8 research outputs found
A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI : the case of parotid changes radiotherapy
Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approachesthe commercial MIM, the open-source Elastix software, and an optimized version of it.
Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.
Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.
Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations
Toward adaptive radiotherapy
Intensity Modulated Radiotherapy (IMRT) and proton therapy are the state-of-art external radiotherapy modalities. To make the most of such precise delivery, accurate knowledge of the patient anatomy and biology during treatment is necessary, as unaccounted variations can compromise the outcome of the treatment. Treatment modification to account for deviations from the planning stage is a framework known as adaptive radiotherapy (ART). To fully utilise the information extracted from different modalities and/or at different time-points it is required to accurately align the imaging data. In this work the feasibility of cone-beam computed tomography (CBCT) and deformable image registration (DIR) for ART was evaluated in the context of head and neck (HN) and lung malignancies, and for IMRT and proton therapy applications. This included the geometric validation of deformations for multiple DIR algorithms, estimating the uncertainty in dose recalculation of a CBCT-based deformed CT (dCT), and the uncertainty in dose summation resulting from the properties of the underlying deformations. The dCT method was shown to be a good interim solution to repeat CT and a superior alternative to simpler direct usage of CBCT for dose calculation; proton therapy treatments were more sensitive to registration errors than IMRT. The ability to co-register multimodal and multitemporal data of the HN was also explored; the results found were promising and the limitations of current algorithms and data acquisition protocols were identified. The use of novel artificial cancer masses as a novel platform for the study of imaging during radiotherapy was explored in this study. The artificial cancer mass model was extended to generate magnetic resonance imaging (MRI)-friendly samples. The tumoroids were imageable in standard T1 and T2 MRI acquisitions, and the relaxometric properties were measured. The main limitation of the current tumour model was the poor reproducibility and controllability of the properties of the samples
3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.
Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control
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A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration
Medical imaging is widely used today to facilitate both disease diagnosis and treatment planning practice, with a key prerequisite being the systematic process of medical image registration (MIR) to align either mono or multimodal images of different anatomical parts of the human body. MIR utilises a similarity measure (SM) to quantify the level of spatial alignment and is particularly demanding due to the presence of inherent modality characteristics like intensity non-uniformities (INU) in magnetic resonance images and large homogeneous non-vascular regions in retinal images. While various intensity and feature-based SMs exist for MIR, mutual information (MI) has become established because of its computational efficiency and ability to register multimodal images. It is however, very sensitive to interpolation artefacts in the presence of INU with noise and can be compromised when overlapping areas are small. Recently MI-based hybrid variants which combine regional features with intensity have emerged, though these incur high dimensionality and large computational overheads.
To address these challenges and secure accurate, efficient and robust registration of images containing high INU, noise and large homogeneous regions, this thesis presents a new hybrid SM framework for 2D multimodal rigid MIR. The framework consistently provides superior quantitative and qualitative performance, while offering a uniquely flexible design trade-off between registration accuracy and computational time. It makes three significant technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational efficiency; and iii) an adaptive algorithm to select the most significant principal components for feature selection.
The thesis findings conclusively confirm the hybrid SM framework offers an accurate and robust 2D registration solution for challenging multimodal medical imaging datasets, while its inherent flexibility means it can also be extended to the 3D registration domain
Model-based segmentation and registration of multimodal medical images
Ph.DDOCTOR OF PHILOSOPH
Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging
Different medical imaging modalities provide complementary anatomical and
functional information. One increasingly important use of such information is in
the clinical management of cardiovascular disease. Multi-modality data is helping
improve diagnosis accuracy, and individualize treatment. The Clinical Research
Imaging Centre at the University of Edinburgh, has been involved in a number
of cardiovascular clinical trials using longitudinal computed tomography (CT) and
multi-parametric magnetic resonance (MR) imaging. The critical image processing
technique that combines the information from all these different datasets is known
as image registration, which is the topic of this thesis. Image registration, especially
multi-modality and multi-parametric registration, remains a challenging field in
medical image analysis. The new registration methods described in this work were
all developed in response to genuine challenges in on-going clinical studies. These
methods have been evaluated using data from these studies.
In order to gain an insight into the building blocks of image registration methods,
the thesis begins with a comprehensive literature review of state-of-the-art algorithms.
This is followed by a description of the first registration method I developed to help
track inflammation in aortic abdominal aneurysms. It registers multi-modality and
multi-parametric images, with new contrast agents. The registration framework uses a
semi-automatically generated region of interest around the aorta. The aorta is aligned
based on a combination of the centres of the regions of interest and intensity matching.
The method achieved sub-voxel accuracy.
The second clinical study involved cardiac data. The first framework failed to
register many of these datasets, because the cardiac data suffers from a common
artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I
developed a new preprocessing technique that is able to correct the artefacts in the
functional data using data from the anatomical scans. The registration framework,
with this preprocessing step and new particle swarm optimizer, achieved significantly
improved registration results on the cardiac data, and was validated quantitatively
using neuro images from a clinical study of neonates. Although on average
the new framework achieved accurate results, when processing data corrupted
by severe artefacts and noise, premature convergence of the optimizer is still a
common problem. To overcome this, I invented a new optimization method, that
achieves more robust convergence by encoding prior knowledge of registration. The
registration results from this new registration-oriented optimizer are more accurate
than other general-purpose particle swarm optimization methods commonly applied
to registration problems.
In summary, this thesis describes a series of novel developments to an image
registration framework, aimed to improve accuracy, robustness and speed. The
resulting registration framework was applied to, and validated by, different types of
images taken from several ongoing clinical trials. In the future, this framework could
be extended to include more diverse transformation models, aided by new machine
learning techniques. It may also be applied to the registration of other types and
modalities of imaging data
Una contribución al registrado articulado: aplicación a la determinación de la maduración ósea mediante análisis de imágenes radiográficas
Este trabajo se plantea de un modo inédito de registrado elástico de imágenes médicas llamado “registrado articulado”. Está basado en un modelo anatómico formado por puntos de control anatómicos conectados entre sí en el que cada hueso es registrado de forma afín y los tejidos blandos son registrados de forma elástica, manteniendo las estructuras óseas largas rectas mientras que la transformación a lo largo de toda la imagen es continua y suave. Se aplica el método articulado en el registrado de radiografías de la mano superando a otros métodos alternativos. Además hemos generalizado al marco de registrado poliafín Log-Euclídeo de forma que permita estructuras articuladas.
Se propone también una metodología inédita basada en registrado de imagen para determinar de forma automática la edad ósea siguiendo el método TW3. Este método realiza el registrado de la radiografía de la mano como un paso previo. Proponemos dos clasificadores diferentes y comparamos sus resultados con los diagnósticos realizados por dos especialistas pediátricos; la metodología seguida nos lleva a la conclusión de que nuestro método automático obtiene resultados comparables a los diagnósticos inter-observador.Departamento de Teoría de la Señal y Comunicaciones e Ingenieria Telemátic
Nonrigid registration of multitemporal CT and MR images for radiotherapy treatment planning
External beam radiotherapy treats cancer lesions with ionizing radiation. Successful treatment requires a correct definition of the target volume. This is achieved using pre-treatment MR and CT images. However, due to changes in patient position, tumor size and organ location, adaptation of the treatment plan over the different treatment sessions might be wanted. This can be achieved with extra MR and CT images obtained during treatment. Bringing all images into a common reference frame, the initial segmentations can be propagated over time and the integrated dose can be correctly calculated.
In this article, we show in two patients with rectum cancer and one with neck cancer that a significant change in tumor position and shape occurs. Our results show that nonrigid registration can correctly detect these shape and position changes in MR images. Validation was performed using manual delineations. For delineations of the manilible, parotid and submandibular gland in the head-and-neck patient, the maximal centroid error decreases from 6 mm to 2 mm, while the minimal Dice similarity criterium (DSC) overlap measure increases from 0.70 to 0.84. In the rectal cancer patients, the maximal centroid error drops from 15 mm to 5 mm, while the minimal DSC rises from 0.22 to 0.57.
Similar experiments were performed on CT images. The validation here was infeasible due to significant inaccuracies in the manual delineations