Purpose: To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Materials and Methods:Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method.Results: The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. Conclusion:The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.

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High-resolution magic angle spinning (HR-MAS) one- and two-dimensional 1H and 13C nuclear magnetic resonance (NMR) spectroscopy has been used to study intact glioblastoma (GBM) brain tumour tissue. The results were compared with in vitro chemical extract and in vivo spectra. The resolution of 1H one-dimensional, 1H TOCSY and 13C HSQC HR-MAS spectra is comparable to that obtained on perchloric extracts. 13C HSQC HR-MAS spectra have been particularly useful for the identification of 37 different metabolites in intact biopsy tumours, excluding water and DSS components. To our knowledge, this is the most detailed assignment of biochemical compounds obtained in intact human tissue, in particular in brain tumour tissue. Tissue degradation during the recording of the NMR experiment was avoided by keeping the sample at a temperature of 4 degrees C. Detailed metabolical compositions of 10 GBM (six primary, two secondary and two unclassified) were obtained. A good correlation between ex vivo and in vivo MRS has been found.

Purpose To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV) and maximum relative cerebral blood flow (rCBF) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV or rCBF at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities. RSNA, 2018.

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