3,794 research outputs found
Advances in computational modelling for personalised medicine after myocardial infarction
Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners
From Fully-Supervised Single-Task to Semi-Supervised Multi-Task Deep Learning Architectures for Segmentation in Medical Imaging Applications
Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical imaging pipeline, providing support for clinical decision-making and computer-aided diagnosis. To be effective in addressing challenging image analysis tasks such as classification, detection, registration, and segmentation, specifically for medical imaging applications, deep learning approaches have shown significant improvement in performance. While deep learning has shown its potential in a variety of medical image analysis problems including segmentation, motion estimation, etc., generalizability is still an unsolved problem and many of these successes are achieved at the cost of a large pool of datasets. For most practical applications, getting access to a copious dataset can be very difficult, often impossible. Annotation is tedious and time-consuming. This cost is further amplified when annotation must be done by a clinical expert in medical imaging applications. Additionally, the applications of deep learning in the real-world clinical setting are still limited due to the lack of reliability caused by the limited prediction capabilities of some deep learning models. Moreover, while using a CNN in an automated image analysis pipeline, itâs critical to understand which segmentation results are problematic and require further manual examination. To this extent, the estimation of uncertainty calibration in a semi-supervised setting for medical image segmentation is still rarely reported. This thesis focuses on developing and evaluating optimized machine learning models for a variety of medical imaging applications, ranging from fully-supervised, single-task learning to semi-supervised, multi-task learning that makes efficient use of annotated training data. The contributions of this dissertation are as follows: (1) developing a fully-supervised, single-task transfer learning for the surgical instrument segmentation from laparoscopic images; and (2) utilizing supervised, single-task, transfer learning for segmenting and digitally removing the surgical instruments from endoscopic/laparoscopic videos to allow the visualization of the anatomy being obscured by the tool. The tool removal algorithms use a tool segmentation mask and either instrument-free reference frames or previous instrument-containing frames to fill in (inpaint) the instrument segmentation mask; (3) developing fully-supervised, single-task learning via efficient weight pruning and learned group convolution for accurate left ventricle (LV), right ventricle (RV) blood pool and myocardium localization and segmentation from 4D cine cardiac MR images; (4) demonstrating the use of our fully-supervised memory-efficient model to generate dynamic patient-specific right ventricle (RV) models from cine cardiac MRI dataset via an unsupervised learning-based deformable registration field; and (5) integrating a Monte Carlo dropout into our fully-supervised memory-efficient model with inherent uncertainty estimation, with the overall goal to estimate the uncertainty associated with the obtained segmentation and error, as a means to flag regions that feature less than optimal segmentation results; (6) developing semi-supervised, single-task learning via self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data; (7) proposing largely-unsupervised, multi-task learning to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two of the foremost critical tasks in medical imaging â segmentation of cardiac structures and reconstruction of the cine cardiac MR images; (8) demonstrating the use of 3D semi-supervised, multi-task learning for jointly learning multiple tasks in a single backbone module â uncertainty estimation, geometric shape generation, and cardiac anatomical structure segmentation of the left atrial cavity from 3D Gadolinium-enhanced magnetic resonance (GE-MR) images. This dissertation summarizes the impact of the contributions of our work in terms of demonstrating the adaptation and use of deep learning architectures featuring different levels of supervision to build a variety of image segmentation tools and techniques that can be used across a wide spectrum of medical image computing applications centered on facilitating and promoting the wide-spread computer-integrated diagnosis and therapy data science
Modelling mitral valvular dynamicsâcurrent trend and future directions
Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed
Respiratory-induced organ motion compensation for MRgHIFU
Summary: High Intensity Focused Ultrasound is an emerging non-invasive technology for the precise
thermal ablation of pathological tissue deep within the body. The fitful, respiratoryinduced
motion of abdominal organs, such as of the liver, renders targeting challenging.
The work in hand describes methods for imaging, modelling and managing respiratoryinduced
organ motion. The main objective is to enable 3D motion prediction of liver
tumours for the treatment with Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU).
To model and predict respiratory motion, the liver motion is initially observed in 3D
space. Fast acquired 2D magnetic resonance images are retrospectively reconstructed
to time-resolved volumes, thus called 4DMRI (3D + time). From these volumes, dense
deformation fields describing the motion from time-step to time-step are extracted using
an intensity-based non-rigid registration algorithm. 4DMRI sequences of 20 subjects,
providing long-term recordings of the variability in liver motion under free breathing,
serve as the basis for this study.
Based on the obtained motion data, three main types of models were investigated and
evaluated in clinically relevant scenarios. In particular, subject-specific motion models,
inter-subject population-based motion models and the combination of both are compared
in comprehensive studies. The analysis of the prediction experiments showed that
statistical models based on Principal Component Analysis are well suited to describe
the motion of a single subject as well as of a population of different and unobserved
subjects. In order to enable target prediction, the respiratory state of the respective
organ was tracked in near-real-time and a temporal prediction of its future position is
estimated. The time span provided by the prediction is used to calculate the new target
position and to readjust the treatment focus. In addition, novel methods for faster
acquisition of subject-specific 3D data based on a manifold learner are presented and
compared to the state-of-the art 4DMRI method.
The developed methods provide motion compensation techniques for the non-invasive
and radiation-free treatment of pathological tissue in moving abdominal organs for
MRgHIFU. ---------- Zusammenfassung: High Intensity Focused Ultrasound ist eine aufkommende, nicht-invasive Technologie
fĂŒr die prĂ€zise thermische Zerstörung von pathologischem Gewebe im Körper. Die
unregelmÀssige ateminduzierte Bewegung der Unterleibsorgane, wie z.B. im Fall der
Leber, macht genaues Zielen anspruchsvoll. Die vorliegende Arbeit beschreibt Verfahren
zur Bildgebung, Modellierung und zur Regelung ateminduzierter Organbewegung.
Das Hauptziel besteht darin, 3D Zielvorhersagen fĂŒr die Behandlung von Lebertumoren
mittels Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU) zu ermöglichen.
Um die Atembewegung modellieren und vorhersagen zu können, wird die Bewegung
der Leber zuerst im dreidimensionalen Raum beobachtet. Schnell aufgenommene 2DMagnetresonanz-
Bilder wurden dabei rĂŒckwirkend zu Volumen mit sowohl guter zeitlicher
als auch rÀumlicher Auflösung, daher 4DMRI (3D + Zeit) genannt, rekonstruiert.
Aus diesen Volumen werden Deformationsfelder, welche die Bewegung von Zeitschritt
zu Zeitschritt beschreiben, mit einem intensitÀtsbasierten, nicht-starren Registrierungsalgorithmus
extrahiert. 4DMRI-Sequenzen von 20 Probanden, welche Langzeitaufzeichungen
von der VariabilitĂ€t der Leberbewegung beinhalten, dienen als Grundlage fĂŒr
diese Studie.
Basierend auf den gewonnenen Bewegungsdaten wurden drei Arten von Modellen
in klinisch relevanten Szenarien untersucht und evaluiert. Personen-spezifische Bewegungsmodelle,
populationsbasierende Bewegungsmodelle und die Kombination beider
wurden in umfassenden Studien verglichen. Die Analyse der Vorhersage-Experimente
zeigte, dass statistische Modelle basierend auf Hauptkomponentenanalyse gut geeignet
sind, um die Bewegung einer einzelnen Person sowie einer Population von unterschiedlichen
und unbeobachteten Personen zu beschreiben. Die Bewegungsvorhersage basiert
auf der AbschÀtzung der Organposition, welche fast in Echtzeit verfolgt wird. Die durch
die Vorhersage bereitgestellte Zeitspanne wird verwendet, um die neue Zielposition zu
berechnen und den Behandlungsfokus auszurichten. DarĂŒber hinaus werden neue Methoden
zur schnelleren Erfassung patienten-spezifischer 3D-Daten und deren Rekonstruktion
vorgestellt und mit der gÀngigen 4DMRI-Methode verglichen. Die entwickelten Methoden beschreiben Techniken zur nichtinvasiven und strahlungsfreien
Behandlung von krankhaftem Gewebe in bewegten Unterleibsorganen mittels
MRgHIFU
Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes
The high speed of cardiorespiratory motion introduces a unique challenge for
cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such
treatments require tracking myocardial landmarks with a maximum latency of 100
ms, which includes the acquisition of the required data. The aim of this study
is to present a new method that allows to track myocardial landmarks from few
readouts of MRI data, thereby achieving a latency sufficient for STAR
treatments. We present a tracking framework that requires only few readouts of
k-space data as input, which can be acquired at least an order of magnitude
faster than MR-images. Combined with the real-time tracking speed of a
probabilistic machine learning framework called Gaussian Processes, this allows
to track myocardial landmarks with a sufficiently low latency for cardiac STAR
guidance, including both the acquisition of required data, and the tracking
inference. The framework is demonstrated in 2D on a motion phantom, and in vivo
on volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the
feasibility of an extension to 3D was demonstrated by in silico 3D experiments
with a digital motion phantom. The framework was compared with template
matching - a reference, image-based, method - and linear regression methods.
Results indicate an order of magnitude lower total latency (<10 ms) for the
proposed framework in comparison with alternative methods. The
root-mean-square-distances and mean end-point-distance with the reference
tracking method was less than 0.8 mm for all experiments, showing excellent
(sub-voxel) agreement. The high accuracy in combination with a total latency of
less than 10 ms - including data acquisition and processing - make the proposed
method a suitable candidate for tracking during STAR treatments
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI)
remains a challenge due to the trade-off between residual motion blurring
caused by discretized motion states and undersampling artefacts. In this work,
we propose to generate blurring-free motion-resolved abdominal reconstructions
by learning a neural implicit representation directly in k-space (NIK). Using
measured sampling points and a data-derived respiratory navigator signal, we
train a network to generate continuous signal values. To aid the regularization
of sparsely sampled regions, we introduce an additional informed correction
layer (ICo), which leverages information from neighboring regions to correct
NIK's prediction. Our proposed generative reconstruction methods, NIK and
ICoNIK, outperform standard motion-resolved reconstruction techniques and
provide a promising solution to address motion artefacts in abdominal MRI
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