Radioactive Needlework, Reconstruction of needle-positions in radiation treatment

Nucletron presented a medical problem to the SWI 2006: how to find needles used for cancer treatment in a prostate? More concretely: how to find the positions of these needles from distorted images from an ultrasound probe? Section 1 explains the background of this problem. In Section 2 we deal with physical explanations for the distortions. In Section 3 we give a brief overview of medical imaging and explain which techniques we used to clean up the images

EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 201

High-speed imaging in fluids

High-speed imaging is in popular demand for a broad range of experiments in fluids. It allows for a detailed visualization of the event under study by acquiring a series of image frames captured at high temporal and spatial resolution. This review covers high-speed imaging basics, by defining criteria for high-speed imaging experiments in fluids and to give rule-of-thumbs for a series of cases. It also considers stroboscopic imaging, triggering and illumination, and scaling issues. It provides guidelines for testing and calibration. Ultra high-speed imaging at frame rates exceeding 1 million frames per second is reviewed, and the combination of conventional experiments in fluids techniques with high-speed imaging techniques are discussed. The review is concluded with a high-speed imaging chart, which summarizes criteria for temporal scale and spatial scale and which facilitates the selection of a high-speed imaging system for the applicatio

Ultrasound based Silent Speech Interface using Deep Learning

Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) és una tecnologia capaç de sintetitzar veu partint únicament de senyals no-acústiques. Pot tenir gran utilitat en casos com pacients de laringectomia, ambients sorollosos o trucades silencioses. Aquesta tèsis explora el cas particular de SSI utilitzant imatges de la llengua captades amb ultrasons com a senyals d'entrada. Es proposa un enfocament de 'síntesis directa' basat en Xarxes Neuronals Profundes i coeficients Mel-generalized cepstral. Aquest document és una extensió del treball de Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface" . Diversos models de xarxes neuronals són presentats i discutits, com les bàsiques xarxes neuronals directes, xarxes neuronals convolucionals o xarxes neuronals recurrents. També s'ha estudiat un pre-processat reductor de soroll basat en un Autoencoder convolucional profund. S'ha portat a terme un nombre considerable d'experiments utilitzant diverses arquitectures de Deep Learning, així com un extens estudi d'optimització d'hyperparàmetres. Els diferents experiments han estat evaluar i qualificar a partir de diferentes mesures de qualitat objectives i subjectives. Els millors resultats, tant en termes objectius com subjectius, els ha presentat una arquitectura basada en una CNN i capes bidireccionals de LSTMs

Implementation of Cost Efficient Image Enhancement Technique Reduce Speckle in Ultrasound Images

Speckle is a granular multiplicative noise that reduces the resolution and contrast of the image there by degrading the diagnostic accuracy of the Ultrasound image. Speckle reduction technique has to be followed to enhance the quality of ultrasound image [3].Speckle noise occurs in all coherent imaging systems, such as ultrasound images. The speckle noise in ultrasound images is often considered as undesirable and has a negative impact on clinical practitioners for diagnosis. Because of the signal-dependent nature of the speckle intensity, speckle noise in ultrasound imaging requires specific handling. So, any ultrasound speckle de-noising method must be designed in such a way that the speckle noise be suppressed without smearing the edges. In other words, any speckle de-noising method must preserve both the edges and structural details of the image and its quality [8].Digital image enhancement techniques are to improving the visual quality of images. Main objective of image enhancement is to process an image so that result is more suitable than original image for specific application. This paper presents real time hardware image enhancement techniques using field programmable gate array (FPGA) [10].It presents architecture for filters pixel by pixel and regions filters for image processing using Xilinx System Generator (XSG). This architecture offer an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explore important aspects concerned to hardware implementation

Ultrasound Image Denoising using Multiscale Ridgelet Transform with Hard and NeighCoeff Thresholding

Ultrasound imaging utilizes sound waves reflected from different organs of the body to give local details and important diagnostic information on the human body. However, using ultrasound images for diagnosis is difficult because of the existence of speckle noise in the image. The speckle noise is due to interference between coherent waves which are backscattered by targeted surfaces and arrive out of phase at the sensor. This hampers the perception and the extraction of fine details from the image. Speckle reduction/filtering i.e. visual enhancement techniques are used for enhancing the visual quality of the images. The multscale ridgelet transform based denoising algorithm for Ultrasound images is proposed for effective edge preservation in comparison to filtering techniques using the Adaptive Filters

Non-Invasive Photoacoustic Imaging of In Vivo Mice with Erythrocyte Derived Optical Nanoparticles to Detect CAD/MI.

Coronary artery disease (CAD) causes mortality and morbidity worldwide. We used near-infrared erythrocyte-derived transducers (NETs), a contrast agent, in combination with a photoacoustic imaging system to identify the locations of atherosclerotic lesions and occlusion due to myocardial-infarction (MI). NETs (≈90 nm diameter) were fabricated from hemoglobin-depleted mice erythrocyte-ghosts and doped with Indocyanine Green (ICG). Ten weeks old male C57BL/6 mice (n = 9) underwent left anterior descending (LAD) coronary artery ligation to mimic vulnerable atherosclerotic plaques and their rupture leading to MI. 150 µL of NETs (20 µM ICG,) was IV injected via tail vein 1-hour prior to photoacoustic (PA) and fluorescence in vivo imaging by exciting NETs at 800 nm and 650 nm, respectively. These results were verified with histochemical analysis. We observed ≈256-fold higher PA signal from the accumulated NETs in the coronary artery above the ligation. Fluorescence signals were detected in LAD coronary, thymus, and liver. Similar signals were observed when the chest was cut open. Atherosclerotic lesions exhibited inflammatory cells. Liver demonstrated normal portal tract, with no parenchymal necrosis, inflammation, fibrosis, or other pathologic changes, suggesting biocompatibility of NETs. Non-invasively detecting atherosclerotic plaques and stenosis using NETs may lay a groundwork for future clinical detection and improving CAD risk assessment

Estimation of the normal contact stiffness for frictional interface in sticking and sliding conditions

Modeling of frictional contact systems with high accuracy needs the knowledge of several contact parameters, which are mainly related to the local phenomena at the contact interfaces and affect the complex dynamics of mechanical systems in a prominent way. This work presents a newer approach for identifying reliable values of the normal contact stiffness between surfaces in contact, in both sliding and sticking conditions. The combination of experimental tests, on a dedicated set-up, with finite element modeling, allowed for an indirect determination of the normal contact stiffness. The stiffness was found to increase with increasing contact pressure and decreasing roughness, while the evolution of surface topography and third-body rheology affected the contact stiffness when sliding

Semi-supervised detection of industrial fouling using ultrasound

Fouling is a large scale problem in industrial equipment such as heat exchangers or pipes, used in factories, ships, airplanes, etc. Traditionally, such equipment is cleaned using sandblasting, chemicals or mechanical methods, all of which require halting the process, which is costly. Recently, high-power ultrasound has become a viable option to these methods. In ultrasonic cleaning ultrasound is projected into the equipment from the outside, which means that the equipment does not need to be halted to perform cleaning. While the cleaning itself is not invasive in nature, in most cases vision cannot be used to determine whether cleaning is actually necessary or not. What remains is to have such a method that is also non-invasive. It is possible to use ultrasound as a kind of a radar to detect whether or not fouling is present, and this has been attempted in previous literature. However, until now, such methods have required extensive manual calculation and knowledge of the physical properties of the setup. We present the first ever system to concurrently clean and detect industrial fouling using ultrasound and deep learning. Our method does not rely on specific properties of the equipment, allowing it to generalize to large industrial processes where it is not practical to calculate or simulate the cleaning scenario. To this end, we extend existing literature on semi-supervised learning by presenting algorithms used to learn from a monotonic process, and model the high-dimensional signal data using a convolutional neural network that is highly robust to temporal variance. This thesis presents the machine learning solution behind the system, and the cleaning components are provided by Altum Technologies. Further, we explore methods to detect and counter the so-called domain shift that occurs when experimenting in the physical world, and provide experimental evidence that our methods work in practice

Prediction of far-field acoustic emissions from cavitation clouds during shock wave lithotripsy for development of a clinical device

This study presents the key simulation and decision stage of a multi-disciplinary project to develop a hospital device for monitoring the effectiveness of kidney stone fragmentation by shock wave lithotripsy (SWL). The device analyses, in real time, the pressure fields detected by sensors placed on the patient's torso, fields generated by the interaction of the incident shock wave, cavitation, kidney stone and soft tissue. Earlier free-Lagrange simulations of those interactions were restricted (by limited computational resources) to computational domains within a few centimetres of the stone. Later studies estimated the far-field pressures generated when those interactions involved only single bubbles. This study extends the free-Lagrange method to quantify the bubble–bubble interaction as a function of their separation. This, in turn, allowed identification of the validity of using a model of non-interacting bubbles to obtain estimations of the far-field pressures from 1000 bubbles distributed within the focus of the SWL field. Up to this point in the multi-disciplinary project, the design of the clinical device had been led by the simulations. This study records the decision point when the project's direction had to be led by far more costly clinical trials instead of the relatively inexpensive simulations. <br/