Functional magnetic resonance imaging (fMRI) is a popular method for in vivo neuroimaging. Modern fMRI sequences are often weighted towards the blood oxygen level dependent (BOLD) signal, which is closely linked to neuronal activity (Logothetis, 2002). This weighting is achieved by tuning several parameters to increase the BOLD-weighted signal contrast. One such parameter is "TE," or echo time. TE is the amount of time elapsed between when protons are excited (the MRI signal source) and measured. Although the total measured signal magnitude decays with echo time, BOLD sensitivity increases (Silvennoinen et al., 2003). The optimal TE maximizes the BOLD signal weighting based on a number of factors, including several MRI scanner parameters (e.g., field strength), imaged tissue composition (e.g., grey vs. white matter), and proximity to air-tissue boundaries.

Industrial discharge has tremendously increased inorganic pollutants in water bodies all over the world. Paper and pulp effluent is included in one of the most pollution generating discharges containing complex chemical compounds such as lignin. For clean and healthy water resources, the recovery of lignin from wastewater from the paper and pulp industry is of high importance. Available chemical and biological technologies for removal of lignin have certain drawbacks. Coagulation and flocculation is not only the economic but also the effective method for removal of lignin. The present review highlights available coagulants employed for removal of lignin from paper and pulp wastewater. Each coagulant is pH dependent and shows varied results with change in effluent characteristics. The hydrolysis products of aluminium-based coagulants, iron-based coagulants and copper sulphate have positive charges. These positive charges promote formation of flocs through charged neutralisation or sweep flocculation. In the case of titanium-based coagulants, hydrolysis product is negatively charged and mode is heterocoagulation. Ninety percent recovery of lignin is achieved by using a mixture of oxotitanium sulphate and aluminium sulphate and 80% with aluminium sulphate. Virtually complete recovery of lignin is observed with oxotitanium sulphate.

These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Background Most MRI radiomics studies to date, even multi-centre ones, have used “pure” datasets deliberately accrued from single-vendor, single-field-strength scanners. This does not reflect aspirations for the ultimate generalisability of AI models. We therefore investigated the development of a radiomics signature from heterogeneous data originating on six different imaging platforms, for a breast cancer exemplar, in order to provide input into future discussions of the viability of radiomics in “real-world” scenarios where image data are not controlled by specific trial protocols but reflective of routine clinical practice. Methods One hundred fifty-six patients with pathologically proven breast cancer underwent multi-contrast MRI prior to neoadjuvant chemotherapy and/or surgery. From these, 92 patients were identified for whom T2-weighted, diffusion-weighted and contrast-enhanced T1-weighted sequences were available, as well as key clinicopathological variables. Regions-of-interest were drawn on the above image types and, from these, semantic and calculated radiomics features were derived. Classification models using a variety of methods, both with and without recursive feature elimination, were developed to predict pathological nodal status. Separately, we applied the same methods to analyse the information carried by the radiomic features regarding the originating scanner type and field strength. Repeated, ten-fold cross-validation was employed to verify the results. In parallel work, survival modelling was performed using random survival forests. Results Prediction of nodal status yielded mean cross-validated AUC values of 0.735 ± 0.15 (SD) for clinical variables alone, 0.673 ± 0.16 (SD) for radiomic features only, and 0.764 ± 0.16 (SD) for radiomics and clinical features together. Prediction of scanner platform from the radiomics features yielded extremely high values of AUC between 0.91 and 1 for the different classes examined indicating the presence of confounding features for the nodal status classification task. Survival analysis, gave out-of-bag prediction errors of 19.3% (clinical features only), 36.9–51.8% (radiomic features from different combinations of image contrasts), and 26.7–35.6% (clinical plus radiomics features). Conclusions Radiomic classification models whose predictive ability was consistent with previous single-vendor, single-field strength studies have been obtained from multi-vendor, multi-field-strength data, despite clear confounding information being present. However, our sample size was too small to obtain useful survival modelling results.

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated K by over 10% at v = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible v where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

We report a new progress in the development of a portable ultrasonic transcranial imaging system, which is expected to significantly improve the clinical utility of transcranial diagnostic ultrasound. When conventional ultrasonic phased array and Doppler techniques are applied through thick skull bones, the ultrasound field is attenuated, deflected, and defocused, leading to image distortion. To address these deficiencies, the ultrasonic transcranial imaging system implements two alternative ultrasonic methods. The first method improves detection of small foreign objects, such as bone fragments, pieces of shrapnel, or bullets, lodged in the brain tissue. Using adaptive beamforming, the method compensates for phase aberration induced by the skull and refocuses the distorted ultrasonic field at the desired location. The second method visualizes the blood flow through intact human skull using ultrasonic speckle reflections from the blood cells, platelets, or contrast agents. By analyzing these random temporal changes, it is possible to obtain 2D or 3D blood flow images, despite the adverse influence of the skull. Both methods were implemented on an advanced open platform phased array controller driving linear and matrix array probes. They were tested on realistic skull bone and head phantoms with foreign inclusions and blood vessel models.

Background and purpose: In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk. Materials and methods: We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV 50Gy) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV 60Gy) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV 60Gy (GTV 65Gy). Results: The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D 95% to the PTV 50Gy , GTV 60Gy and GTV 65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively. Conclusions: Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established.

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