Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this paper we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end-to-end trainable and self-supervised without any external annotations. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise a network to extract the shared latent features, which are then used for MRI synthesis. Since paired data is unavailable for our study (and rare in practice), pixel-level constraints are infeasible to apply. We instead propose to enforce the distributions to be statistically indistinguishable, by adversarial learning in both the image domain and feature space. To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint. A new cross-modal attention technique is proposed to utilise non-local spatial information, by encouraging multi-modal knowledge fusion and propagation. We extend the approach to consider the case where 3D auxiliary information (e.g., 3D neighbours and a 3D location index) from volumetric data is also available, and show that this improves image synthesis. The proposed approach is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.Comment: IEEE Transactions on Medical Imaging 202

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Magnetic Resonance Imaging to Enhance the Diagnosis of Fetal Brain Abnormalities in utero

Purpose This thesis aims to determine the diagnostic performance of in utero MR (iuMR) imaging to diagnose fetal brain abnormalities and describes the development, application and processing of a 3D volume MR acquisition. Methods A systematic review and meta-analysis of existing evidence was conducted. A prospective multicentre study of pregnant women, with a fetal brain abnormality on ultrasound (USS), was undertaken – The MERIDIAN study. In addition, an investigation of fetuses with no brain abnormality on USS was undertaken. Diagnostic accuracy and confidence, as well as positive and negative predictive values, were calculated. A 3D image acquisition technique was introduced, its ability to aid diagnosis measured and computational post-processing applied. Fetal brain volumes were extracted from the 3D data using image segmentation and these were assessed for reproducibility and validity. Resultant data allowed 3D models of fetal brains to be printed. Normally developing fetal brain volumes were plotted graphically thereby allowing comparison with abnormal fetuses. Results A total of 570 complete datasets were available from 903 eligible participants. Diagnostic accuracy was 68% for USS and 93% for iuMR. 95% of diagnoses made by iuMR were reported with high confidence compared to 82% on USS. Changes in pregnancy management occurred in 33% of cases. Positive and negative predictive values of iuMR were 93% and 99.5% respectively. 3D image quality was diagnostic in 89.6%, of which 91.4% gave an accurate diagnosis. Intra- and inter-observer agreement of brain volume measurements was high. Agreement between computer based and brain model volume measurements was also high. Conclusions iuMR imaging improves diagnostic accuracy and confidence for fetal brain abnormalities, influencing pregnancy management in a high proportion of cases. 3D imaging enables versatile visualisation of fetal brain anatomy and reliable extraction of volumes. This additional quantitative information could improve diagnosis in relevant cases

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Long-term effects of pre-and postnatal glucocorticoid treatment in congenital adrenal hyperplasia

Congenital adrenal hyperplasia (CAH) is an autosomal recessive disorder mostly caused by mutations in the CYP21A2 gene leading to impaired production of cortisol and aldosterone. Precursors in the steroidogenic pathway are shunted to pathways of androgen production and elevated levels of androgens may cause virilization of the external genitalia in females with CAH already in utero. Prenatal treatment with the synthetic glucocorticoid (GC) dexamethasone (DEX) can ameliorate virilization of the female fetus but because of the recessive mode of the inheritance of CAH and that treatment has to be initiated before the genotype of the fetus can be determined, the majority of the treated cases will be unnecessarily exposed to DEX during fetal life. Moreover, patients with CAH require GC replacement therapy after birth and during their life span there may be episodes of over- or under-treatment with a risk of developing adverse effects. Side effects of pre-and postnatal GC exposure may develop into chronic conditions with permanent effects on growth, metabolism, cognition, behavior and normal immune functioning. In this study, the effects of prenatal DEX treatment and postnatal GC treatment in the context of CAH were evaluated in a cohort of 265 individuals. The cohort comprised DEX-treated individuals with and without CAH, patients with CAH not prenatally treated with DEX and controls from the general population. The long-term impact on cognition, behavior, brain morphology, metabolism and DNA methylation was studied. Prenatal treatment with DEX was associated with cognitive impairments, particularly working memory. The effects seem to normalize by adult age in individuals without CAH who were treated with DEX during the first trimester of fetal life. In patients with CAH, prenatal DEX therapy was associated with reduced thickness and surface area bilaterally of a large area encompassing the parietal and superior occipital cortex. Moreover, the effects of DEX treatment on DNA methylation were associated with alterations in the DNA methylation profile, denoting an altered epigenetic programming of the immune system and, in particular, inflammation in individuals without CAH treated in the first trimester. This finding may confer altered risks for immune-related disorders later in life. When looking at the long-term outcome in patients with CAH, patients showed deficits in tests measuring executive functioning. Deficits in spatial working memory were associated with decreased white matter integrity that, in turn, was associated with lower dosages of GCs. Patients also showed structural alterations in the prefrontal regions involved in executive functioning and in areas of the parietal and superior occipital cortex involved in sensory integration. In addition, patients exhibited reduced cerebellar volume. In our analysis of DNA methylation in patients with CAH, we identified hypermethylation in two CpGs in two genes (FAIM2 and SFI1). Methylation was associated with the severity of CAH and brain structure, but we could not identify any association between methylation in these two genes and metabolic or cognitive outcome. In conclusion, this study extends our knowledge about the effects of pre-and postnatal GC treatment in CAH. The results have implications for the use of prenatal DEX treatment