Biomedical subjects

Reinhard Männer

Publications and source records attributed to Reinhard Männer.

3 recordsLinked to original sources

PubMed2003

Computer-generated stratified diffractive optical elements.

We present what is to our knowledge a new type of diffractive optical element (DOE), the computer-generated stratified diffractive optical element (SDOE), a hybridization of thin computer-generated DOEs and volume holograms. A model and several algorithms for calculating computer-generated SDOEs are given. Simulations and experimental results are presented that exhibit the properties of computer-generated SDOEs: the strong angular and wavelength selectivity of SDOEs makes it possible to store multiple pages in a computer-generated SDOE, which can be read out separately (multiplexing). The reconstruction of an optimized SDOE has a higher quality than the reconstruction of optimized one-layer DOEs. SDOEs can be calculated to have only one diffraction order.

Journal Article↗

PubMed2002

A divide and conquer approach to fast loop modeling.

We describe a fast ab initio method for modeling local segments in protein structures. The algorithm is based on a divide and conquer approach and uses a database of precalculated look-up tables, which represent a large set of possible conformations for loop segments of variable length. The target loop is recursively decomposed until the resulting conformations are small enough to be compiled analytically. The algorithm, which is not restricted to any specific loop length, generates a ranked set of loop conformations in 20-180 s on a desktop PC. The prediction quality is evaluated in terms of global RMSD. Depending on loop length the top prediction varies between 1.06 A RMSD for three-residue loops and 3.72 A RMSD for eight-residue loops. Due to its speed the method may also be useful to generate alternative starting conformations for complex simulations.

Algorithms↗

PubMed2002

Ultrasound, a new tool for surface matching in computer-navigated surgery.

OBJECTIVE: The object of this study was to investigate the feasibility of generating a bone surface from data provided by an ultrasound examination and to match this surface with the previous computed tomography (CT) scan. METHODS: From a CT data set of a training model of the pelvis, a three-dimensional surface was extracted by global thresholding-based segmentation. The same model was placed in a water basin, and ultrasound images were taken with a guided ultrasound transducer. The three-dimensional surface was generated from the ultrasound data set, and the two surfaces were matched in a semiautomatic mode. RESULTS: With special segmentation methods, a surface could be extracted automatically from the CT and the ultrasound data set. From these segmented ultrasound slices, a volume data set of the model was generated. After approximate initial matching, the local matching process was completed automatically. CONCLUSION: One of the limitations in computer-assisted surgery is the complicated matching process. Using special algorithms, a surface was extracted from the data set of an ultrasound examination and matched in a semiautomatic mode with the surface of a CT data set, facilitating the matching process.

Algorithms↗