A system to detect cerebral aneurysms in multimodality angiographic data sets. Issue 9 (13th August 2014)

• Record Type: Journal Article
• Authors: Hentschke, Clemens M.; Beuing, Oliver; Paukisch, Harald; Scherlach, Cordula; Skalej, Martin; Tönnies, Klaus D.
• Is Part Of: Medical physics. Volume 41:Issue 9(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-08-13
• Subjects: Computed tomography -- Magnetic resonance imaging -- Probability theory; biomedical MRI -- computerised tomography -- filtering theory -- medical disorders -- probability; cerebral aneurysms -- computer‐aided diagnosis -- object detection -- multi‐modality -- angiography; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers; Data sets -- Databases -- Rutherford backscattering -- Computer aided diagnosis -- Angiography -- Vascular system -- Radiologists -- Three dimensional image processing -- Magnetic resonance; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4890775
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors' system is parametrizable for contrast‐enhanced magnetic resonance angiography (CE‐MRA), time‐of‐flight magnetic resonance angiography (TOF‐MRA), and computed tomography angiography (CTA). Methods: : Initial volumes of interest are found by applying a multiscale sphere‐enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized by domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: : The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE‐MRA data sets at an average false positive rate per data set (FPDS ) of 8.2. For TOF‐MRA, we achieve 95% sensitivity at 11.3 FPDS . For CTA, we reach a sensitivity of 95% at 22.8 FPDS . For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: : The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer‐aided detection tool to help radiologists find cerebral aneurysms.