Computer-aided diagnosis improves detection of small intracranial aneurysms on MRA in a clinical setting.

BACKGROUND AND PURPOSE: MRA is widely accepted as a noninvasive diagnostic tool for the detection of intracranial aneurysms, but detection is still a challenging task with rather low detection rates. Our aim was to examine the performance of a computer-aided diagnosis algorithm for detecting intracranial aneurysms on MRA in a clinical setting. MATERIALS AND METHODS: Aneurysm detectability was evaluated retrospectively in 48 subjects with and without computer-aided diagnosis by 6 readers using a clinical 3D viewing system. Aneurysms ranged from 1.1 to 6.0 mm (mean = 3.12 mm, median = 2.50 mm). We conducted a multireader, multicase, double-crossover design, free-response, observer-performance study on sets of images from different MRA scanners by using DSA as the reference standard. Jackknife alternative free-response operating characteristic curve analysis with the figure of merit was used. RESULTS: For all readers combined, the mean figure of merit improved from 0.655 to 0.759, indicating a change in the figure of merit attributable to computer-aided diagnosis of 0.10 (95% CI, 0.03-0.18), which was statistically significant (F(1,47) = 7.00, P = .011). Five of the 6 radiologists had improved performance with computer-aided diagnosis, primarily due to increased sensitivity. CONCLUSIONS: In conditions similar to clinical practice, using computer-aided diagnosis significantly improved radiologists' detection of intracranial DSA-confirmed aneurysms of ≤6 mm.

Haptic rendering of isosurfaces directly from medical images.

Virtual environments for surgical training, planning and rehearsal have the potential to significantly enhance patient treatment and diagnosis. Haptic feedback devices provide forces to the physician through a manipulator, simulating palpation, scalpel cuts, or retraction of tissue. While haptics have been studied in other fields, medical applications of haptics remain in their infancy. We propose a method of haptically rendering isosurfaces (representing hard structures) directly from anatomical datasets rather than through traditional intermediate graphical representations such as polygons. Our algorithm determines an implicit surface representation of a volumetric isosurface on the fly, and renders the structure using standard haptic algorithms to calculate the forces felt by the user. This approach has the advantage of providing easy access to the rich volume dataset containing the actual anatomy. By relating Hounsfield units to density, haptic rendering has the ability to provide different resistances based on the tissue type being rendered. We developed and tested our algorithm using a quadric implicit surface, a common primitive in computer graphics, and have applied it to a variety of anatomical image datasets. Our paper describes the algorithm in sufficient detail to facilitate reproduction by others.

Evaluating virtual endoscopy for clinical use.

Virtual endoscopy is a term used to describe computer simulated endoscopy procedures derived from high resolution images of patient anatomy. By simulating the endoscopic examination, the patient is spared the discomfort and possible complications of an actual examination. The physician also has more flexibility in a virtual endoscopic examination of 3D patient data in comparison to a real endoscopic examination. Virtual endoscopy removes the physical and physiologic constraints of real endoscopy and can create views that are not possible in an actual endoscopic examination. This may enhance the performance of actual endoscopic examinations. Virtual endoscopy may also be used to perform "numerical biopsies"; anatomic measurements such as size, distance, shape, and density. Virtual endoscopy allows the physician to comprehensively explore the patient anatomy using an intuitive and interactive interface. There are currently two technical approaches to performing virtual endoscopy: perspective volume rendering and surface rendering of polygonal models. Perspective volume rendering uses traditional volumetric rendering algorithms to create visualizations directly from the volumetric dataset. Polygonal models require a preprocessing step to convert the segmented volume information into a polygonal surface that may be displayed at real time frame rates. Both paradigms have inherent strengths and weaknesses. We illustrate and compare the methods on actual patient data, including simulated endoscopic examinations of the airways, colon and esophagus. Preliminary results in virtual endoscopy show promise and will continue to be an area of active research leading to useful clinical applications.