Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI.

Introduction: In-vivo measurements of placental structure and function have the potential to improve prediction, diagnosis, and treatment planning for a wide range of pregnancy complications, such as fetal growth restriction and pre-eclampsia, and hence inform clinical decision making, ultimately improving patient outcomes. MRI is emerging as a technique with increased sensitivity to placental structure and function compared to the current clinical standard, ultrasound.

Methods: We demonstrate and evaluate a combined diffusion-relaxation MRI acquisition and analysis pipeline on a sizable cohort of 78 normal pregnancies with gestational ages ranging from 15 + 5 to 38 + 4 weeks.

Altered amygdala volumes and microstructure in focal epilepsy patients with tonic-clonic seizures, ictal, and post-convulsive central apnea.

Objectives: Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined the volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by the presence or absence of FBTCS, ictal central apnea (ICA), and post-convulsive central apnea (PCCA).

Low-Field Combined Diffusion-Relaxation MRI for Mapping Placenta Structure and Function.

Purpose: Demonstrating quantitative multi-parametric mapping in the placenta with combined -diffusion MRI at low-field (0.55T).

Methods: We present 57 placental MRI scans performed on a commercially available 0.

Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology.

The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal.

Altered Amygdala Volumes and Microstructure in Focal Epilepsy Patients with Tonic-Clonic Seizures, Ictal and Post-Ictal Central Apnea.

Objectives: Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by presence or absence of FBTCS, ictal central apnea (ICA) and post-ictal central apnea (PICA).

Neurite Orientation Dispersion and Density Imaging in Psychiatric Disorders: A Systematic Literature Review and a Technical Note.

While major psychiatric disorders lack signature diagnostic neuropathologies akin to dementias, classic postmortem studies have established microstructural involvement, i.e., cellular changes in neurons and glia, as a key pathophysiological finding.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping.

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.

Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease.

Background: Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables.

Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.

Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates.

Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study.

This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites.

Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T.

Inflammation of brain tissue is a complex response of the immune system to the presence of toxic compounds or to cell injury, leading to a cascade of pathological processes that include glial cell activation. Noninvasive MRI markers of glial reactivity would be very useful for in vivo detection and monitoring of inflammation processes in the brain, as well as for evaluating the efficacy of personalized treatments. Due to their specific location in glial cells, myo-inositol (mIns) and choline compounds (tCho) seem to be the best candidates for probing glial-specific intra-cellular compartments.

Placental magnetic resonance imaging in chronic hypertension: A case-control study.

Introduction: We aimed to explore the use of magnetic resonance imaging (MRI) in vivo as a tool to elucidate the placental phenotype in women with chronic hypertension.

Methods: In case-control study, women with chronic hypertension and those with uncomplicated pregnancies were imaged using either a 3T Achieva or 1.5T Ingenia scanner.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.

Purpose: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning.

Methods: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images.

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources of uncertainty in such problems. Here we introduce methods to characterise different components of uncertainty, and demonstrate the ideas using diffusion MRI super-resolution.

Challenges for biophysical modeling of microstructure.

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible.

ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation.

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution.

In Utero Diffusion MRI: Challenges, Advances, and Applications.

In utero diffusion magnetic resonance imaging (MRI) provides unique opportunities to noninvasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental villous trees, or the fibers in the fetal heart remain to be studied and understood in detail. Advances in fetal interventions and surgery furthermore increase the need for ever more precise antenatal diagnosis from fetal MRI.

Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter.

Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient.

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

Purpose: A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time.

Methods: We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications.

Renormalization of radiobiological response functions by energy loss fluctuations and complexities in chromosome aberration induction: deactivation theory for proton therapy from cells to tumor control.

We employ a multi-scale mechanistic approach built upon our recent phenomenological/computational methodologies [R. Abolfath et al., Sci.

The use of video motion analysis to determine the impact of anatomic complexity on endovascular performance in carotid artery stenting.

Objective: Video motion analysis (VMA) uses fluoroscopic sequences to derive information on catheter and guidewire movement and is able to calculate two-dimensional catheter tip path length (PL) on the basis of frame-by-frame pixel coordinates. The objective of this study was to evaluate the effect of anatomic complexity on the efficiency of completion of defined stages of simulated carotid artery stenting as measured by VMA.

Methods: Twenty interventionists each performed a standardized easy, medium, and difficult carotid artery stenting case in random order on an ANGIO Mentor (Simbionix, Airport City, Israel) simulator.

Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches.

The Centre for Medical Image Computing (CMIC) at University College London (UCL) hosted a two-day workshop on placenta imaging on April 12th and 13th 2018. The workshop consisted of 10 invited talks, 3 contributed talks, a poster session, a public interaction session and a panel discussion about the future direction of placental imaging. With approximately 50 placental researchers in attendance, the workshop was a platform for engineers, clinicians and medical experts in the field to network and exchange ideas.