4 research outputs found
Cardiac motion and deformation estimation in tagged magnetic resonance imaging
Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate
of 4.3 million deaths each year. The assessment of the regional wall deformation is a
relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays,
this study can be performed using several image modalities. In the current thesis, we
focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique
allows acquiring images with tags on the myocardium, which deform with the muscle.
The present thesis intends to assess the left ventricle (LV) deformation using
radial and circumferential strain. To compute such strain values, both endo- and
epicardial contours of the LV are required.
As such, a new framework to automatically assess the LV function is proposed.
This framework presents: (i) an automatic segmentation technique, based on a tag
suppression strategy followed by an active contour segmentation method, and (ii) a
tracking approach to extract myocardial deformation, based on a non-rigid registration
method. The automatic segmentation uses the B-spline Explicit Active Surface
framework, which was previously applied in ultra-sound and cine-MRI images. In both
cases, a real-time and accurate contour was achieved. Regarding the registration step,
starting from a state-of-art approach, termed sequential 2D, we suggest a new method
(termed sequential 2D+t), where the temporal information is included on the model.
The tracking methods were first tested on synthetic data to study the registration
parameters influence. Furthermore, the proposed and original methods were applied on
porcine data with myocardial ischemia. Both methods were able to detect dysfunctional
regions. A comparison between the strain curve in the sequential 2D and sequential
2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve
was detected in the sequential 2D+t strategy. The validation of the segmentation
approach uses a human dataset. A comparison between the manual contour and the
proposed segmentation method results was performed. The results, suggest that
proposed method has an acceptable performance, removing the tedious task related with
manual segmentation and the intra-observer variability. Finally, a comparison between
the proposed framework and the currently available commercial software was
performed. The commercial software results were obtained from core-lab analysis. An
acceptable result (r = 0.601) was achieved when comparing the strain peak values.
Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com
aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do
miocárdio a um nível local é um importante indicador clínico e pode ser usado para a
deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias
modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi
a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se
deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca.
Nesta tese, pretende-se estudar a deformação radial e circunferencial do
ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial.
Desta forma, uma ferramenta para o estudo da deformação do VE foi
desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma
estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno
ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado
em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit
Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em
ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada.
Vários esquemas de registo foram apresentados. Neste ponto, começando com uma
técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi
proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo.
De forma a analisar a influência dos parâmetros do registo, estes foram
estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram
testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar
as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas
para cada um dos métodos propostos, foi possível observar uma suavização na direção
temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi
validada com um dataset humano. Um contorno manual foi comparado com o obtido
pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo
mais rápida do que a realização de um contorno manual e eliminando a variabilidade
entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e
um software comercial (com análise de core-lab). A comparação entre os valores de
pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante
notar, que a nova ferramenta tende a apresentar um resultado mais aceitável
Multi-modality cardiac image computing: a survey
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities.
This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future
An image segmentation and registration approach to cardiac function analysis using MRI
Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent
years, significant progress has been made in the care and treatment of patients with such
diseases. A crucial factor for this progress has been the development of magnetic resonance
(MR) imaging which makes it possible to diagnose and assess the cardiovascular function
of the patient. The ability to obtain high-resolution, cine volume images easily and safely
has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability
to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during
the image acquisition process. With the development of advanced MR imaging acquisition
technologies, 3D MR imaging is more and more clinically feasible. This recent development has
allowed new potentially 3D image analysis technologies to be deployed. However, quantitative
analysis of cardiovascular system from the images remains a challenging topic.
The work presented in this thesis describes the development of segmentation and motion
analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic
resonance (CMR) images. The first main contribution of the thesis is the development of a fully
automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art
techniques. The proposed segmentation technique is capable of generating an accurate 3D
segmentation from multiple image sequences. The proposed segmentation technique is robust
even in the presence of pathological changes, large anatomical shape variations and locally
varying contrast in the images.
Another main contribution of this thesis is the development of motion tracking techniques that
can integrate motion information from different sources. For example, the radial motion of
the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial
surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of
both longitudinal and circumferential motion. We propose a novel technique based on non-rigid
image registration for the myocardial motion estimation using both untagged and 3D tagged MR
images. The novel aspect of our technique is its simultaneous use of complementary information
from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted
to maximise the utility of information from both images.
The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can
capture fine local details such as motion discontinuities without sacrificing robustness. We
demonstrate the capabilities of the proposed framework to accurately estimate smooth as well
as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard
FFD approach, a significant increase in registration accuracy can be observed in datasets with
discontinuous motion patterns.
Both the segmentation and motion tracking techniques presented in this thesis have been
applied to clinical studies. We focus on two important clinical applications that can be
addressed by the techniques proposed in this thesis. The first clinical application aims
at measuring longitudinal changes in cardiac morphology and function during the cardiac
remodelling process. The second clinical application aims at selecting patients that positively
respond to cardiac resynchronization therapy (CRT).
The final chapter of this thesis summarises the main conclusions that can be drawn from the
work presented here and also discusses possible avenues for future research