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Intracranial aneurysms represent a significant cause of morbidity and mortality. While the risk factors for aneurysm formation are known, the detection of aneurysms remains challenging. Magnetic resonance angiography (MRA) has recently emerged as a useful non-invasive method for aneurysm detection. However, even for experienced neuroradiologists, the sensitivity to small (<5 mm) aneurysms in MRA images is poor, on the order of 30~60% in recent, large series. We describe a fully automated computer-aided detection (CAD) scheme for detecting aneurysms on 3D time-of-flight (TOF) MRA images. The scheme locates points of interest (POIs) on individual MRA datasets by combining two complementary techniques. The first technique segments the intracranial arteries automatically and finds POIs from the segmented vessels. The second technique identifies POIs directly from the raw, unsegmented image dataset. This latter technique is useful in cases of incomplete segmentation. Following a series of feature calculations, a small fraction of POIs are retained as candidate aneurysms from the collected POIs according to predetermined rules. The CAD scheme was evaluated on 287 datasets containing 147 aneurysms that were verified with digital subtraction angiography, the accepted standard of reference for aneurysm detection. For two different operating points, the CAD scheme achieved a sensitivity of 80% (71% for aneurysms less than 5 mm) with three mean false positives per case, and 95% (91% for aneurysms less than 5 mm) with nine mean false positives per case. In conclusion, the CAD scheme showed good accuracy and may have application in improving the sensitivity of aneurysm detection on MR images.

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A number of side‐chain analogues of Δ8‐THC were tested in GTPγS binding assay in rat cerebellar membranes. O‐1125, a saturated side‐chain compound stimulated GTPγS binding with an Emax of 165.0%, and an EC50 of 17.4 nM. O‐1236, O‐1237 and O‐1238, three‐enyl derivatives containing a cis carbon‐carbon double bond in the side‐chain, stimulated GTPγS binding, acting as partial agonists with Emax values ranging from 51.3–87.5% and EC50 values between 4.4 and 29.7 nM. The stimulatory effects of O‐1125, O‐1236, O‐1237 and O‐1238 on GTPγS binding were antagonized by the CB1 receptor antagonist SR 141716A. The KB values obtained ranged from 0.11–0.21 mM, suggesting an action at CB1 receptors. Five‐ynyl derivatives (O‐584, O‐806, O‐823, O‐1176 and O‐1184), each containing a carbon‐carbon triple bond in the side‐chain, did not stimulate GTPγS binding and were tested as potential cannabinoid receptor antagonists. Each ‐ynyl compound antagonized the stimulatory effects of four cannabinoid receptor agonists on GTPγS binding. The KB values obtained, all found to be in the nanomolar range, did not differ between agonists or from cerebellar binding affinity. In conclusion, alterations of the side‐chain of the classical cannabinoid structure may exert a large influence on affinity and efficacy at the CB1 receptor. Furthermore, this study confirms the ability of the GTPγS binding assay to assess discrete differences in ligand efficacies which potentially may not be observed using alternative functional assays, thus providing a unique tool for the assessment of the molecular mechanisms underlying ligand efficacies. British Journal of Pharmacology (1999) 126, 1575–1584; doi: