Vasireddi Anil,  Bradley Helen,  Shah Amisha

Final Pr. ID: Paper #: 111

EOS^TM is a biplanar radiographic imaging system that can simultaneously acquire whole-body frontal and lateral standing radiographs with moving x-ray tubes and detectors. EOS has been shown to reduce radiation exposure and study time in comparison to standard digital radiography. While it has primarily found utility in evaluation of scoliosis, there has been limited study of EOS in performing pediatric skeletal surveys, which can be time-consuming and challenging given the age of the patient and the large number of images required. This study evaluates how implementing EOS in combination with digital radiography (DR) can reduce the number of images acquired and improve patient/family experience. Read More

Authors:  Vasireddi Anil , Bradley Helen , Shah Amisha

Keywords:  Skeletal Survey, EOS, Xray

Guillen Gutierrez Cinthia,  Rodriguez Garza Claudia,  Elizondo Riojas Guillermo,  Hernández Grimaldo Edgar,  Garza Acosta Andrea

Final Pr. ID: Poster #: EDU-020 (S)

Cystic fibrosis (CF) is caused by autosomal-recessive mutations in the CF transmembrane regulator (CFTR) gene. Results in production of abnormally viscous mucus and secretions in the lungs of patients
It is the most common genetic disorder leading to chronic pulmonary disease in children.

In the lung, the cystic fibrosis transmembrane regulator (CFTR) is a protein responsible for efflux of chloride and inhibition of the sodium channel's activity which controls the influx of sodium. Pulmonary manifestations of CF includes
Bronchiectasis
Pneumothorax
Recurrent bacterial infection
Pulmonary arterial hypertension

Chest XRAY: is inferior to CT for the assessment of patients with known bronchiectasis. Nevertheless, radiography remains a useful modality for assessing the pulmonary complications associated with bronchiectasis, because of its low cost, availability, low radiation dose, and speed of acquisition

Brasfield scoring system
The score is based on conventional chest radiographic findings and has been reported to have good correlation with pulmonary function.
There is a maximum score of 25 with points subtracted based on the score from each of the following categories:

Air trapping: generalized pulmonary overdistension (sternal bowing, depression of diaphragms, or thoracic kyphosis)
Linear markings Linear opacification due to prominence of bronchi; may be seen as parallel line densities, branching, or “end-on” circular densities (bronchial wall thickening)
Nodular cystic lesions: multiple discrete rounded densities ≥0.5 cm in diameter, with either radiopaque or radiolucent centers (bronchiectasis); does not refer to irregular linear markings; confluent nodules not classified as large lesion
Large lesions: segmental or lobar atelectasis or consolidation, including acute pneumonia.
General severity: impression of overall severity on chest x-ray

HRCT has become indispensable in the monitoring of CF patients and is used to guide therapy and assess response to treatment, as it not only correlates with lung function tests.
Scans are repeated every 6 to 18 months depending on the clinical course.

BHALLA SCORE SYSTEM
Bhalla system can assess the degree of lung involvement and the evolution of the damages caused by lung disease based on various radiological findings. It values
Bronchiectasias
Peribronchial thickening
Extent of bronchiectasias
Extent of mucous plugs
Abscesses or sacculations
Bronchial generations affected
Number of bullae
Extent of emphysema
Collapse or consolidation Read More

Authors:  Guillen Gutierrez Cinthia , Rodriguez Garza Claudia , Elizondo Riojas Guillermo , Hernández Grimaldo Edgar , Garza Acosta Andrea

Keywords:  Chest CT, Chest Xray, Education

Somasundaram Elanchezhian,  Brady Samuel,  Crotty Eric,  Trout Andrew,  Anton Christopher,  Towbin Alexander,  Coley Brian,  Dillman Jonathan

Final Pr. ID: Paper #: 032

At our institution, airway radiographs are routinely checked by the radiologist to ensure diagnostic image quality prior to the technologist completing the examination. These checks interrupt the workflow for both the technologist and radiologist. In this study, we develop and validate a deep learning algorithm to detect non-diagnostic lateral airway radiographs. Read More

Authors:  Somasundaram Elanchezhian , Brady Samuel , Crotty Eric , Trout Andrew , Anton Christopher , Towbin Alexander , Coley Brian , Dillman Jonathan

Keywords:  Deep learning, Airway, Xray