Jeroen Jozef Sonnemans

Automatic Detection of Pulmonary Embolism in CTA Images

Pulmonary embolism (PE) is a common life-threatening disorder for which an early diagnosis is desirable. We propose a new system for the automatic detection of PE in contrast-enhanced CT images. The system consists of candidate detection, feature computation and classification. Candidate detection focusses on the inclusion of PE-even complete occlusions-and the exclusion of false detections, such as tissue and parenchymal diseases. Feature computation does not only focus on the intensity, shape and size of an embolus, but also on locations and the shape of the pulmonary vascular tree. Several classifiers have been tested and the results show that the performance is optimized by using a bagged tree classifier with two features based on the shape of a blood vessel and the distance to the vessel boundary. The system was trained on 38 CT data sets. Evaluation on 19 other data sets showed that the system generalizes well. The sensitivity of our system on the evaluation data is 63% at 4.9 false positives per data set, which allowed the radiologist to improve the number of detected PE by 22%.

Impact of Image Quality on the Performance of Computer-Aided Detection of Pulmonary Embolism

OBJECTIVE The purpose of this article is to assess the relationship between CT image quality and the number and type of false-positive (FP) findings found by a prototype computer-aided detection (CAD) algorithm for automatic detection of pulmonary embolism (PE). MATERIALS AND METHODS This retrospective study included 278 subjects (138 men and 140 women; mean age, 57 years; range, 18-88 years) who underwent consecutive CT pulmonary angiographies performed during off hours. Twenty-four percent (68/278) of studies were reported as positive for PE. CAD findings were classified as true-positive or FP by two independent readers and, in cases of discordance, by a third radiologist. Each FP result was classified according to underlying cause. The degree of vascular enhancement, image noise, motion artifacts, overall quality, and presence of underlying lung disease were rated on a 4- or 5-point scale. Chi-square tests and t tests were used to test significance of differences. RESULTS The mean number of FP CAD findings was 4.7 (median, 2) per examination. Most were caused by veins (30% [389/1,298]) or airspace consolidations (22% [286/1,298]). There was a significant positive association between the number of FP findings and image noise, motion artifacts, low vascular enhancement, low overall quality, and the extent of underlying disease. On a per-embolism basis, sensitivity decreased from 70.6% (214/303) for scans with zero to five FP findings, to 62.3% (33/53) for scans with six to 10 FP findings, to 60% (12/20) for scans with more than 10 FP findings. CONCLUSION There is a strong association between CT image quality and the number of FP findings indicated by a CAD algorithm for the detection of PE.

Fast and easy visualization of blood flow patterns in 4D Qflow MRI

To enable efficient fast and easy visualization of blood flow patterns in 4D Qflow MRI we have automated vessel segmentation and flow pattern visualization. The new methods enable flow pattern visualization within 10 seconds. As such, our method allows for routine clinical use for flow pattern visualization.

210 Validation of a semi-automatic coronary vessel tracker algorithm for magnetic resonance whole-heart angiography

Introduction Recently MR whole-heart imaging has emerged as a new technique for visualization of the coronary arteries without requiring radiation or contrast agent. Although detection of coronary disease on whole-heart MR data is still a subject of research [1], reconstruction of the coronary vascular tree for advanced 3D visualization can give valuable information in combination with 2D quantitative cardiac MR analysis.