Adaptive Contrast-Based Computer Aided Detection for Pulmonary Embolism

This work involves the computer-aided diagnosis (CAD) of pulmonary embolism (PE) in contrast-enhanced computed tomography pulmonary angiography (CTPA). Contrast plays an important role in analyzing and identifying PE in CTPA. At times the contrast mixing in blood may be insufficient due to several factors such as scanning speed, body weight and injection duration. This results in a suboptimal study (mixing artifact) due to non-homogeneous enhancement of blood's opacity. Most current CAD systems are not optimized to detect PE in sub optimal studies. To this effect, we propose new techniques for CAD to work robustly in both optimal and suboptimal situations. First, the contrast level at the pulmonary trunk is automatically detected using a landmark detection tool. This information is then used to dynamically configure the candidate generation (CG) and classification stages of the algorithm. In CG, a fast method based on tobogganing is proposed which also detects wall-adhering emboli. In addition, our proposed method correctly encapsulates potential PE candidates that enable accurate feature calculation over the entire PE candidate. Finally a classifier gating scheme has been designed that automatically switches the appropriate classifier for suboptimal and optimal studies. The system performance has been validated on 86 real-world cases collected from different clinical sites. Results show around 5% improvement in the detection of segmental PE and 6% improvement in lobar and sub segmental PE with a 40% decrease in the average false positive rate when compared to a similar system without contrast detection.