Kulturlandschaften in der Raumordnung: zur Entstehungsgeschichte eines Themas

Mit der Novellierung von 1998 wurde der Begriff "gewachsene Kulturlandschaften" erstmals im Raumordnungsgesetz verankert. Um dem gesetzlichen Auftrag zur Erhaltung von Kulturlandschaften Genüge tun zu können, gilt es, den Begriff und die aus dem Gesetz abzuleitenden Aufgaben zu konkretisieren und Arbeitshilfen für die raumplanerische Praxis zu entwickeln. Dabei ist zu klären, wie die zwischen verschiedenen Fachbereichen zum Teil stark differierenden Verständnisse und Herangehensweisen zu einzelnen Aspekten des Themenfeldes "Kulturlandschaft" zusammengeführt werden können. Insbesondere muss sich die Raumordnung auch damit auseinandersetzen, wie mit der Dynamik von Kulturlandschaften umzugehen ist. Bei all dem gilt, dass ganz unterschiedliche Landschaftstypen, z.B. auch Stadtlandschaften, als Kulturlandschaften betrachtet werden können. Der vorliegende Band, Ergebnis eines gemeinsamen Arbeitskreises der vier raumwissenschaftlichen Einrichtungen der Leibniz-Gemeinschaft, befasst sich zunächst mit Kulturlandschaften als Gegenstand aktueller Diskurse und behandelt im Anschluss raumordnungsrechtliche und fachdisziplinäre Zugänge zum Thema. Weiter werden Kulturlandschaften im Spannungsfeld zwischen Privateigentum und Gemeinschaftsgut diskutiert. Der umfangreichste Teil des Bandes widmet sich Methoden, Problemen und Perspektiven im Umgang mit Kulturlandschaften in der Planungspraxis und stellt deutsche und europäische Beispiele vor. Das Abschlusskapitel gibt Handlungsempfehlungen zur Rolle der Regionalplanung und stellt Herausforderungen im planerischen Umgang mit Kulturlandschaften sowie weiteren Forschungsbedarf dar

Combined Perfusion and Permeability Imaging Reveals Different Pathophysiologic Tissue Responses After Successful Thrombectomy.

Despite successful recanalization of large-vessel occlusions in acute ischemic stroke, individual patients profit to a varying degree. Dynamic susceptibility-weighted perfusion and dynamic T1-weighted contrast-enhanced blood-brain barrier permeability imaging may help to determine secondary stroke injury and predict clinical outcome. We prospectively performed perfusion and permeability imaging in 38 patients within 24 h after successful mechanical thrombectomy of an occlusion of the middle cerebral artery M1 segment. Perfusion alterations were evaluated on cerebral blood flow maps, blood-brain barrier disruption (BBBD) visually and quantitatively on ktrans maps and hemorrhagic transformation on susceptibility-weighted images. Visual BBBD within the DWI lesion corresponded to a median ktrans elevation (IQR) of 0.77 (0.41-1.4) min-1 and was found in all 7 cases of hypoperfusion (100%), in 10 of 16 cases of hyperperfusion (63%), and in only three of 13 cases with unaffected perfusion (23%). BBBD was significantly associated with hemorrhagic transformation (p < 0.001). While BBBD alone was not a predictor of clinical outcome at 3 months (positive predictive value (PPV) = 0.8 [0.56-0.94]), hypoperfusion occurred more often in patients with unfavorable clinical outcome (PPV = 0.43 [0.10-0.82]) compared to hyperperfusion (PPV = 0.93 [0.68-1.0]) or unaffected perfusion (PPV = 1.0 [0.75-1.0]). We show that combined perfusion and permeability imaging reveals distinct infarct signatures after recanalization, indicating the severity of prior ischemic damage. It assists in predicting clinical outcome and may identify patients at risk of stroke progression

Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke

Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making

Peripheral nerve involvement in multiple sclerosis: Demonstration by magnetic resonance neurography

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140006/1/ana25068_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140006/2/ana25068.pd

Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging.Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei.Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future