Shape analysis in shape space

This study aims to classify different deformations based on the shape space concept. A shape space is a quotient space in which each point corresponds to a class of shapes. The shapes of each class are transformed to each other by a transformation group preserving a geometrical property in which we are interested. Therefore, each deformation is a curve on the high dimensional shape space manifold, and one can classify the deformations by comparison of their corresponding deformation curves in shape space. Towards this end, two classification methods are proposed. In the first method, a quasi conformal shape space is constructed based on a novel quasi-conformal metric, which preserves the curvature changes at each vertex during the deformation. Besides, a classification framework is introduced for deformation classification. The results on synthetic and real datasets show the effectiveness of the metric to estimate the intrinsic geometry of the shape space manifold, and its ability to classify and interpolate different deformations. In the second method, we introduce the medial surface shape space which classifies the deformations based on the medial surface and thickness of the shape. This shape space is based on the log map and uses two novel measures, average of the normal vectors and mean of the positions, to determine the distance between each pair of shapes on shape space. We applied these methods to classify the left ventricle deformations. The experimental results shows that the first method can remarkably classify the normal and abnormal subjects but this method cannot spot the location of the abnormality. In contrast, the second method can discriminate healthy subjects from patients with cardiomyopathy, and also can spot the abnormality on the left ventricle, which makes it a valuable assistant tool for diagnostic purposes

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

Embedded system for real-time digital processing of medical Ultrasound Doppler signals

Ultrasound (US) Doppler systems are routinely used for the diagnosis of cardiovascular diseases. Depending on the application, either single tone bursts or more complex waveforms are periodically transmitted throughout a piezoelectric transducer towards the region of interest. Extraction of Doppler information from echoes backscattered from moving blood cells typically involves coherent demodulation and matched filtering of the received signal, followed by a suitable processing module. In this paper, we present an embedded Doppler US system which has been designed as open research platform, programmable according to a variety of strategies in both transmission and reception. By suitably sharing the processing tasks between a state-of-the-art FGPA and a DSP, the system can be used in several medical US applications. As reference examples, the detection of microemboli in cerebral circulation and the measurement of wall _distension_ in carotid arteries are finally presented

Development of a Surgical Assistance System for Guiding Transcatheter Aortic Valve Implantation

Development of image-guided interventional systems is growing up rapidly in the recent years. These new systems become an essential part of the modern minimally invasive surgical procedures, especially for the cardiac surgery. Transcatheter aortic valve implantation (TAVI) is a recently developed surgical technique to treat severe aortic valve stenosis in elderly and high-risk patients. The placement of stented aortic valve prosthesis is crucial and typically performed under live 2D fluoroscopy guidance. To assist the placement of the prosthesis during the surgical procedure, a new fluoroscopy-based TAVI assistance system has been developed. The developed assistance system integrates a 3D geometrical aortic mesh model and anatomical valve landmarks with live 2D fluoroscopic images. The 3D aortic mesh model and landmarks are reconstructed from interventional angiographic and fluoroscopic C-arm CT system, and a target area of valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3D aortic mesh model, landmarks and target area of implantation onto fluoroscopic images is updated by approximating the aortic root motion from a pigtail catheter motion without contrast agent. A rigid intensity-based registration method is also used to track continuously the aortic root motion in the presence of contrast agent. Moreover, the aortic valve prosthesis is tracked in fluoroscopic images to guide the surgeon to perform the appropriate placement of prosthesis into the estimated target area of implantation. An interactive graphical user interface for the surgeon is developed to initialize the system algorithms, control the visualization view of the guidance results, and correct manually overlay errors if needed. Retrospective experiments were carried out on several patient datasets from the clinical routine of the TAVI in a hybrid operating room. The maximum displacement errors were small for both the dynamic overlay of aortic mesh models and tracking the prosthesis, and within the clinically accepted ranges. High success rates of the developed assistance system were obtained for all tested patient datasets. The results show that the developed surgical assistance system provides a helpful tool for the surgeon by automatically defining the desired placement position of the prosthesis during the surgical procedure of the TAVI.Die Entwicklung bildgeführter interventioneller Systeme wächst rasant in den letzten Jahren. Diese neuen Systeme werden zunehmend ein wesentlicher Bestandteil der technischen Ausstattung bei modernen minimal-invasiven chirurgischen Eingriffen. Diese Entwicklung gilt besonders für die Herzchirurgie. Transkatheter Aortenklappen-Implantation (TAKI) ist eine neue entwickelte Operationstechnik zur Behandlung der schweren Aortenklappen-Stenose bei alten und Hochrisiko-Patienten. Die Platzierung der Aortenklappenprothese ist entscheidend und wird in der Regel unter live-2D-fluoroskopischen Bildgebung durchgeführt. Zur Unterstützung der Platzierung der Prothese während des chirurgischen Eingriffs wurde in dieser Arbeit ein neues Fluoroskopie-basiertes TAKI Assistenzsystem entwickelt. Das entwickelte Assistenzsystem überlagert eine 3D-Geometrie des Aorten-Netzmodells und anatomischen Landmarken auf live-2D-fluoroskopische Bilder. Das 3D-Aorten-Netzmodell und die Landmarken werden auf Basis der interventionellen Angiographie und Fluoroskopie mittels eines C-Arm-CT-Systems rekonstruiert. Unter Verwendung dieser Aorten-Netzmodelle wird das Zielgebiet der Klappen-Implantation automatisch geschätzt. Mit Hilfe eines auf Template Matching basierenden Tracking-Ansatzes wird die Überlagerung des visualisierten 3D-Aorten-Netzmodells, der berechneten Landmarken und der Zielbereich der Implantation auf fluoroskopischen Bildern korrekt überlagert. Eine kompensation der Aortenwurzelbewegung erfolgt durch Bewegungsverfolgung eines Pigtail-Katheters in Bildsequenzen ohne Kontrastmittel. Eine starrere Intensitätsbasierte Registrierungsmethode wurde verwendet, um kontinuierlich die Aortenwurzelbewegung in Bildsequenzen mit Kontrastmittelgabe zu detektieren. Die Aortenklappenprothese wird in die fluoroskopischen Bilder eingeblendet und dient dem Chirurg als Leitfaden für die richtige Platzierung der realen Prothese. Eine interaktive Benutzerschnittstelle für den Chirurg wurde zur Initialisierung der Systemsalgorithmen, zur Steuerung der Visualisierung und für manuelle Korrektur eventueller Überlagerungsfehler entwickelt. Retrospektive Experimente wurden an mehreren Patienten-Datensätze aus der klinischen Routine der TAKI in einem Hybrid-OP durchgeführt. Hohe Erfolgsraten des entwickelten Assistenzsystems wurden für alle getesteten Patienten-Datensätze erzielt. Die Ergebnisse zeigen, dass das entwickelte chirurgische Assistenzsystem ein hilfreiches Werkzeug für den Chirurg bei der Platzierung Position der Prothese während des chirurgischen Eingriffs der TAKI bietet

Analysis of Venous Blood Flow and Deformation in the Calf under External Compression

Deep vein thrombosis (DVT) is a common post-operative complication, and a serious threat to the patient’s general recovery. In recent years, there has been increasing awareness of the risk of DVT in healthy individuals after prolonged immobility, such as people taking long-period flights or sitting at a computer. Mechanical methods of DVT prophylaxis, such as compression stockings, have gained widespread acceptance, but the haemodynamic mechanism of their action is still not well understood. In this study, computational modelling approaches based on magnetic resonance (MR) images are used to (i) predict the deformation of calf and deep veins under external compression, (ii) determine blood flow and wall shear stress in the deep veins of the calf, and (iii) quantify the effect of external compression on flow and wall shear stress in the deep veins. As a first step, MR images of the calf obtained with and without external compression were analysed, which indicated different levels of compressibility for different calf muscle compartments. A 2D finite element model (FEM) with specifically tailored boundary conditions for different muscle components was developed to simulate the deformation of the calf under compression. The calf tissues were described by a linear elastic model. The simulation results showed a good qualitative agreement with the measurements in terms of deep vein deformation, but the area reduction predicted by the FEM was much larger than that obtained from the MR images. In an attempt to improve the 2D FEM, a hyperelastic material model was employed and a finite element based non-rigid registration algorithm was developed to calculate the bulk modulus of the calf tissues. Using subject-specific bulk modulus derived with this method together with a hyperelastic material model, the numerical results showed better quantitative agreement with MR measured deformations of deep veins and calf tissues. In order to understand the effect of external compression on flow in the deep veins, MR imaging and real-time flow mapping were performed on 10 healthy volunteers before and after compression. Computational fluid dynamics was then employed to calculate the haemodynamic wall shear stress (WSS), based on the measured changes in vessel geometry and flow waveforms. The overall results indicated that application of the compression stocking led to a reduction in both blood flow rate and cross sectional area of the peroneal veins in the calf, which resulted in an increase in WSS, but the individual effects were highly variable. Finally, a 3D fluid-structure interactions (FSI) model was developed for a segment of the calf with realistic geometry for the calf muscle and bones but idealised geometry for the deep vein. The hyperelastic material properties evaluated previously were employed to describe the solid behaviours. Some predictive ability of the FSI model was demonstrated, but further improvement and validation are still needed

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:01

Quantitative Cardiac Magnetic Resonance Imaging Biomarkers for the Characterisation of Ischaemic Cardiomyopathy

Our understanding of the processes that determine outcomes in patients with ischaemic cardiomyopathy is based on conventional physiological concepts such as ischaemia and viability. Qualitative methods for characterising these processes tend to be binary and often fail to capture the complexity of the underlying biology. Importantly, these are perhaps inadequate to evaluate treatment effects, including the impact of coronary revascularisation. The aim of this thesis was to deploy novel quantitative cardiac magnetic resonance (CMR) techniques to evaluate and distinguish between the pathophysiological processes that determine outcomes in patients with ischaemic cardiomyopathy, through integration of anatomical, functional, perfusion and tissue characterisation information. The work is centred around the use of coronary artery bypass graft (CABG) surgery as the method for revascularisation, and focuses on the impact of myocardial blood flow alterations on cardiac physiology and clinical outcomes. In this work, I first evaluate the impact of surgical revascularisation on myocardial structure and function in patients with impaired left ventricular (LV) systolic function, using paired assessments before and after CABG. I found that at 6 months following revascularisation, despite improvement in functional capacity, more than a third of total myocardial segments examined are no longer considered revascularised. As a result, the overall augmentation in global myocardial blood flow (MBF) following CABG surgery is significantly blunted. There are however technical concerns regarding the quantitative estimation of myocardial blood flow in patients with coronary artery grafts, particularly in relation to the impact of long coronary grafts on contrast kinetics. I therefore evaluated the impact of arterial contrast delay on myocardial blood flow estimation in patients with left internal mammary artery (LIMA) grafts. I showed that absolute MBF estimation is minimally affected by delayed contrast arrival in patients with LIMA grafts, and that irrespective of graft patency, residual native disease severity is a key determinant of myocardial blood flow. Following these findings, I then assessed the prognostic impact of myocardial blood flow in a large cohort of patients with prior CABG. The only imaging study to date examining the prognostic role of quantitative perfusion indices in this population, it demonstrated that both stress MBF and myocardial perfusion reserve (MPR) independently predict adverse cardiovascular outcomes and all cause-mortality. Finally, using the existing quantitative perfusion technique and its associated framework, I co-developed and implemented a non-invasive, in-line method of measuring pulmonary transit time (PTT) and pulmonary blood volume (PBV) during routine CMR scanning. I then found that both imaging parameters can be used as independent quantitative prognostic biomarkers in patients with known or suspected coronary artery disease