Fast fetal head compounding from multi-view 3D ultrasound.

The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools. METHODS: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. RESULTS: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. CONCLUSION: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.

PRETUS: A plug-in based platform for real-time ultrasound imaging research.

We present PRETUS - a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brought in via independent plug-ins that can be arranged in sequence. The software allows to capture the real-time stream of ultrasound images from virtually any ultrasound machine, applies computational methods and visualizes the results on-the-fly. Plug-ins can run concurrently without blocking each other. They can be implemented in C++ and Python. A graphical user interface can be implemented for each plug-in, and presented to the user in a compact way. The software is free and open source, and allows for rapid prototyping and testing of real-time ultrasound imaging methods in a manufacturer-agnostic fashion. The software is provided with input, output and processing plug-ins, as well as with tutorials to illustrate how to develop new plug-ins for PRETUS.

A Virtual Reality System for Improved Image-Based Planning of Complex Cardiac Procedures.

The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper, we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multiplanar reformatting, flow visualisation and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation and multiuser interactions. The available features were evaluated by imaging and nonimaging clinicians, showing that the virtual reality system can help improve the understanding and communication of three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome.

Characterising phenotypes often requires quantification of anatomical shape. Quantitative shape comparison (morphometrics) traditionally uses manually located landmarks and is limited by landmark number and operator accuracy. Here, we apply a landmark-free method to characterise the craniofacial skeletal phenotype of the Dp1Tyb mouse model of Down syndrome and a population of the Diversity Outbred (DO) mouse model, comparing it with a landmark-based approach. We identified cranial dysmorphologies in Dp1Tyb mice, especially smaller size and brachycephaly (front-back shortening), homologous to the human phenotype. Shape variation in the DO mice was partly attributable to allometry (size-dependent shape variation) and sexual dimorphism. The landmark-free method performed as well as, or better than, the landmark-based method but was less labour-intensive, required less user training and, uniquely, enabled fine mapping of local differences as planar expansion or shrinkage. Its higher resolution pinpointed reductions in interior mid-snout structures and occipital bones in both the models that were not otherwise apparent. We propose that this landmark-free pipeline could make morphometrics widely accessible beyond its traditional niches in zoology and palaeontology, especially in characterising developmental mutant phenotypes.

Measuring cortical mean diffusivity to assess early microstructural cortical change in presymptomatic familial Alzheimer's disease.

BACKGROUND: There is increasing interest in improving understanding of the timing and nature of early neurodegeneration in Alzheimer's disease (AD) and developing methods to measure this in vivo. Autosomal dominant familial Alzheimer's disease (FAD) provides the opportunity for investigation of presymptomatic change. We assessed early microstructural breakdown of cortical grey matter in FAD with diffusion-weighted MRI. METHODS: Diffusion-weighted and T1-weighed MRI were acquired in 38 FAD mutation carriers (17 symptomatic, 21 presymptomatic) and 39 controls. Mean diffusivity (MD) was calculated for six cortical regions previously identified as being particularly vulnerable to FAD-related neurodegeneration. Linear regression compared MD between symptomatic and presymptomatic carriers and controls, adjusting for age and sex. Spearman coefficients assessed associations between cortical MD and cortical thickness. Spearman coefficients also assessed associations between cortical MD and estimated years to/from onset (EYO). Across mutation carriers, linear regression assessed associations between MD and EYO, adjusting for cortical thickness. RESULTS: Compared with controls, cortical MD was higher in symptomatic mutation carriers (mean ± SD CDR = 0.88 ± 0.39) for all six regions (p < 0.001). In late presymptomatic carriers (within 8.1 years of predicted symptom onset), MD was higher in the precuneus (p = 0.04) and inferior parietal cortex (p = 0.003) compared with controls. Across all presymptomatic carriers, MD in the precuneus correlated with EYO (p = 0.04). Across all mutation carriers, there was strong evidence (p < 0.001) of association between MD and cortical thickness in all regions except entorhinal cortex. After adjusting for cortical thickness, there remained an association (p < 0.05) in mutation carriers between MD and EYO in all regions except entorhinal cortex. CONCLUSIONS: Cortical MD measurement detects microstructural breakdown in presymptomatic FAD and correlates with proximity to symptom onset independently of cortical thickness. Cortical MD may thus be a feasible biomarker of early AD-related neurodegeneration, offering additional/complementary information to conventional MRI measures.

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper, we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. In addition, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation, and so on, to verify the effectiveness of our method. Our method is more consistent than human annotation and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. Furthermore, we demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion, and automated biometric measurements.

Mechanically Powered Motion Imaging Phantoms: Proof of Concept.

Motion imaging phantoms are expensive, bulky and difficult to transport and set-up. The purpose of this paper is to demonstrate a simple approach to the design of multi-modality motion imaging phantoms that use mechanically stored energy to produce motion.We propose two phantom designs that use mainsprings and elastic bands to store energy. A rectangular piece was attached to an axle at the end of the transmission chain of each phantom, and underwent a rotary motion upon release of the mechanical motor. The phantoms were imaged with MRI and US, and the image sequences were embedded in a 1D non linear manifold (Laplacian Eigenmap) and the spectrogram of the embedding was used to derive the angular velocity over time. The derived velocities were consistent and reproducible within a small error. The proposed motion phantom concept showed great potential for the construction of simple and affordable motion phantoms.

Virtual interaction and visualisation of 3D medical imaging data with VTK and Unity.

The authors present a method to interconnect the Visualisation Toolkit (VTK) and Unity. This integration enables them to exploit the visualisation capabilities of VTK with Unity's widespread support of virtual, augmented, and mixed reality displays, and interaction and manipulation devices, for the development of medical image applications for virtual environments. The proposed method utilises OpenGL context sharing between Unity and VTK to render VTK objects into the Unity scene via a Unity native plugin. The proposed method is demonstrated in a simple Unity application that performs VTK volume rendering to display thoracic computed tomography and cardiac magnetic resonance images. Quantitative measurements of the achieved frame rates show that this approach provides over 90 fps using standard hardware, which is suitable for current augmented reality/virtual reality display devices.

ApoE influences regional white-matter axonal density loss in Alzheimer's disease.

Mechanisms underlying phenotypic heterogeneity in young onset Alzheimer disease (YOAD) are poorly understood. We used diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI) with tract-based spatial statistics to investigate apolipoprotein (APOE) ε4 modulation of white-matter damage in 37 patients with YOAD (22, 59% APOE ε4 positive) and 23 age-matched controls. Correlation between neurite density index (NDI) and neuropsychological performance was assessed in 4 white-matter regions of interest. White-matter disruption was more widespread in ε4+ individuals but more focal (posterior predominant) in the absence of an ε4 allele. NODDI metrics indicate fractional anisotropy changes are underpinned by combinations of axonal loss and morphological change. Regional NDI in parieto-occipital white matter correlated with visual object and spatial perception battery performance (right and left, both p = 0.02), and performance (nonverbal) intelligence (WASI matrices, right, p = 0.04). NODDI provides tissue-specific microstructural metrics of white-matter tract damage in YOAD, including NDI which correlates with focal cognitive deficits, and APOEε4 status is associated with different patterns of white-matter neurodegeneration.

NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data.

Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download. We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require.

Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data.

Neuroimaging biomarkers play a prominent role for disease diagnosis or tracking neurodegenerative processes. Multiple methods have been proposed by the community to extract robust disease specific markers from various imaging modalities. Evaluating the accuracy and robustness of developed methods is difficult due to the lack of a biologically realistic ground truth. We propose a proof-of-concept method for a patient- and disease-specific brain neurodegeneration simulator. The proposed scheme, based on longitudinal multi-modal data, has been applied to a population of normal controls and patients diagnosed with Alzheimer's disease or frontotemporal dementia. We simulated follow-up images from baseline scans and compared them to real repeat images. Additionally, simulated maps of volume change are generated, which can be compared to maps estimated from real longitudinal data. The results indicate that the proposed simulator reproduces realistic patient-specific patterns of longitudinal brain change for the given populations.

In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing.

In vivo imaging of cardiac 3D fibre architecture is still a practical and methodological challenge. However it potentially provides important clinical insights, for example leading to a better understanding of the pathophysiology and the follow up of ventricular remodelling after therapy. Recently, the acquisition of 2D multi-slice Diffusion Tensor Images (DTI) of the in vivo human heart has become feasible, yielding a limited number of slices with relatively poor signal-to-noise ratios. In this article, we present a method to analyse the fibre architecture of the left ventricle (LV) using shape-based transformation into a normalised Prolate Spheroidal coordinate frame. Secondly, a dense approximation scheme of the complete 3D cardiac fibre architecture of the LV from a limited number of DTI slices is proposed and validated using ex vivo data. Those two methods are applied in vivo to a group of healthy volunteers, on which 2D DTI slices of the LV were acquired using a free-breathing motion compensated protocol. Results demonstrate the advantages of using curvilinear coordinates both for the anaylsis and the interpolation of cardiac DTI information. Resulting in vivo fibre architecture was found to agree with data from previous studies on ex vivo hearts.

In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images.

In vivo imaging of the cardiac 3D fibre architecture is still a challenge, but it would have many clinical applications, for instance to better understand pathologies and to follow up remodelling after therapy. Recently, cardiac MRI enabled the acquisition of Diffusion Tensor images (DTI) of 2D slices. We propose a method for the complete 3D reconstruction of cardiac fibre architecture in the left ventricular myocardium from sparse in vivo DTI slices. This is achieved in two steps. First we map non-linearly the left ventricular geometry to a truncated ellipsoid. Second, we express coordinates and tensor components in Prolate Spheroidal System, where an anisotropic Gaussian kernel regression interpolation is performed. The framework is initially applied to a statistical cardiac DTI atlas in order to estimate the optimal anisotropic bandwidths. Then, it is applied to in vivo beating heart DTI data sparsely acquired on a healthy subject. Resulting in vivo tensor field shows good correlation with literature, especially the elevation (helix) angle transmural variation. To our knowledge, this is the first reconstruction of in vivo human 3D cardiac fibre structure. Such approach opens up possibilities in terms of analysis of the fibre architecture in patients.

Distribution and fibre field similarity mapping of the human anterior commissure fibres by diffusion tensor imaging.

OBJECT: The anterior commissure is a critical interhemispheric pathway in animals, yet its connections in humans are not clearly understood. Its distribution has shown to vary greatly between species, and it is thought that in humans it may convey axons from a larger territory than previously thought. The aim was to use an anatomical mapping tool to look at the anterior commissure fibres and to compare the distribution findings with published anatomical understanding. MATERIALS AND METHODS: Two different diffusion-weighted imaging data sets were acquired from eight healthy subjects using a 3 Tesla MR scanner with 32 gradient directions. Diffusion tensor imaging tractography was performed, and the anterior commissure fibres were selected using three-dimensional regions of interest. Distribution of the fibres was observed by means of registration with T2-weighted images. The fibre field similarity maps were produced for five of the eight subjects by comparing each subject's fibres to the combined map of the five data sets. RESULTS: Fibres were shown to lead into the temporal lobe and towards the orbitofrontal cortex in the majority of subjects. Fibres were also distributed to the parietal or occipital lobes in all five subjects in whom the anterior commissure was large enough for interhemispheric fibres to be tracked through. The fibre field similarity maps highlighted areas where the local distances of fibre tracts were displayed for each subject compared to the combined bundle map. CONCLUSION: The anterior commissure may play a more important role in interhemispheric communication than currently presumed by conveying axons from a wider territory, and the fibre field similarity maps give a novel approach to quantifying and visualising characteristics of fibre tracts.