206 research outputs found
Sparse Projections of Medical Images onto Manifolds
Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space.National Alliance for Medical Image Computing (U.S.) (grant NIH NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (grant NIH NCRR NAC P41-RR13218)National Institutes of Health (U.S.) (grant NIH NIBIB NAC P41-EB-015902
Autoadaptive motion modelling for MR-based respiratory motion estimation
© 2016 The Authors.Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data.In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible.We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment.We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10 min after a change in breathing pattern. On real data we demonstrated the methods ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12 min of scanning. Furthermore, the method has a prediction latency of 800 ms. These limitations may be overcome in future work by altering the acquisition protocol
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
für die präzise thermische Zerstörung von pathologischem Gewebe im Körper. Die
unregelmässige 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 für 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 rückwirkend zu Volumen mit sowohl guter zeitlicher
als auch räumlicher Auflösung, daher 4DMRI (3D + Zeit) genannt, rekonstruiert.
Aus diesen Volumen werden Deformationsfelder, welche die Bewegung von Zeitschritt
zu Zeitschritt beschreiben, mit einem intensitätsbasierten, nicht-starren Registrierungsalgorithmus
extrahiert. 4DMRI-Sequenzen von 20 Probanden, welche Langzeitaufzeichungen
von der Variabilität der Leberbewegung beinhalten, dienen als Grundlage für
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 Abschätzung 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. Darüber hinaus werden neue Methoden
zur schnelleren Erfassung patienten-spezifischer 3D-Daten und deren Rekonstruktion
vorgestellt und mit der gängigen 4DMRI-Methode verglichen. Die entwickelten Methoden beschreiben Techniken zur nichtinvasiven und strahlungsfreien
Behandlung von krankhaftem Gewebe in bewegten Unterleibsorganen mittels
MRgHIFU
Autoadaptive motion modelling for MR-based respiratory motion estimation
This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following:
Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold.
Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)]
40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing.
Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's).
The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows:
|-- bhs (breath hold data)
| |-- images (images)
| | |-- cor
| | `-- sag
| `-- mfs_slpos2slpos (registration results)
| `-- sag
`-- dyn (dynamic free-breathing data)
|-- images (images)
| |-- cor
| `-- sag
`-- mfs_tpos2tpos (registration results)
|-- cor
`-- sag
Please, see our publication [1] for details on the acquisition sequence and registration used.
--
[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, Autoadaptive motion modelling for MR-based respiratory motion estimation, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.00
SUBJECT-SPECIFIC 4D ULTRASOUND LIVER MODEL AND DYNAMIC PHANTOM FOR ROBOTIC-ASSISTED LIVER BIOPSY STUDY
Ph.DDOCTOR OF PHILOSOPH
Applications of the golden angle in cardiovascular MRI
The use of radial trajectories has been seen as a potential solution to highly efficient
cardiovascular magnetic resonance imaging (MRI). By acquiring a broad
range of spatial frequencies per repetition time, the acquisition is time-efficient
and robust against motion. Of particular interest is the golden angle profile
order, which promises a near-uniform k-space coverage for an arbitrary number
of readouts, enabling flexible data resorting, which is critical for efficient
cardiovascular MRI.
In Study I the use of 2D golden angle profile ordering is explored for imaging
pulmonary embolisms. The insensitivity to motion and flow is used to reduce
the artifacts that otherwise degrade images of the pulmonary vasculature when
imaging with thin slices. It was found that the proposed technique could improve
the image quality. Another source of artifacts arises when gradients are
rapidly switched, and local induction of eddy currents may perturb spin equilibrium.
In Study II, we propose a generalized golden angle profile orderings
in 3D which reduces eddy-current artifacts. We demonstrate the efficacy of our
generalization through numerical simulations, phantom imaging and imaging of
a healthy volunteer. In Study III an improved 2D golden angle profile ordering
was explored which resulted in a higher degree of k-space uniformity after
physiological binning. This novel profile ordering was used in combination with
a phase-contrast readout to enable quantification of myocardial tissue velocity
and transmitral blood flow velocity, which are essential parameters for diastolic
function assessment. When compared to echocardiography, it was found that
MRI could accurately quantify myocardial tissue velocity, whereas transmitral
blood flow velocity was underestimated. Study IV explored a further development
of Study III by proposing a 3D version of the improved profile ordering.
This novel ordering was used to acquire whole-heart functional images during
free-breathing in less than one minute.
Together, these results indicate that golden-angle-based imaging has the potential
to improve cardiovascular MRI in several areas
Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease
Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges.
The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified.
The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing.
The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD.
The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD
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