Microstructural imaging of the human spinal cord with advanced diffusion MRI

The aim of this PhD thesis is to advance the state-of-the-art of spinal cord magnetic resonance imaging (MRI) in multiple sclerosis (MS), a demyelinating, inflammatory and neurodegenerative disease of the central nervous system. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion-weighted (DW) MRI technique that provides indices of density and orientation dispersion of neuronal processes. These could be new useful biomarkers for the spinal cord, since they could better characterise overall, widespread MS pathology than conventional metrics. In this thesis, we test innovative clinically feasible acquisitions as well as signal analysis methods to study the potential of NODDI for the spinal cord. We also design and run computer simulations that corroborate our in vivo findings. Furthermore, we compare NODDI metrics to quantitative histological features, with the aim of validating their specificity. The thesis is divided in two parts. In the first part, in vivo experiments are described. Specific objectives are: i) to demonstrate the feasibility of performing NODDI in the spinal cord and in clinical settings; ii) to study the possibility of extracting with new approaches such as NODDI more specific microstructural information from standard DW acquisitions; iii) to assess how features typical of spinal cord microstructure, such as presence of large axons, influence NODDI metrics. In the second part of the thesis, ex vivo experiments are discussed. Their objective is the validation of the specificity of NODDI metrics via comparison to quantitative histology in post mortem spinal cord tissue. The experiments required the implementation of high-field DW scans as well as histological procedures and complex analysis pipelines. The results of this thesis contribute to current scientific knowledge. They prove that NODDI offers new opportunities to study how neurodegenerative diseases such as MS alter neural tissue complexity. We showed for the first time that NODDI can be performed in the spinal cord in vivo and in clinical scans. We also demonstrated that NODDI analysis of standard DW data is challenging, and quantified how the presence of large axons in the spinal cord influences NODDI metrics. Lastly, our ex vivo data highlight that unlike routine DW MRI methods, NODDI can detect reliably pathological variations of neurite orientation dispersion. NODDI is also sensitive to the density of axons and dendrites, but can not fully resolve axonal loss and demyelination in MS. We believe that the technique is a key element of a more general multi-modal MRI approach, which is necessary to obtain a complete description of complex diseases such as MS

Perinatal mental health around the world: priorities for research and service development in Italy

In Italy, most studies on perinatal mental health and initiatives aimed at improving the early detection and management of perinatal mental disorders have been carried out at the local level. National population-based studies are lacking. A study of pregnant women, recruited and diagnosed by a university hospital, found a 12.4% prevalence of minor and major depression during pregnancy, and a prevalence of 9.6% in the postpartum period. In a population-based surveillance system, covering 77% of national births, suicide was identified to be one of the main causes of maternal death within the first year after birth, yet half of those who were known to have a high suicide risk during the postpartum period had not been referred to a mental health service. The value of recognising depressive or anxiety symptoms early, during pregnancy, has been emphasised by recent research and should be linked to multi-professional psychosocial interventions. Since 2017, the Italian public primary care services that are dedicated to pregnancy assistance (Family Care Centres) have been tasked to provide free psychological assessment to pregnant and postpartum women. Action is now needed in order to improve access to Italian Family Care Centres for pregnant women and to develop an integrated care model involving obstetric and mental health services

Valoración y mejora de la extracción del genoma del VIH usando nanopartículas magnéticas

El proceso de extracción de los ácidos nucleicos es un paso preliminar en todo el proceso diagnóstico molecular. En el trabajo clínico con el VIH, dicho proceso condiciona el flujo de trabajo y la respuesta de los distintos sistemas y plataformas que se usan con el virus. En nuestro trabajo hemos establecido los parámetros de funcionamiento de los sistemas automáticos de extracción más frecuentemente utilizados. Para ello hemos utilizado un estándar, constituido por varias construcciones alternativas, generadas por mutaciones dirigidas sobre el plásmido pNL4.3, que tienen como base la estructura genómica del VIH-1. De esta forma hemos podido estudiar el proceso de extracción, y valorar las concentraciones, la pureza y la integridad de los ácidos nucleicos extraídos con los distintos sistemas y químicas empleados. La mayor parte de los sistemas automáticos en uso utilizan nanopartículas magnéticas y absorción inespecífica. Hemos desarrollado nanopartículas alternativas y estudiado las propiedades de magnetismo de las mismas. Con la finalidad de poder mejorar los rendimientos y mantener la integridad de las hebras obtenidas hemos recubierto estas nanopartículas tanto con materiales inorgánicos como orgánicos; también las hemos funcionalizado para mejorar sus condiciones de estabilidad, absorción y protección frente a las nucleasas. Pudimos así estudiar y comparar varios tipos de nanopartículas magnéticas de características y tamaños diferentes. De esta forma hemos conseguido lotes de partículas estándar, alternativas, que pueden evitar variabilidad en nuestro trabajo con el VIH

Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo

Here we present the application of neurite orientation dispersion and density imaging (NODDI) to the healthy spinal cord in vivo. NODDI provides maps such as the intra-neurite tissue volume fraction (vin), the orientation dispersion index (ODI) and the isotropic volume fraction (viso), and here we investigate their potential for spinal cord imaging. We scanned five healthy volunteers, four of whom twice, on a 3 T MRI system with a ZOOM-EPI sequence. In accordance to the published NODDI protocol, multiple b-shells were acquired at cervical level and both NODDI and diffusion tensor imaging (DTI) metrics were obtained and analysed to: i) characterise differences in grey and white matter (GM/WM); ii) assess the scan–rescan reproducibility of NODDI; iii) investigate the relationship between NODDI and DTI; and iv) compare the quality of fit of NODDI and DTI. Our results demonstrated that: i) anatomical features can be identified in NODDI maps, such as clear contrast between GM and WM in ODI; ii) the variabilities of vin and ODI are comparable to that of DTI and are driven by biological differences between subjects for ODI, have similar contribution from measurement errors and biological variation for vin, whereas viso shows higher variability, driven by measurement errors; iii) NODDI identifies potential sources contributing to DTI indices, as in the brain; and iv) NODDI outperforms DTI in terms of quality of fit. In conclusion, this work shows that NODDI is a useful model for in vivo diffusion MRI of the spinal cord, providing metrics closely related to tissue microstructure, in line with findings in the brain

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

MRI; Machine learning; Multiple sclerosisRessonància magnètica; Aprenentatge automàtic; Esclerosi múltipleResonancia magnética; Aprendizaje automático; Esclerosis múltipleIntroduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.This project has received funding under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 634541. FG received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/PR22/11920010”. FG has also received support from the Beatriu de Pinós (2020 BP 00117) programme, funded by the Secretary of Universities and Research (Government of Catalonia). BK, FP, and OC are supported by the National Institute of Health Research Biomedical Research Centre at UCL and UCLH. EPSRC grants EP/M020533/1 and EP/J020990/01, MRC MR/T046422/1 and MR/T046473/1, Wellcome Trust award 221915/Z/20/Z, and the NIHR UCLH BRC support DCA's work in this area. CGWK also receives funding from Horizon 2020 [Research and Innovation Action Grants Human Brain Project 945539 (SGA3)], BRC (#BRC704/CAP/CGW), MRC (#MR/S026088/1), and Ataxia UK

Bayesian image quality transfer

Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However,the original framework gives no indication of confidence in its output,which is a significant barrier to adoption in clinical practice and downstream processing. In this article,we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty,providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition,the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary. PROSUB uses the paradigm of recursive feature elimination (RFE) and progressively subsamples measurements during deep learning training, improving optimization stability. PROSUB also integrates a neural architecture search (NAS) paradigm, allowing the network architecture hyperparameters to respond to the subsampling process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge, producing large improvements >18% MSE on the MUDI challenge sub-tasks and qualitative improvements on downstream processes useful for clinical applications. We also show the benefits of incorporating NAS and analyze the effect of PROSUB's components. As our method generalizes to other problems beyond MRI measurement selection-reconstruction, our code is https://github.com/sbb-gh/PROSU

Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging

Brain; Protocol design; Quantitative MRI (qMRI)Cerebro; Diseño de protocolo; Resonancia magnética cuantitativa (qMRI)Cervell; Disseny del protocol; Ressonància magnètica quantitativa (qMRI)Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI). Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly. Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons. Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.This project was funded by the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1, G007748, I027084, N018702). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541 and 666992, and from: Rosetrees Trust (United Kingdom, funding FG); Prostate Cancer United Kingdom Targeted Call 2014 (Translational Research St.2, project reference PG14-018-TR2); Cancer Research United Kingdom grant ref. A21099; Spinal Research (United Kingdom), Wings for Life (Austria), Craig H. Neilsen Foundation (United States) for jointly funding the INSPIRED study; Wings for Life (#169111); United Kingdom Multiple Sclerosis Society (grants 892/08 and 77/2017); the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centres and UCLH NIHR Biomedical Research Centre; Champalimaud Centre for the Unknown, Lisbon (Portugal); European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101003390. FG is currently supported by the investigator-initiated PREdICT study at the Vall d’Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation

Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer

Magnetic resonance imaging; Pathology; Prostate diseaseImatges per ressonància magnètica; Patologia; Malaltia de la pròstataImágenes por resonancia magnética; Patología; Enfermedad de la próstataIntroduction Multiparametric MRI (mpMRI) is now widely used to risk stratify men with a suspicion of prostate cancer and identify suspicious regions for biopsy. However, the technique has modest specificity and a high false-positive rate, especially in men with mpMRI scored as indeterminate (3/5) or likely (4/5) to have clinically significant cancer (csPCa) (Gleason ≥3+4). Advanced MRI techniques have emerged which seek to improve this characterisation and could predict biopsy results non-invasively. Before these techniques are translated clinically, robust histological and clinical validation is required. Methods and analysis This study aims to clinically validate two advanced MRI techniques in a prospectively recruited cohort of men suspected of prostate cancer. Histological analysis of men undergoing biopsy or prostatectomy will be used for biological validation of biomarkers derived from Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours and Luminal Water imaging. In particular, prostatectomy specimens will be processed using three-dimension printed patient-specific moulds to allow for accurate MRI and histology mapping. The index tests will be compared with the histological reference standard to derive false positive rate and true positive rate for men with mpMRI scores which are indeterminate (3/5) or likely (4/5) to have clinically significant prostate cancer (csPCa). Histopathological validation from both biopsy and prostatectomy samples will provide the best ground truth in validating promising MRI techniques which could predict biopsy results and help avoid unnecessary biopsies in men suspected of prostate cancer. Ethics and dissemination Ethical approval was granted by the London—Queen Square Research Ethics Committee (19/LO/1803) on 23 January 2020. Results from the study will be presented at conferences and submitted to peer-reviewed journals for publication. Results will also be available on ClinicalTrials.gov.This work is supported by Engineering and Physical Sciences Research Council (EPSRC), grant reference (EP/R006032/1) and EP/M020533/1