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Biomedical subjects

Karl-Juergen Wolf

Publications and source records attributed to Karl-Juergen Wolf.

2 recordsLinked to original sources

MR image-guided endovascular procedures with the ultrasmall superparamagnetic iron oxide SH U 555 C as an intravascular contrast agent: study in pigs.

PURPOSE: To evaluate the feasibility of using the ultrasmall superparamagnetic iron oxide (USPIO) SH U 555 C as an intravascular contrast agent for magnetic resonance (MR) image-guided vascular procedures with an open MR imaging system. MATERIALS AND METHODS: All experiments were performed with MR imaging at 0.2 T. MR image-guided interventions were performed in USPIO-enhanced vessels in four pigs. With near real-time MR image guidance (acquisition time, 0.64 second per section), the splenic and renal arteries were consecutively catheterized by using a susceptibility artifact-based catheter-guide wire combination. Angioplasty and stent implantation were performed four times in the renal artery and twice in the iliac artery. Intraaortal signal intensity (SI) was measured during the interventions. RESULTS: After administration of SH U 555 C (40 micromol of iron per kilogram of body weight), a three-dimensional MR angiographic sequence was performed that allowed visualization of the abdominal and pelvic vessels that were as small as 2 mm in diameter. Catheterization, angioplasty, and stent implantation were successfully guided in the USPIO-enhanced vasculature. Sixty minutes after contrast agent injection, the mean aortic SI was 70% of the maximum measured enhancement levels. CONCLUSION: One intravenous injection of SH U 555 C enabled long, continuous intravascular SI enhancement at MR angiography, and, in combination with susceptibility artifact-based device tracking, the injection allowed the performance of MR imaging-guided intravascular interventions in an open MR imaging system.

Animals↗

Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.

Algorithms↗