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Purpose: Full‐slice magnetic resonance spectroscopic imaging at ≥ 7 T is especially vulnerable to lipid contaminations arising from regions close to the skull. This contamination can be mitigated by improving the point spread function via higher spatial resolution sampling and k‐space filtering, but this prolongs scan times and reduces the signal‐to‐noise ratio (SNR) efficiency. Currently applied parallel imaging methods accelerate magnetic resonance spectroscopic imaging scans at 7T, but increase lipid artifacts and lower SNR‐efficiency further. In this study, we propose an SNR‐efficient spatial‐spectral sampling scheme using concentric circle echo planar trajectories (CONCEPT), which was adapted to intrinsically acquire a Hamming‐weighted k‐space, thus termed density‐weighted‐CONCEPT. This minimizes voxel bleeding, while preserving an optimal SNR. Theory and Methods: Trajectories were theoretically derived and verified in phantoms as well as in the human brain via measurements of five volunteers (single‐slice, field‐of‐view 220 × 220 mm2, matrix 64 × 64, scan time 6 min) with free induction decay magnetic resonance spectroscopic imaging. Density‐weighted‐CONCEPT was compared to (a) the originally proposed CONCEPT with equidistant circles (here termed e‐CONCEPT), (b) elliptical phase‐encoding, and (c) 5‐fold Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding. Results: By intrinsically sampling a Hamming‐weighted k‐space, density‐weighted‐CONCEPT removed Gibbs‐ringing artifacts and had in vivo +9.5%, +24.4%, and +39.7% higher SNR than e‐CONCEPT, elliptical phase‐encoding, and the Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding (all P < 0.05), respectively, which lead to improved metabolic maps. Conclusion: Density‐weighted‐CONCEPT provides clinically attractive full‐slice high‐resolution magnetic resonance spectroscopic imaging with optimal SNR at 7T. Magn Reson Med 79:2874–2885, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. [ABSTRACT FROM AUTHOR]

Purpose: Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data. Methods: The measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets. Results: The MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice. Conclusion: The proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. [ABSTRACT FROM AUTHOR]

Dear Editor, We are aware of the opinion expressed by Radner et al. regarding our publication "Comparing reading speeds for reading standardized single sentences and paragraphs in patients with maculopathy" [[1]]. Thus, if we had not considered the mistakes, we would have found the following results: The mean reading speeds remain not significantly different between the 3 Radner sentences (96.57 wpm instead of 92.6) and the 3 IReST paragraphs (94.96 wpm). [Extracted from the article]

In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation. [ABSTRACT FROM AUTHOR]

Objective: To explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer.Methods: Institutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis.Results: There was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p < 0.02) before the mid-therapy point. The best predictor was lesion diameter change observed before mid-therapy (AUC = 0.93). Segmented volume was not able to differentiate between pCR and non-pCR at any time-point. The ADC values from 3D-ROI were not significantly different from 2D data (p = 0.06). The best AUC (0.79) for pCR prediction using DWI was median ADC measured before mid-therapy of NACT.Conclusions: The results of this study should be considered in NACT monitoring planning, especially in MRI protocol designing and time point selection.Key Points: • Mid-therapy diameter changes are the best predictors of pCR in neoadjuvant chemotherapy. • Volumetric measures are not strictly superior in therapy monitoring to lesion diameter. • Size measures perform as a better predictor than ADC values. [ABSTRACT FROM AUTHOR]

Presently, three major MR vendors provide commercial 7-T units for clinical research under ethical permission, with the number of operating 7-T systems having increased to over 50. This rapid increase indicates the growing interest in ultrahigh-field MRI because of improved clinical results with regard to morphological as well as functional and metabolic capabilities. As the signal-to-noise ratio scales linearly with the field strength (B0) of the scanner, the most obvious application at 7 T is to obtain higher spatial resolution in the brain, musculoskeletal system and breast. Of specific clinical interest for neuro-applications is the cerebral cortex at 7 T, for the detection of changes in cortical structure as a sign of early dementia, as well as for the visualization of cortical microinfarcts and cortical plaques in multiple sclerosis. In the imaging of the hippocampus, even subfields of the internal hippocampal anatomy and pathology can be visualized with excellent resolution. The dynamic and static blood oxygenation level-dependent contrast increases linearly with the field strength, which significantly improves the pre-surgical evaluation of eloquent areas before tumor removal. Using susceptibility-weighted imaging, the plaque-vessel relationship and iron accumulation in multiple sclerosis can be visualized for the first time. Multi-nuclear clinical applications, such as sodium imaging for the evaluation of repair tissue quality after cartilage transplantation and 31P spectroscopy for the differentiation between non-alcoholic benign liver disease and potentially progressive steatohepatitis, are only possible at ultrahigh fields. Although neuro- and musculoskeletal imaging have already demonstrated the clinical superiority of ultrahigh fields, whole-body clinical applications at 7 T are still limited, mainly because of the lack of suitable coils. The purpose of this article was therefore to review the clinical studies that have been performed thus far at 7 T, compared with 3 T, as well as those studies performed at 7 T that cannot be routinely performed at 3 T. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]