Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning

Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10]. MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs). Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way to quantify the pain is needed for better diagnosis and treatment. Various medical imaging technologies have been used to try to find quantifiable and measurable biomarkers of MTrPs. Ultrasound imaging, with it’s accessibility and variety of modalities, has shown significant findings in identifying MTrPs. Elastography ultrasound, which is used for measuring stiffness in soft tissues, has shown that MTrPs tend to be stiffer than normal muscle tissue. Doppler ultrasound has shown that bloodflow velocities differ significantly in areas surrounding MTrPs. MTrPs have been identified in standard B-mode grayscale ultrasound, but have varying conclusions with some studies identifying them as dark hypoechoic blobs and other studies showing them as bright hyperechoic blobs. Despite these discoveries, there is a high variance among results with no correlations to severity or pain. As a step towards quantifying the pain associated with MTrPs, this work aims to introduce a machine learning approach using image processing with texture recognition to detect MTrPs in Bmode ultrasound. A texture recognition algorithm called Gray Level Co-Occurrence Matrix (GLCM) is used to extract texture features from the B-mode ultrasound image. Feature maps are generated to emphasize these texture features in an image format in anticipation that a deep convolutional neural network will be able to correlate the features with the presence of a MTrP. The GLCM feature maps are compared to the elastography ultrasound to determine any correlations with muscle stiffness and then evaluated in the presence of MTrPs. The feature map generation is accelerated with a GPU-based implementation for the goal of real-time processing and inference of the machine learning model. Finally, two deep learning models are implemented to detect MTrPs comparing the effect of using GLCM feature maps of B-mode ultrasound to emphasize texture features for machine learning model inputs

Automatic classification of deformable shapes

Let $\mathcal{D}$ be a dataset of smooth 3D-surfaces, partitioned into disjoint classes $\mathit{CL}_j$, $j= 1, \ldots, k$. We show how optimized diffeomorphic registration applied to large numbers of pairs $S,S' \in \mathcal{D}$ can provide descriptive feature vectors to implement automatic classification on $\mathcal{D}$, and generate classifiers invariant by rigid motions in $\mathbb{R}^3$. To enhance accuracy of automatic classification, we enrich the smallest classes $\mathit{CL}_j$ by diffeomorphic interpolation of smooth surfaces between pairs $S,S' \in \mathit{CL}_j$. We also implement small random perturbations of surfaces $S\in \mathit{CL}_j$ by random flows of smooth diffeomorphisms $F_t:\mathbb{R}^3 \to \mathbb{R}^3$. Finally, we test our automatic classification methods on a cardiology data base of discretized mitral valve surfaces.Comment: 29 pages; 8 figures; one tabl

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

3D heart reconstruction

The purpose of this thesis was to achieve a 3D reconstruction of the four heart chambers using 2D echocardiographic images. A level set algorithm based on the phase symmetry approach and on a new logarithmic based stopping function was used to extract simultaneously the four heart cavities from these images in a fully automatic way. However to proceed to the 3D reconstruction using the segmented images, it was first necessary to satisfy clinical practise requirements. This means that the algorithm had to be validated to access the performance of the segmentation. Regarding this, the framework of this thesis was divided in two parts: validation of the segmentation algorithm and 3D reconstruction. The contours obtained in the segmentation were compared with the ones obtained by four physicians to evaluate the performance, reliability and confidence for eventual clinical practice. That algorithm evaluation versus clinicians‟ performance was made using eleven figures of merit: Mean/Max/Larger than 5 pixels Distance, Pratt Function, Hausdorff Distance, Similarity Angle, Similarity Region, Accuracy, Overlap, Sensitivity and Specificity; and two statistical tools: Box-plots and Dendrograms. The results indicate a reliable performance of the level set method for all chambers. The evaluation was based on echocardiography images of children. The 3D reconstruction was achieved using a heart phantom. This phantom was mainly composed by four balloons attached together and submerged in a water environment. A robotic arm with an ultrasound probe attached was used to take a large number of frames from the heart phantom. Several attempts were made using two types of acquisition: in Rotation and in Translation. Offline reconstructions of two rotations and one translation were presented and analyzed. The results of the rotations were far better than the translation. It was possible to infer the shape and volume of the balloons. These results present one more step in the way for a real-time 3D reconstruction using a tele-echographic syste

A Non-Rigid Registration Method for Analyzing Myocardial Wall Motion for Cardiac CT Images

Cardiac resynchronization therapy (CRT) has a high percentage of non-responders. Successfully locating the optimal location for CRT lead placement on a priori images can increase efficiency in procedural preparation and execution and could potentially increase the rate of CRT responders. Registration has been used in the past to assess the motion of medical images. Specifically, one method of non-rigid registration has been utilized to assess the motion of left ventricular MR cardiac images. As CT imaging is often performed as part of resynchronization treatment planning and is a fast and accessible means of imaging, extending this registration method to assessing left ventricular motion of CT images could provide another means of reproducible contractility assessment. This thesis investigates the use of non-rigid registration to evaluate the myocardium motion in multi-phase multi-slice computed tomography (MSCT) cardiac imaging for the evaluation of mechanical contraction of the left ventricle

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

Computer Aided Analysis of Late Gadolinium Enhanced Cardiac MRI

Ph.DDOCTOR OF PHILOSOPH

Quantitative imaging in cardiovascular CT angiography

In de afgelopen decennia is computertomografie (CT) een prominente niet-invasieve modaliteit om hart- en vaatziekten te evalueren geworden. Dit proefschrift heeft als doel de rol van CT in de therapeutische behandeling van coronaire hartziekte (CAD) en klepaandoeningen te onderzoeken.De relatie tussen kransslagadergeometrie (statisch en dynamisch) en aanwezigheid en omvang van CAD met CT werd onderzocht. De resultaten suggereren dat de statische geometrie van de kransslagader significant gerelateerd is aan de aanwezigheid van plaque en stenose. Er was echter geen verband tussen dynamische verandering van de coronaire arterie-geometrie en de ernst van CAD. Een algoritme om de invloed van intraluminair contrastmiddel op niet-verkalkte atherosclerotische plaque Hounsfield-Unit-waarden te corrigeren werd gepresenteerd en gevalideerd met behulp van fantomen.Diagnose en operatieplanning kunnen cruciale gevolgen hebben voor de klinische uitkomst van chirurgische ingrepen. In dit proefschrift wordt beschreven dat halfautomatische softwareprogramma’s het kwantificeren van het aortaklepgebied betere reproduceerbare resultaten toonden in vergelijking met handmatige metingen, en vergelijkbare resultaten met de huidige gouden standaard, de echocardiografie. Een systematische review over het dynamische gedrag van de aorta-annulus toont aan dat de vorm van de aorta-annulus tijdens de hartcyclus verandert, wat impliceert dat er bij het bepalen van een prothese rekening moet worden gehouden met meerdere fasen. Een andere review beschrijft het gebruik van 3D-printen in de chirurgische planning samen met andere toepassingen voor de behandeling van hartklepaandoeningen.CT is de belangrijkste beeldvormingsmodaliteit in deze onderzoeken, die gericht waren op de therapeutische behandeling van hart- en vaatziekten, van vroege risicobepaling tot diagnose en chirurgische planning.In the recent decades computed tomography (CT) has emerged as a dominant non-invasive modality to evaluate cardiovascular diseases. This thesis aimed to explore the role of CT in the therapeutic management of coronary artery disease (CAD) and valvular diseases.The relationship between both static and dynamic coronary artery geometry and presence and extent of CAD using CT was investigated. The results suggest that the static coronary artery geometry is significantly related to presence of plaque and significant stenosis. However, there were no such relationship between dynamic change of coronary artery geometry and severity of CAD. As part of this thesis an algorithm to correct the influence of lumen contrast enhancement on non-calcified atherosclerotic plaque Hounsfield-Unit values was presented. The algorithm was validated using phantoms. The diagnosis and surgical planning may have crucial impact on clinical outcome. Semi-automatic software for aortic valve area quantification presented in this thesis was proven to be more repeatable and similar to gold standard echocardiography in comparison to manual measurements. The systematic review regarding the dynamic behavior of aortic annulus revealed that aortic annulus geometry changes throughout the cardiac cycle which implies that multiple phases should be taken into account for prosthesis sizing. Another review in this thesis discusses the use of 3D printing in the surgical planning along with other applications for the treatment of valvular diseases.CT is the main imaging modality in these studies which were focused on the therapeutic management of cardiovascular diseases from early risk determination to diagnosis and surgical planning

Three-dimensional in-vivo intra-cardiac vortex flow from 4D Flow MRI : quantification, automatic identification and association with energy loss

Despite one might intuitively think that blood inflows inside the human heart in a straight path, direct imaging of cardiac blood flow showed that, actually, cardiac blood inflows in a swirling motion forming what is called vortex flow pattern. In fact, blood inflow forms a pair of counter-rotating vortices distal to the mitral valve inside the heart. In three-dimensions, this pair of vortices forms a ring-like vortex: a vortex ring. This thesis revealed the three-dimensional form and time evolution of such vortex ring flow in the human heart in health and disease. This was achieved using cutting-edge 4D Flow MRI in-vivo imaging technology and developments of novel advanced methods integrating image and applied fluid mechanics concepts enabling characterization, quantification and automatic identification of heart’s vortex flow. In this thesis, a novel framework was developed allowing for the first noninvasive assessment of intra-cardiac viscous energy loss. This thesis showed that alteration in cardiac vortex ring flow properties, due to heart disease, can result in 2-4 fold increase in viscous energy loss in the left ventricle. As such, heart’s vortex flow could be an efficient mechanism of energy loss minimization in healthy hearts and might act as a biomarker of cardiac health.This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO) and partly funded by the Ministry of Economic Affairs (project number 11626).LUMC / Geneeskund

Quantitative Analysis of Ultrasound Images of the Preterm Brain

In this PhD new algorithms are proposed to better understand and diagnose white matter damage in the preterm Brain. Since Ultrasound imaging is the most suited modality for the inspection of brain pathologies in very low birth weight infants we propose multiple techniques to assist in what is called Computer-Aided Diagnosis. As a main result we are able to increase the qualitative diagnosis from a 70% detectability to a 98% quantitative detectability