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Nasal septum defects can currently only be reconstructed using autologous cartilage grafts. In this study, we examine the reconstruction of septal cartilage defects in a rabbit model using porcine decellularized nasal septal cartilage (DNSC) functionalized with recombinant platelet-derived growth factor-BB (PDFG-BB). The supportive function of the transplanted DNSC was estimated by the degree of septum deviation and shrinkage using magnetic resonance imaging (MRI). The biocompatibility of the transplanted scaffolds was evaluated by histology according to international standards. A study group with an autologous septal transplant was used as a reference. In situ regeneration of cartilage defects was assessed by histological evaluation 4 and 16 weeks following DNSC transplantation. A study group with non-functionalized DNSC was introduced for estimation of the effects of PDFG-BB functionalization. DNSC scaffolds provided sufficient structural support to the nasal septum, with no significant shrinkage or septal deviations as evaluated by the MRI. Biocompatibility analysis after 4 weeks revealed an increased inflammatory reaction of the surrounding tissue in response to DNSC as compared to the autologous transplants. The inflammatory reaction was, however, significantly attenuated after 16 weeks in the PDGF-BB group whereas only a slight improvement of the biocompatibility score was observed in the untreated group. In situ regeneration of septal cartilage, as evidenced by the degradation of the DNSC matrix and production of neocartilage, was observed in both experimental groups after 16 weeks but was more pronounced in the PDFG-BB group. Overall, DNSC provided structural support to the nasal septum and stimulated in situ regeneration of the cartilage tissue. Furthermore, PDFG-BB augmented the regenerative potential of DNSC and enhanced the healing process, as demonstrated by reduced inflammation after 16 weeks. [ABSTRACT FROM AUTHOR]

Background Functional techniques like diffusion-weighted imaging (DWI) are gaining more and more importance in liver magnetic resonance imaging (MRI). Diffusion kurtosis imaging (DKI) is an advanced technique that might help to overcome current limitations of DWI. Purpose To evaluate DKI for the differentiation of hepatic lesions in comparison to conventional DWI at 3 Tesla. Material and Methods Fifty-six consecutive patients were examined using a routine abdominal MR protocol at 3 Tesla which included DWI with b-values of 50, 400, 800, and 1000 s/mm2. Apparent diffusion coefficient maps were calculated applying a standard mono-exponential fit, while a non-Gaussian kurtosis fit was used to obtain DKI maps. ADC as well as Kurtosis-corrected diffusion ( D) values were quantified by region of interest analysis and compared between lesions. Results Sixty-eight hepatic lesions (hepatocellular carcinoma [HCC] [n = 25]; hepatic adenoma [n = 4], cysts [n = 18]; hepatic hemangioma [HH] [n = 18]; and focal nodular hyperplasia [n = 3]) were identified. Differentiation of malignant and benign lesions was possible based on both DWI ADC as well as DKI D-values ( P values were in the range of 0.04 to < 0.0001). Conclusion In vivo abdominal DKI calculated using standard b-values is feasible and enables quantitative differentiation between malignant and benign liver lesions. Assessment of conventional ADC values leads to similar results when using b-values below 1000 s/mm2 for DKI calculation. [ABSTRACT FROM AUTHOR]

The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived from MR perfusion images and to assess the diagnostic accuracy of the model and the sensitivity to sample size. A total of 86 patients with histologically-confirmed gliomas were imaged using dynamic susceptibility contrast (DSC) MRI at 1.5T. Histogram signatures from 53 of the 86 patients were analyzed independently by four neuroradiologists and used as a basis for the predictive SVM model. The resulting SVM model was tested on the remaining 33 patients and analyzed by a fifth neuroradiologist. At optimal SVM parameters, the true positive rate (TPR) and true negative rate (TNR) of the SVM model on the 33 patients was 0.76 and 0.82, respectively. The interobserver agreement and the TPR increased significantly when the SVM model was based on an increasing sample size ( P < 0.001). This result suggests that a predictive SVM model can aid in the diagnosis of glioma grade from MR perfusion images and that the model improves with increasing sample size. Magn Reson Med 60:945-952, 2008. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]