Confocal laser scanning microscopy is used in many different fields of research nowadays. Therefore, high spatial resolution is required as well as high temporal resolution. Further, the quality of the resulting images has to be considered. Doing imaging with laser scanning microscopes, the balance between spatial resolution, speed and signal-to-noise ratio has to be defined for every specimen and experiment individually. Special adaptations to standard laser scanning microscopes improve the efficiency of the Leica TCS SP2 and Leica TCS SP2 RS for high-resolution low-noise imaging. Here, we want to report from the acousto-optical beam splitter, the resonant K-scanner and new objectives.
Proc. SPIE. 4323, Medical Imaging 2001: PACS and Integrated Medical Information Systems: Design and Evaluation
KEYWORDS: Databases, Magnetic resonance imaging, Image segmentation, Image processing, Diagnostics, Image analysis, Clinical research, 3D image processing, Functional magnetic resonance imaging, Picture Archiving and Communication System
In the last few years more and more University Hospitals as well as private hospitals changed to digital information systems for patient record, diagnostic files and digital images. Not only that patient management becomes easier, it is also very remarkable how clinical research can profit from Picture Archiving and Communication Systems (PACS) and diagnostic databases, especially from image databases. Since images are available on the finger tip, difficulties arise when image data needs to be processed, e.g. segmented, classified or co-registered, which usually demands a lot computational power. Today's clinical environment does support PACS very well, but real image processing is still under-developed. The purpose of this paper is to introduce a parallel cluster of standard distributed systems and its software components and how such a system can be integrated into a hospital environment. To demonstrate the cluster technique we present our clinical experience with the crucial but cost-intensive motion correction of clinical routine and research functional MRI (fMRI) data, as it is processed in our Lab on a daily basis.
The structure of an fMRI time series coregistration algorithm can be divided into modules (preprocessing, minimization procedure, interpolation method, cost function), for each of which there are many different approaches. In our study we implemented some of the most recent techniques and compared their combinations with regard to both registration accuracy and runtime performance. Bidirectional inconsistency and difference image analysis served as quality measures. The result shows that with an appropriate choice of methods realignment results can be improved by far compared with standard solutions. Finally, an automatic parameter adaptation method was incorporated. Additionally, the algorithm was implemented to run on a distributed 48 processor PC cluster, surpassing the performance of conventional applications running on high end workstations.