Development of novel methods for obtaining robust dynamic susceptibility contrast magnetic resonance imaging biomarkers from diseased brain in children

Dynamic susceptibility contrast (DSC-) MRI is an important imaging technique from which estimates of perfusion measures including cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) can be calculated. These perfusion measures can be used to indicate health in a range of diseases. However, acquisition protocol varies from centre-to-centre, which leads to variability in data quality between centres and limits the clinical applicability of DSC-MRI. Currently, the recommended process for assessing data quality is by eye, which is very time consuming and subjective between reviewers. In this work an automated processing pipeline for DSC-MRI was produced. Work to develop the pipeline demonstrated that data quality of DSC-MRI data can be assessed using machine learning classifiers, which were trained using metrics calculated from the data and the results of qualitative review. It also showed that it was possible to denoise the data using singular value decomposition (SVD) based methods, which were validated on a simulator and confirmed in patient data. The pipeline created was applied to a multicentre patient dataset where it demonstrated the importance of denoising DSC-MRI data in improving data quality and how data quality can vary with acquisition protocol. It was also applied to a single centre study of patients receiving differing treatments for brain tumours and suggested there are no significant changes in relative CBV (rCBV) in non-tumour brain between differing treatment groups. The pipeline developed during this work has wider applications in other imaging modalities and could be adapted to be applied to other perfusion imaging methods, such as dynamic contrast enhanced (DCE-) MRI, or any other imaging modality that involves analysis of a signal variation with time, such as computed tomography (CT) perfusion imaging or positron emission tomography (PET)

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)