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 T2* 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. We estimate the joint T2*-ADC spectra using an inverse Laplace transform. Results: T2*-ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. Conclusions: Combined T2*-diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2*-ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs

Generalised super resolution for quantitative MRI using self-supervised mixture of experts

Multi-modal and multi-contrast imaging datasets have diverse voxel-wise intensities. For example, quantitative MRI acquisition protocols are designed specifically to yield multiple images with widely-varying contrast that inform models relating MR signals to tissue characteristics. The large variance across images in such data prevents the use of standard normalisation techniques, making super resolution highly challenging. We propose a novel self-supervised mixture-of-experts (SS-MoE) paradigm for deep neural networks, and hence present a method enabling improved super resolution of data where image intensities are diverse and have large variance. Unlike the conventional MoE that automatically aggregates expert results for each input, we explicitly assign an input to the corresponding expert based on the predictive pseudo error labels in a self-supervised fashion. A new gater module is trained to discriminate the error levels of inputs estimated by Multiscale Quantile Segmentation. We show that our new paradigm reduces the error and improves the robustness when super resolving combined diffusion-relaxometry MRI data from the Super MUDI dataset. Our approach is suitable for a wide range of quantitative MRI techniques, and multi-contrast or multi-modal imaging techniques in general. It could be applied to super resolve images with inadequate resolution, or reduce the scanning time needed to acquire images of the required resolution. The source code and the trained models are available at https://github.com/hongxiangharry/SS-MoE

Integrated and efficient diffusion-relaxometry using ZEBRA

The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provide powerful new ways to explore tissue microstructure with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique regarding its ability to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, called ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies and thus facilitates wider use of these techniques both for research-driven and clinical applications

A Click Chemistry-Based Artificial Metallo-Nuclease

Artificial metallo-nucleases (AMNs) are promising DNA damaging drug candidates. Here, we demonstrate how the 1,2,3-triazole linker produced by the Cu-catalysed azide-alkyne cycloaddition (CuAAC) reaction can be directed to build Cu-binding AMN scaffolds. We selected biologically inert reaction partners tris(azidomethyl)mesitylene and ethynyl-thiophene to develop TC-Thio, a bioactive C3-symmetric ligand in which three thiophene-triazole moieties are positioned around a central mesitylene core. The ligand was characterised by X-ray crystallography and forms multinuclear CuII and CuI complexes identified by mass spectrometry and rationalised by density functional theory (DFT). Upon Cu coordination, CuII-TC-Thio becomes a potent DNA binding and cleaving agent. Mechanistic studies reveal DNA recognition occurs exclusively at the minor groove with subsequent oxidative damage promoted through a superoxide- and peroxide-dependent pathway. Single molecule imaging of DNA isolated from peripheral blood mononuclear cells shows that the complex has comparable activity to the clinical drug temozolomide, causing DNA damage that is recognised by a combination of base excision repair (BER) enzymes

Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease

Congenital heart disease (CHD) is the most common congenital malformation and is associated with adverse neurodevelopmental outcomes. The placenta is crucial for healthy fetal development and placental development is altered in pregnancy when the fetus has CHD. This study utilized advanced combined diffusion-relaxation MRI and a data-driven analysis technique to test the hypothesis that placental microstructure and perfusion are altered in CHD-affected pregnancies. 48 participants (36 controls, 12 CHD) underwent 67 MRI scans (50 control, 17 CHD). Significant differences in the weighting of two independent placental and uterine-wall tissue components were identified between the CHD and control groups (both pFDR < 0.001), with changes most evident after 30 weeks gestation. A significant trend over gestation in weighting for a third independent tissue component was also observed in the CHD cohort (R = 0.50, pFDR = 0.04), but not in controls. These findings add to existing evidence that placental development is altered in CHD. The results may reflect alterations in placental perfusion or the changes in fetal-placental flow, villous structure and maturation that occur in CHD. Further research is needed to validate and better understand these findings and to understand the relationship between placental development, CHD, and its neurodevelopmental implications

Best practices for the diagnosis and evaluation of infants with robin sequence:a clinical consensus report

Importance: Robin sequence (RS) is a congenital condition characterized by micrognathia, glossoptosis, and upper airway obstruction. Currently, no consensus exists regarding the diagnosis and evaluation of children with RS. An international, multidisciplinary consensus group was formed to begin to overcome this limitation. Objective: To report a consensus-derived set of best practices for the diagnosis and evaluation of infants with RS as a starting point for defining standards and management. Evidence Review: Based on a literature review and expert opinion, a clinical consensus report was generated. Findings: Because RS can occur as an isolated condition or as part of a syndrome or multiple-anomaly disorder, the diagnostic process for each newborn may differ. Micrognathia is hypothesized as the initiating event, but the diagnosis of micrognathia is subjective. Glossoptosis and upper airway compromise complete the primary characteristics of RS. It can be difficult to judge the severity of tongue base airway obstruction, and the possibility of multilevel obstruction exists. The initial assessment of the clinical features and severity of respiratory distress is important and has practical implications. Signs of upper airway obstruction can be intermittent and are more likely to be present when the infant is asleep. Therefore, sleep studies are recommended. Feeding problems are common and may be exacerbated by the presence of a cleft palate. The clinical features and their severity can vary widely and ultimately dictate the required investigations and treatments. Conclusions and Relevance: Agreed-on recommendations for the initial evaluation of RS and clinical descriptors are provided in this consensus report. Researchers and clinicians will ideally use uniform definitions and comparable assessments. Prospective studies and the standard application of validated assessments are needed to build an evidence base guiding standards of care for infants and children with RS

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. T2-weighted images, diffusion weighted and T1/T2* relaxometry data was acquired. Placental T2*, T1 and apparent diffusion coefficient (ADC) maps were calculated. Results 129 women (43 with chronic hypertension and 86 uncomplicated pregnancies) were imaged at a median of 27.7 weeks’ gestation (interquartile range (IQR) 23.9–32.1) and 28.9 (IQR 26.1–32.9) respectively. Visual analysis of T2-weighted imaging demonstrated placentae to be either appropriate for gestation or to have advanced lobulation in women with chronic hypertension, resulting in a greater range of placental mean T2* values for a given gestation, compared to gestation-matched controls. Both skew and kurtosis (derived from histograms of T2* values across the whole placenta) increased with advancing gestational age at imaging in healthy pregnancies; women with chronic hypertension had values overlapping those in the control group range. Upon visual assessment, the mean ADC declined in the third trimester, with a corresponding decline in placental mean T2* values and showed an overlap of values between women with chronic hypertension and the control group. Discussion A combined placental MR examination including T2 weighted imaging, T2*, T1 mapping and diffusion imaging demonstrates varying placental phenotypes in a cohort of women with chronic hypertension, showing overlap with the control group

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Topical Indexing and Questions to Represent Text for Retrieval and Browsing

AI systems for text analysis and retrieval face a double-edged problem of knowledge representation and ergonomics. On one hand, the content of a text must be explicitly represented, and at a level of abstraction that is plausible and descriptively useful. On the other hand, the effort to encode the knowledge must be in line with the benefits that that provides. There are no ideal solutions, but a good scheme would be one where the content of texts were usefully represented, accurate retrievals were easily made, and the knowledge encoding was naturally done. This paper describes a methodology for representing texts in terms of the questions that are raised and answered by them: a natural and efficient way of abstracting and capturing knowledge. The questions arising from the texts are, in turn, classified according to a theory of topical indexing. This indexing scheme relates the questions to each other, and this enables the automatic generation of a prunable network of associated texts..