Many of the unusual properties of liquid water are attributed to its unique structure, comprised of a random and fluctuating three-dimensional network of hydrogen bonds that link the highly polar water molecules. One of the most direct probes of the dynamics of this network is the infrared spectrum of the OH stretching vibration, which reflects the distribution of hydrogen-bonded structures and the intermolecular forces controlling the structural dynamics of the liquid. Indeed, water dynamics has been studied in detail, most recently using multi-dimensional nonlinear infrared spectroscopy for acquiring structural and dynamical information on femtosecond timescales. But owing to technical difficulties, only OH stretching vibrations in D2O or OD vibrations in H2O could be monitored. Here we show that using a specially designed, ultrathin sample cell allows us to observe OH stretching vibrations in H2O. Under these fully resonant conditions, we observe hydrogen bond network dynamics more than one order of magnitude faster than seen in earlier studies that include an extremely fast sweep in the OH frequencies on a 50-fs timescale and an equally fast disappearance of the initial inhomogeneous distribution of sites. Our results highlight the efficiency of energy redistribution within the hydrogen-bonded network, and that liquid water essentially loses the memory of persistent correlations in its structure within 50 fs.

Background: Magnetic Resonance linear accelerator (MR-linac) systems represent a new type of technology that allows for online MR-guidance for high precision radiotherapy (RT). Currently, the first MR-linac installations are being introduced clinically. Since the imaging performance of these integrated MR-linac systems is critical for their application, a thorough commissioning of the MRI performance is essential. However, guidelines on the commissioning of MR-guided RT systems are not yet defined and data on the performance of MR-linacs are not yet available. Materials & methods: Here we describe a comprehensive commissioning protocol, which contains standard MRI performance measurements as well as dedicated hybrid tests that specifically assess the interactions between the Linac and the MRI system. The commissioning results of four MR-linac systems are presented in a multi-center study. Results: Although the four systems showed similar performance in all the standard MRI performance tests, some differences were observed relating to the hybrid character of the systems. Field homogeneity measurements identified differences in the gantry shim configuration, which was later confirmed by the vendor. Conclusion:Our results highlight the importance of dedicated hybrid commissioning tests and the ability to compare the machines between institutes at this very early stage of clinical introduction. Until formal guidelines and tolerances are defined the tests described in this study may be used as a practical guideline. Moreover, the multi-center results provide initial bench mark data for future MR-linac installations.

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About 20–40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.