafumi, et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_c57pb3d352uj58c9nasqjdpsse