Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974

This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974

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

Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Diaphragm as an anatomic surrogate for lung tumor motion

Lung tumor motion due to respiration poses a challenge in the application of modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumor's motion and the surrogate's motion. The present paper presents an analysis of the correlation between the tumor motion and the diaphragm motion in order to evaluate the potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior-inferior lung tumor motion in 32 fluoroscopic image sequences from 10 lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8mm and an error at the 95% confidence level of 2.1mm. However, for one patient studied, the correlation is much weaker compared to other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.Comment: Accepted by Physics in Medicine and Biolog

Trapping and displacement of liquid collars and plugs in rough-walled tubes

A liquid film wetting the interior of a long circular cylinder redistributes under the action of surface tension to form annular collars or occlusive plugs. These equilibrium structures are invariant under axial translation within a perfectly smooth uniform tube and therefore can be displaced axially by very weak external forcing. We consider how this degeneracy is disrupted when the tube wall is rough, and determine threshold conditions under which collars or plugs resist displacement under forcing. Wall roughness is modelled as a non-axisymmetric Gaussian random field of prescribed correlation length and small variance, mimicking some of the geometric irregularities inherent in applications such as lung airways. The thin film coating this surface is modelled using lubrication theory. When the roughness is weak, we show how the locations of equilibrium collars and plugs can be identified in terms of the azimuthally averaged tube radius; we derive conditions specifying equilibrium collar locations under an externally imposed shear flow, and plug locations under an imposed pressure gradient. We use these results to determine the probability of external forcing being sufficient to displace a collar or plug from a rough-walled tube, when the tube roughness is defined only in statistical terms