Preliminary investigation of computer-aided detection of pulmonary embolism in three-dimensional computed tomography pulmonary angiography images.
OBJECTIVE
We sought to develop a computer-aided diagnosis (CAD) system for assisting radiologists in the detection of pulmonary embolism (PE) on computed tomography pulmonary angiographic (CTPA) images.


METHODS
An adaptive three-dimensional (3D) voxel clustering method was developed based on expectation-maximization (EM) analysis to extract vessels from their surrounding tissues. Using a connected component analysis, the vessel tree was reconstructed by tracking the vessel and its branches in 3D space. The tracked vessels were prescreened for suspicious PE areas using a second EM analysis. A rule-based false-positive (FP) reduction method was designed to detect true PE based on the features of PE and vessels. In this preliminary study, 14 patients with positive CTPA for PE were studied. CT scans were performed at 1.25-mm collimation using a GE LightSpeed CT scanner; eight of these patients also had extensive lung parenchymal or pleural disease. One hundred sixty-three emboli were identified by two experienced thoracic radiologists. The emboli identified by the radiologists were used as the "gold standard." For each embolus, the percent diameter occlusion (clot) and conspicuity of embolus (rating of 1 to 5, with 5 being the most conspicuous) were visually estimated. One hundred one emboli were identified in the six patients without lung diseases; 57 were proximal to the subsegmental and 44 were subsegmental. For the eight patients with lung diseases, 62 emboli were identified, of which 37 were proximal to the subsegmental and 25 were subsegmental. A computer-detected volume was counted as true-positive when it overlapped with an embolus volume identified by the radiologists.


RESULTS
In the cases without lung diseases, if the PE had a conspicuity of >2 and only partially (20%-80%) occluded the vessel, our method detected 92.0% of proximal emboli and 77.8% of subsegmental emboli, with an average of 18.3 FPs/case. In the cases containing extensive lung disease, 66.7% and 40.0% of the PEs were detected with an average of 11.4 FPs/case under the same conditions. For the 14 PE cases, 13 of them were diagnosed as positive PE cases (case sensitivity was 92.9%).


CONCLUSIONS
This preliminary study indicates that our automated method is a promising approach to CAD of PE on CTPA. Further study is under way to collect a larger data set and to improve the detection accuracy for PE, especially those with <20% or >80% occlusion, and for very subtle PE. A fully developed CAD system is expected to provide a useful aid for PE detection on CTPA.
Zhou
Chuan
C
Department of Radiology, University of Michigan, Ann Arbor, CGC B2103, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USA. chuan@umich.edu
Chan
Heang-Ping
HP
Patel
Smita
S
Cascade
Philip N
PN
Sahiner
Berkman
B
Hadjiiski
Lubomir M
LM
Kazerooni
Ella A
EA
D000792:Angiography; D006801:Humans; D021621:Imaging, Three-Dimensional; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*