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

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

© 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

SUBJECT-SPECIFIC 4D ULTRASOUND LIVER MODEL AND DYNAMIC PHANTOM FOR ROBOTIC-ASSISTED LIVER BIOPSY STUDY

Ph.DDOCTOR OF PHILOSOPH

Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) 'motion-free' gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice

Assessing and Improving 4D-CT Imaging for Radiotherapy Applications

Lung cancer has both a high incidence and death rate. A contributing factor to these high rates comes from the difficulty of treating lung cancers due to the inherent mobility of the lung tissue and the tumour. 4D-CT imaging has been developed to image lung tumours as they move during respiration. Most 4D-CT imaging methods rely on data from an external respiratory surrogate to sort the images according to respiratory phase. However, it has been shown that respiratory surrogate 4D-CT methods can suffer from imaging artifacts that degrade the image quality of the 4D-CT volumes that are used to plan a patient\u27s radiation therapy. In Chapter 2 of this thesis a method to investigate the correlation between an external respiratory surrogate and the internal anatomy was developed. The studies were performed on ventilated pigs with an induced inconsistent amplitude of breathing. The effect of inconsistent breathing on the correlation between the external marker and the internal anatomy was tested using a linear regression. It was found in 10 of the 12 studies performed that there were significant changes in the slope of the regression line as a result of inconsistent breathing. From this study we conclude that the relationship between an external marker and the internal anatomy is not stable and can be perturbed by inconsistent breathing amplitudes. Chapter 3 describes the development of a image based 4D-CT imaging algorithm based on the concept of normalized cross correlation (NCC) between images. The volumes produced by the image based algorithm were compared to volumes produced using a clinical external marker 4D-CT algorithm. The image based method produced 4D-CT volumes that had a reduced number of imaging artifacts when compared to the external marker produced volumes. It was shown that an image based 4D-CT method could be developed and perform as well or better than external marker methods that are currently in clinical use. In Chapter 4 a method was developed to assess the uncertainties of the locations of anatomical structures in the volumes produced by the image based 4D-CT algorithm developed in Chapter 3. The uncertainties introduced by using NCC to match a pair of images according to respiratory phase were modeled and experimentally determined. Additionally, the assumption that two subvolumes could be matched in respiratory phase using a single pair of 2D overlapping images was experimentally validated. It was shown that when the image based 4D-CT algorithm developed in Chapter 3 was applied to data acquired from a ventilated pig with induced inconsistent breathing the displacement uncertainties were on the order of 1.0 millimeter. The results of this thesis show that there exists the possibility of a miscorrelation between the motion of a respiratory surrogate (marker) and the internal anatomy under inconsistent breathing amplitude. Additionally, it was shown that an image based 4D-CT method that operates without the need of one or more external respiratory surrogate(s) could produce artifact free volumes synchronous with respiratory phase. The spatial uncertainties of the volumes produced by the image based 4D-CT method were quantified and shown to be small (~ 1mm) which is an acceptable accuracy for radiation treatment planning. The elimination of the external respiratory surrogates simplifies the implementation and increases the throughput of the image based 4D-CT method as well

Shape-correlated statistical modeling and analysis for respiratory motion estimation

Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Effective and accurate respiration estimation is crucial to account for the motion effects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause difficulties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere. Learning-based linear motion modeling techniques have the advantage of incorporating prior knowledge for robust motion estimation. In this research shape-correlation deformation statistics (SCDS) capture strong correlations between the shape of the lung and the dense deformation field under breathing. Dimension reduction and linear regression techniques are used to extract the correlation statistics. Based on the assumption that the deformation correlations are consistent between planning and treatment time, patient-specific SCDS trained from a 4D planning image sequence is used to predict the respiratory motion in the patient's artifact-laden 4D treatment image sequence. Furthermore, a prediction-driven atlas formation method is developed to weaken the consistency assumption, by integrating intensity information from the target images and the SCDS predictions into a common optimization framework. The strategy of balancing between the prediction constraints and the intensity-matching forces makes the method less sensitive to variation in the correlation and utilizes intensity information besides the lung boundaries. This strategy thus provides improved motion estimation accuracy and robustness. The SCDS-based methods are shown to be effective in modeling and estimating respiratory motion in lung, with evaluations and comparisons carried out on both simulated images and patient images