Image Analysis for Contrast Enhanced Ultrasound Carotid Plaque Imaging

__Abstract__ Intraplaque neovascularization (IPN) has been presented as an important biomarker for progressive atherosclerotic disease and plaque vulnerability in several pathological studies. Therefore, quantification of IPN may allow early prediction of plaque at risk of rupture and thus prevention of future cardiovascular events such as stroke. Contrast enhanced ultrasound (CEUS) enables us to detect and visualize IPN by use of ultrasound contrast agents. So, the degree of IPN can potentially be measured by quantitative imaging biomarkers derived from CEUS. Since quantification tools for IPN are scarce, so far mainly visual IPN scoring on CEUS clips has been used to assess IPN, which is subjective and tedious. Currently available commercial tools for contrast quantification, e.g. QLAB region of interest (ROI) quantification tool (Philips Medical Systems, Bothell, USA) and VueBox (Bracco Suisse SA, Geneva, Switzerland), are not suitable for quantitative analysis of IPN. These commercial quantification tools have been developed mainly for time intensity curve analysis (TIC) of large organs such as heart, liver and prostate, not for plaques. Plaques are very small and intermittently perfused. Therefore, the perfusion characteristics of plaques are quite different from those of large organs and TIC analysis as applied in large well-perfused organs is not applicable. Some IPN quantification approaches have been reported but they suffer from a number of limitations such as imaging artifacts and no or imperfect motion compensation. In this thesis work, we avoided the known limitations of IPN quantification methods reported in previous studies and developed and evaluated specialized IPN analysis tools for carotid CEUS image sequences

Relationship between radiographic features and bone mineral density in elderly men

Lumbar disc degeneration is characterised radiologically by the presence of osteophytes, endplate sclerosis, and disc space narrowing. Our study was designed to assess anterior lumbar osteophytes, disc space narrowing, end plate sclerosis, and bone mineral density (BMD) in the lumbar vertebrae and femoral neck of elderly men. A total of 1000 men, aged between 71 and 90 years, were invited to participate in the study. BMD was assessed at the spine and femoral neck using dual energy X-ray absorptiometry (DXA). We examined the relationship with the degree of lumbar spinal and femoral neck deformity by using the Z-score. Lateral and anterioposterior spinal radiographs were evaluated for features of lumbar disc degeneration. The observers consisted of a consultant physical therapist, a radiologist, and anatomists who together studied the series of radiographs. Anterior lumbar osteophytes (grade 0–3), end-plate sclerosis, and disc space narrowing (grade 0–2) were evaluated. The Pearson correlation test was used to determine the association between radiographic features, the lumbar mineral density (LBMD), and femoral neck mineral density (FNBMD). In all, 90.6% of lumbar vertebral levels showed evidence of anterior osteophytes, 87.5% showed evidence of end plate sclerosis, and 68.2% of disc space narrowing. Additionally, there was a strong negative correlation in terms of age at the femoral neck, though not at the spine. On the other hand, there was a significant correlation between osteophyte grade and end plate sclerosis at the spine. In our study, the radiographic features of lumbar disc degeneration, anterior osteophytes, and end plate sclerosis were associated with an increase in BMD at the spine. (Folia Morphol 2010; 69, 3: 170-176

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.Comment: Challenge in Adolescent Brain Cognitive Development Neurocognitive Predictio

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019

Quantification of bound microbubbles in ultrasound molecular imaging

Molecular markers associated with diseases can be visualized and quantified noninvasively with targeted ultrasound contrast agent (t-UCA) consisting of microbubbles (MBs) that can bind to specific molecular targets. Techniques used for quantifying t-UCA assume that all unbound MBs are taken out of the blood pool few minutes after injection and only MBs bound to the molecular markers remain. However, differences in physiology, diseases, and experimental conditions can increase the longevity of unbound MBs. In such conditions, unbound MBs will falsely be quantified as bound MBs. We have developed a novel technique to distinguish and classify bound from unbound MBs. In the post-processing steps, first, tissue motion was compensated using block-matching (BM) techniques. To preserve only stationary contrast signals, a minimum intensity projection (MinIP) or 20th-percentile intensity projection (PerIP) was applied. The after-flash MinIP or PerIP was subtracted from the before-flash MinIP or PerIP. In this way, tissue artifacts in contrast images were suppressed. In the next step, bound MB candidates were detected. Finally, detected objects were tracked to classify the candidates as unbound or bound MBs based on their displacement. This technique was validated in vitro, followed by two in vivo experiments in mice. Tumors (n = 2) and salivary glands of hypercholesterolemic mice (n = 8) were imaged using a commercially available scanner. Boluses of 100 μL of a commercially available t-UCA targeted to angiogenesis markers and untargeted control UCA were injected separately. Our results show considerable reduction in misclassification of unbound MBs as bound ones. Using our method, the ratio of bound MBs in salivary gland for images with targeted UCA versus control UCA was improved by up to two times compared with unprocessed images

Nonlinear Markov Random Fields Learned via Backpropagation

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.Comment: Accepted for the international conference on Information Processing in Medical Imaging (IPMI) 2019, camera ready versio

Self-consistent Spectral Function for Non-Degenerate Coulomb Systems and Analytic Scaling Behaviour

Novel results for the self-consistent single-particle spectral function and self-energy are presented for non-degenerate one-component Coulomb systems at various densities and temperatures. The GW^0-method for the dynamical self-energy is used to include many-particle correlations beyond the quasi-particle approximation. The self-energy is analysed over a broad range of densities and temperatures (n=10^17/cm^3-10^27/cm^3, T=10^2 eV/k_B-10^4 eV/k_B). The spectral function shows a systematic behaviour, which is determined by collective plasma modes at small wavenumbers and converges towards a quasi-particle resonance at higher wavenumbers. In the low density limit, the numerical results comply with an analytic scaling law that is presented for the first time. It predicts a power-law behaviour of the imaginary part of the self-energy, Im Sigma ~ -n^(1/4). This resolves a long time problem of the quasi-particle approximation which yields a finite self-energy at vanishing density.Comment: 28 pages, 9 figure

Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image intensities that may not be representative of any one of the underlying classes. PV is particularly problematic for segmentation when there is a large resolution gap between the atlas and the test scan, e.g., when segmenting clinical scans with thick slices, or when using a high-resolution atlas. In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) low resolution (LR) scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates LR images from HR label maps with a generative model of PV, and can be trained to segment scans of any desired target contrast and resolution, even for previously unseen modalities where neither images nor segmentations are available at training. PV-SynthSeg does not require any preprocessing, and runs in seconds. We demonstrate the accuracy and flexibility of the method with extensive experiments on three datasets and 2,680 scans. The code is available at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202

Determination of composition and structure of spongy bone tissue in human head of femur by Raman spectral mapping

Biomechanical properties of bone depend on the composition and organization of collagen fibers. In this study, Raman microspectroscopy was employed to determine the content of mineral and organic constituents and orientation of collagen fibers in spongy bone in the human head of femur at the microstructural level. Changes in composition and structure of trabecula were illustrated using Raman spectral mapping. The polarized Raman spectra permit separate analysis of local variations in orientation and composition. The ratios of ν2PO43−/Amide III, ν4PO43−/Amide III and ν1CO32−/ν2PO43− are used to describe relative amounts of spongy bone components. The ν1PO43−/Amide I ratio is quite susceptible to orientation effect and brings information on collagen fibers orientation. The results presented illustrate the versatility of the Raman method in the study of bone tissue. The study permits better understanding of bone physiology and evaluation of the biomechanical properties of bone