Biology subjects

Panagiotaki, E.

Publications and source records attributed to Panagiotaki, E..

3 recordsLinked to original sources

bioRxiv2021

Synthesizing VERDICT maps from standard diffusion mp-MRI data using GANs

VERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from standard DWI data from multi-parametric (mp)- MRI by employing conditional generative adversarial networks (GANs). We use data from 67 patients who underwent both standard DWI-MRI and VERDICT MRI and rely on correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps. Quantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps that could supplement mp-MRI assessment for better diagnosis.

bioengineering↗

bioRxiv2020

"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

PurposeWe introduce "Select and retrieve via direct upsampling" network (SARDU-Net), a data-driven framework for model-free quantitative MRI (qMRI) protocol design, and demonstrate it on in vivo brain and prostate diffusion-relaxation imaging (DRI). MethodsSARDU-Net selects subsets of informative measurements within lengthy pilot scans, without the requirement to identify tissue parameters for which to optimise for. The algorithm consists of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using deep neural networks, which are trained jointly end-to-end. We demonstrate the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on 3 healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing the reproducibility of the procedure and testing sub-protocols for their potential to inform multi-contrast analyses via T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) modelling. ResultsIn both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations. The sub-protocols enable multi-contrast modelling for which they were not optimised explicitly, providing robust T1-SMDT and HM-MRI maps and goodness-of-fit in the top 5% against extensive sub-protocol comparisons. ConclusionsSARDU-Net gives new opportunities to identify economical but informative qMRI protocols from a subset of the pilot scans that can be used for acquisition-time-sensitive applications. The simple architecture makes the algorithm easy to train when exhaustive searches are intractable, and applicable to a variety of anatomical contexts.

bioengineering↗

bioRxiv2017

Quantitation of brain tumour microstructure response to Temozolomide therapy using non-invasive VERDICT MRI

There has been slow progress in the development of new therapeutic strategies for treating brain tumours, partly because assessment of treatment response is difficult and largely reliant on simple bi-dimensional measurements of MRI contrast-enhancing regions. Hence, there is a clinical need to develop improved imaging techniques for monitoring treatment response. In this study, we evaluate VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) MRI in mouse glioblastomas for the quantification of tumour microstructure and assessment of response to Temozolomide (TMZ) chemotherapy, and, we investigate the feasibility of applying VERDICT MRI in a range of human gliomas. VERDICT MRI detected response to TMZ earlier than structural and apparent diffusion coefficient (ADC) measurements. A significant reduction in the cell radius parameter was detected three days earlier than ADC and six days earlier than structural MRI. Histological analysis showed the same trend as VERDICT of decreased intracellular volume fraction in the TMZ-treated mice. Vascular volume fraction was not altered by TMZ, which was consistent with optical projection tomography measurements. In patients, glioblastoma compartmental volume fractions showed good agreement with mouse glioblastoma parameters. The VERDICT parameters varied across the human gliomas, with raised intracellular volume fraction in the oligodendrogliomas and elevated cell radius in both low-grade tumours subtypes. In conclusion, our results suggest that VERDICT MRI is more sensitive at detecting TMZ response than structural or ADC measurements. In patients, VERDICT is feasible within clinical scan times, and performed best at characterising glioblastoma. Further optimisation should improve assessment of different glioma subtypes.

biophysics↗