Enhanced Image Computer Aided Detection of Intracranial Aneurysms Karl Diedrich, MS1; John Roberts, PhD1; Dennis Parker, PhD1

1University of Utah, Salt Lake City, UT

IntroductionTraditional aneurysm computer aided diagnosis (CAD) schemes call potential aneurysms by circling the area and the radiologists confirm or reject the aneurysm call. Our new artery centerline based method enhances the images by coloring the three dimensional anatomical shaded surface from cool blue to hot red by increasing aneurysm likelihood giving finer grained information than circling potential aneurysms. The system assists the viewer in diagnosing and visualizing aneurysms for treatment planning without forcing the viewer to call or reject an aneurysm.

Image acquisition, segmentation, and enhancements.

Materials and Methods•3-D images from Time of Flight MR Angiography highlight flowing arterial blood.

•Segment arteries (tools created in ImageJ)

– Maximum Intensity Projection Z-buffer algorithm identifies artery seed voxels.

– Seed intensity histogram method determines thresholds to grow 3-D segmented arteries from seeds.

•Centerlines are determined using distance from edge (DFE) scoring and Dijkstra's shortest paths algorithm finding the lowest cost line through the segmentation. Line length thresholding removes lines crossing the arteries leaving the centerlines.

•Voxels are assigned to centerline voxels by nearest distance from centerline.

•The change in the absolute difference between DFE of two centerline points eight voxels apart is assigned to all voxels belonging to the middle centerline point.

•The DFE change is thresholded by intensity and size of the signal spot reducing false positives.

•The images are colored cool blue to hot red by increasing change in DFE.

Results

Discussion The structural information in the three dimensional image may provide enough information for radiologists to reject false positives and make accurate calls. The anatomical information in the image is also useful for treatment planning.

ConclusionWe developed segmentation, centerline and aneurysm enhancement tools that signal potential intracranial aneurysms.

Acknowledgements•Department of Biomedical Informatics

• NLM Grant: T15LM007124

•Utah Center for Advanced Imaging Research

Contact InformationKarl.Diedrich@utah.edu

Image acquisition Artery segmentation Centerline determination

Projection of arteries segmented by finding smoothest Z-buffer seed and region growing.

Distance from edge (DFE) cross section. Higher hotter middle values become centerlines.

Centerlines are red in a distance from centerline (DFC) image.

Assignment banding

Cross section of centerline and distance from centerline. Lowest DFC assigns all artery voxels to centerline voxels.

This image shows the change in DFE (radius) of the arteries. Black is no change, dark blue is a slight change and light blue is more change.

Unsegmented projections of aneurysm subjects.

Thresholding: keep top 8% intensity, largest 20% size.

Artery voxels are assigned to the nearest centerline voxel and colored by DFE to match the centerline.

Segmented change in DFE colored surface shading.

Future work•Reducing extra centerlines in round aneurysms

•Threshold using high DFC over DFE ratio to remove false positives at artery tips.

•Implement second aneurysm prediction algorithm using intensity gradients that enhance round shaped aneurysms.

•Combine the two aneurysm prediction methods to reduce false positives.

•Observer performance study to test the effectiveness of computer enhanced diagnosis of intracranial aneurysms.

Maximum Intensity Projection showing Bright moving arterial blood in Time of Flight Magnetic Resonance Angiogram.

7 mm aneurysm

2 mm aneurysm

Subjects Aneurysms Detected aneurysms

Sensitivity False Positives per subject

8 9 9 1.0 3.875