Dual-energy Study Research Articles

Model-based approach for analysis of contrast media regions in contrast enhanced breast model images

Contrast agents in radiology improve visualisation and iodine-based ones are applied in differ- ent radiology techniques. Energies, used in these techniques, vary in a very wide range. The energies can be as low as 26 kVp in mammography and reach up to 140 - 150 kVp in computed tomography. Dual-energy studies can also be applied. X-ray images of resin phantoms with iodine-based contrast base are taken at different energies and the image pixel values of various regions of interest were measured. Based on the performed measurements, the visual contrast between the studied different areas is calculated and analysed. The obtained data are applied for the estimation of models for the expected pixel values depending on the energies and the contrast agent quantities. These models can be used to describe the real conditions, which differ from the applicable to theoretical calculations ideal conditions.

Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs.

DICOM header information is frequently used to classify medical image types; however, if a header is missing fields or contains incorrect data, the utility is limited. To expedite image classification, we trained convolutional neural networks (CNNs) in two classification tasks for thoracic radiographic views obtained from dual-energy studies: (a)distinguishing between frontal, lateral, soft tissue, and bone images and (b)distinguishing between posteroanterior (PA) or anteroposterior (AP) chest radiographs. CNNs with AlexNet architecture were trained from scratch. 1910 manually classified radiographs were used for training the network to accomplish task (a), then tested with an independent test set (3757 images). Frontal radiographs from the two datasets were combined to train a network to accomplish task (b); tested using an independent test set of 1000 radiographs. ROC analysis was performed for each trained CNN with area under the curve (AUC) as a performance metric. Classification between frontal images (AP/PA) and other image types yielded an AUC of 0.997 [95% confidence interval (CI): 0.996, 0.998]. Classification between PA and AP radiographs resulted in an AUC of 0.973 (95% CI: 0.961, 0.981). CNNs were able to rapidly classify thoracic radiographs with high accuracy, thus potentially contributing to effective and efficient workflow.