Superimposition of an average three-dimensional pattern of brain structures on CT scans

Supra- and infrasylvian conduction aphasia

Fifteen cases of conduction aphasia which were tested with the Aachen Aphasia Test (AAT), are presented. The CT lesion data were transformed to a standard 3D-reference brain referring to the ACPC line. According to the lesion profiles a group of 6 patients had pure suprasylvian lesions, a group of 4 patients had pure infrasylvian lesions, and a group of 5 patients had lesions in both supra- and infrasylvian regions. Suprasylvian conduction aphasics are superior to infrasylvian conduction aphasics in the token test and in repetition tasks. Infrasylvian conduction aphasics use more stereotypes in spontaneous speech than suprasylvian conduction aphasics. Conduction aphasics with both lesion sites perform less well in tests of naming, writing, and comprehension than the pure types. Thus conduction aphasia is a heterogeneous syndrome anatomically and linguistically.

Building 3D sulcal models using local geometry

This paper presents a series of 3D statistical models of the cortical sulci. They are built from points located automatically over the sulcal fissures, and corresponded automatically using variants on the iterative closest point algorithm. The models are progressively improved by adding in more and more structural and configural information, and the final results are consistent with findings from other anatomical studies. The models can be used to locate and label anatomical features automatically in 3D MR images of the head, for analysis, visualisation, classification, and normalisation.

An aphasia database on the internet: a model for computer-assisted analysis in aphasiology

A web-based software model was developed as an example for data mining in aphasiology. It is used for educating medical and engineering students. It is based upon a database of 254 aphasic patients which contains the diagnosis of the aphasia type, profiles of an aphasia test battery (Aachen Aphasia Test), and some further clinical information. In addition, the cerebral lesion profiles of 147 of these cases were standardized by transferring the coordinates of the lesions to a 3D reference brain based upon the ACPC coordinate system. Two artificial neural networks were used to perform a classification of the aphasia type. First, a coarse classification was achieved by using an assessment of spontaneous speech of the patient which produced correct results in 87% of the test cases. Data analysis tools were used to select four features of the 30 available test features to yield a more accurate diagnosis. This classifier produced correct results in 92% of the test cases. The neural network approach is similar to grouping performed in group studies, while the nearest-neighbor method shows a design more similar to case studies. It finds the neurolinguistic and the lesion data of patients whose AAT profiles are most similar to the user's input. This way lesion profiles can be compared to each other interindividually. The Aphasia Diagnoser is available on the Web address http://fuzzy.iau.dtu.dk/aphasia.nsf and thus should facilitate a discussion about the reliability and possibilities of data-mining techniques in aphasiology.

A new representation of knowledge concerning human anatomy and function

By integrating concepts of computer graphics and artificial intelligence, novel ways of representing medical knowledge become possible. They allow unprecedented possibilities ranging from three-dimensional interactive atlases to systems for surgery rehearsal.

Computer-aided interpretation of SPECT images of the brain using an MRI-derived 3D neuro-anatomical atlas

Nuclear medicine images have comparatively poor spatial resolution, making it difficult to relate the functional information which they contain to precise anatomical structures. A 3D neuro-anatomical atlas has been generated from the MRI data set of a normal, healthy volunteer to assist in the interpretation of nuclear medicine scans of the brain. Region growing and edge-detection techniques were used to semi-automatically segment the data set into the major tissue types within the brain. The atlas was then labelled interactively by marking points on each 2D slice. Anatomical structures useful in the interpretation of SPECT images were labelled. Additional, more detailed information corresponding to these structures is provided via an interactive index which allows access to images, diagrams and explanations. Registration of patient SPECT studies with the atlas is accomplished by using the position of the skull vertex and four external fiducial markers attached to the skin surface. The 3D coordinates determined from these points are used to calculate the transformation required to rotate, scale and translate the SPECT data, in 3D, to match the atlas. Corresponding 2D slices from the two 3D data sets are then displayed side-by-side on a computer screen. A cursor linking the two images allows the delineation of regions of interest (ROIs) in the SPECT scan based on anatomical structures identified from the atlas. Conversely regions of abnormal isotope distribution in the SPECT image can be localized by reference to corresponding structures in the atlas.