Four-Dimensional Imaging and Radiation Therapy: A Review of Challenges and Advancements in Clinical Practices

4-Dimensional Radiation therapy (4DRT) has been vastly developed in the past two decades. Motion management has become a vital part of high precision radiotherapy, wherein Stereotactic Body Radiation Therapy (SBRT) turns out to be a great boon for treating thoracic and abdominal tumours with confidence. In this review paper, we have analyzed the development of motion management strategies and the advancement of 4DRT.We have discussed the evolution of Internal target volume (ITV), 4D imaging techniques and the problems of breathing motion. In the second part, we have discussed various methods to tackle breathing motion. We also have reviewed the dosimetric aspects of 4D imaging and its clinical implications. In the last section, we have elaborated the 4D radiation therapy and recent advancements and practices.&nbsp

Iterative sorting for four-dimensional CT images based on internal anatomy motion

Current four-dimensional ͑4D͒ computed tomography ͑CT͒ imaging techniques using multislice CT scanners require retrospective sorting of the reconstructed two-dimensional ͑2D͒ CT images. Most existing sorting methods depend on externally monitored breathing signals recorded by extra instruments. External signals may not always accurately capture the breathing status and may lead to severe discontinuity artifacts in the sorted CT volumes. This article describes a method to find the temporal correspondences for the free-breathing multislice CT images acquired at different table positions based on internal anatomy movement. The algorithm iteratively sorts the CT images using estimated internal motion indices. It starts from two imperfect reference volumes obtained from the unsorted CT images; then, in each iteration, thorax motion is estimated from the reference volumes and the free-breathing CT images. Based on the estimated motion, the breathing indices as well as the reference volumes are refined and fed into the next iteration. The algorithm terminates when two successive iterations attain the same sorted reference volumes. In three out of five patient studies, our method attained comparable image quality with that using external breathing signals. For the other two patient studies, where the external signals poorly reflected the internal motion, the proposed method significantly improved the sorted 4D CT volumes, albeit with greater computation time

Improvements in four-dimensional and dual energy computed tomography

Dual energy and 4D computed tomography (CT) seek to address some of the limitations in traditional CT imaging. Dual energy CT, among other purposes, allows for the quantification and improved visualization of contrast materials, and 4D CT is often used in radiation therapy applications as it allows for the visualization and quantification of object motion. While much research has been done with these technologies, areas remain for potential improvement, both in preclinical and clinical settings, which will be explored in this dissertation. Preclinical dual energy cone-beam CT (CBCT) can benefit from wider separation between the peak energy of the two energy spectra. Using simulations and an x-ray source with a wide kVp range the contrast to noise ratio and Iodine concentration accuracy and precision were determined from Iodine material images. Improvements of 80% in CNR and 58% in precision were observed in the optimal energy pair of 60kVp/200kVp compared to a standard energy pair of 80kVp/140kVp. In 4D imaging, using projection data to obtain the required respiratory signal (“data driven”) can reduce setup complexity and cost of preclinical respiratory monitoring and reduce clinical 4D CT artifacts. Several clinical data driven 4D CBCT methods were modified for mice. Errors in projection sorting were within 4% of a breathing phase and were statistically less than the previous method for data driven 4D CBCT in mice. In clinical 4D CT, semi-automatically drawn target volumes and artifacts were compared between data driven and standard 4D CT images. Target volumes were shown to be statistically at least as large as standard contours, and artifacts were significantly reduced using the data driven technique. 4D CBCT is promising for use in evaluating tumor motion immediately prior to radiation treatment, but suffers from under sampling artifacts. An iterative volume of interest based reconstruction (I4D VOI) that aims to reduce artifacts without increases in computation time was compared to several other reconstruction techniques using a long scan patient data set. No statistical difference in tumor motion error was observed between I4D VOI and any of the other reconstruction methods. However, potential improvement over non-iterative VOI was demonstrated and computation time was reduced compared to TV minimization

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

Statistical shape modelling: automatic shape model building

Statistical Shape Models (SSM) have wide applications in image segmentation, surface registration and morphometry. This thesis deals with an important issue in SSM, which is establishing correspondence between a set of shape surfaces on either 2D or 3D. Current methods involve either manual annotation of the data (current ‘gold standard’); or establishing correspondences by using segmentation or registration algorithms; or using an information technique, Minimum Description Length (MDL), as an objective function that measures the utility of a model (the state-of-the-art). This thesis presents in principle another framework for establishing correspondences completely automatically by treating it as a learning process. Shannon theory is used extensively to develop an objective function, which measures the performance of a model along each eigenvector direction, and a proper weighting is automatically calculated for each energy component. Correspondence finding can then be treated as optimizing the objective function. An efficient optimization method is also incorporated by deriving the gradient of the cost function. Experimental results on various data are presented on both 2D and 3D. In the end, a quantitative evaluation between the proposed algorithm and MDL shows that the proposed model has better Generalization Ability, Specificity and similar Compactness. It also shows a good potential ability to solve the so-called “Pile Up” problem that exists in MDL. In terms of application, I used the proposed algorithm to help build a facial contour classifier. First, correspondence points across facial contours are found automatically and classifiers are trained by using the correspondence points found by the MDL, proposed method and direct human observer. These classification schemes are then used to perform gender prediction on facial contours. The final conclusion for the experiments is that MEM found correspondence points built classification scheme conveys a relatively more accurate gender prediction result. Although, we have explored the potential of our proposed method to some extent, this is not the end of the research for this topic. The future work is also clearly stated which includes more validations on various 3D datasets; discrimination analysis between normal and abnormal subjects could be the direct application for the proposed algorithm, extension to model-building using appearance information, etc

Lung deformation estimation and four-dimensional CT lung reconstruction

Abstract. Four-dimensional (4D) computed tomography (CT) image acquisition is a useful technique in radiation treatment planning and interventional radiology in that it can account for respiratory motion of lungs. Current 4D lung reconstruction techniques have limitations in either spatial or temporal resolution. In addition, most of these techniques rely on auxiliary surrogates to relate the time of CT scan to the patient’s respiratory phase. In this paper, we propose a novel 4D CT lung reconstruction and deformation estimation algorithm. Our algorithm is purely image based. The algorithm can reconstruct high quality 4D images even if the original images are acquired under irregular respiratory motion. The algorithm is validated using synthetic 4D lung data. Experimental results from a swine study data are also presented.

Apports de la microscopie biphotonique intravitale pulmonaire à l'étude de la physiopathologie de la maladie du charbon

Bacillus anthracis, l'agent infectieux responsable de la maladie du charbon, est un agent pathogène majeur du risque biologique provoqué, notamment en raison de la sévérité de la forme respiratoire de la maladie. Celle-ci résulte de l'inhalation de spores dont les mécanismes de pénétration au niveau pulmonaire sont mal connus à l'heure actuelle. Cette thèse présente les apports des microscopies confocale et biphotonique à l'étude de ces mécanismes de pénétration des spores inhalées. Le modèle murin CX3CR1+/gfp, dont la sous-population CD11b+ de cellules dendritiques (DCs) exprime constitutivement la protéine de fluorescence verte (GFP), a été utilisé dans ces travaux. Une première partie présente le développement d'une méthode automatisée de discrimination des DCs parmi d'autres populations cellulaires exprimant le même fluorophore, en se basant sur le calcul d'un coefficient morphologique. Cette méthode a permis d'étudier dans un deuxième temps le comportement spécifique de la sous-population de DCs CD11b, après infection par des spores de B. anthracis. L'étude microscopique a été d'abord effectuée in situ, c'est-à-dire sur des explants pulmonaires maintenus dans des conditions favorables à la préservation de l'activité cellulaire, puis in vivo, sur des souris anesthésiées et ventilées. Le protocole d'imagerie tire profit d'une stratégie d'acquisition et de traitement a posteriori des données permettant de surmonter, sans contrainte mécanique appliquée à l'organe, les problèmes de focalisation liés aux mouvements thoraciques durant la ventilation de l'animal. Cette stratégie originale utilise un sur-échantillonnage de l'acquisition et profite du signal de seconde harmonique généré par le collagène comme référence spatiale ; elle a permis l'observation in vivo d'interactions entre DCs et macrophages au niveau pulmonaire. Ces interactions, de type synapse immunologique, sont favorisées par l'infection et présentent donc un rôle fonctionnel qui reste à définir. La formation de synapses immunologiques entre macrophages et DCs pourrait non seulement représenter un chaînon manquant à l'explication de la pénétration des spores de B. anthracis au niveau pulmonaire, mais pourrait aussi constituer un enjeu crucial dans la compréhension de la réponse immunitaire associée aux infections pulmonaires.Bacillus anthracis, the causative agent of anthrax, is a major bioterrorism pathogen mainly because it can lead to a severe respiratory form of the disease. This form results from inhalation of spores, whose ways of entry into the lungs are not fully understood. This thesis reports the contribution of confocal and two-photon microscopy to the study of the penetration mechanisms of inhaled spores. The animal model utilized was CX3CR1+/gfp mouse, which constitutively expresses the green fluorescent protein (GFP) on CD11b+ dendritic cells (DCs). First, we present an automated method allowing discrimination of DCs among other GFP expressing cells, based on a morphologic coefficient. This method was then applied to the study of the specific behavior of CD11b DCs, after infection by B. anthracis spores. The microscopic study was first performed in situ, i.e. on explanted organs kept in conditions favorable to cell dynamics, then in vivo, i.e. on anesthetized and ventilated mice. In this case the imaging protocol profits from both acquisition and post-processing strategies, and allowed overcoming the focalization pitfalls coming from chest movements during ventilation. This novel strategy is based on an over-sampling of frame acquisition and utilizes second harmonic generation signal from alveolar collagen as a spatial reference. It led to the first ever in vivo observation of interactions between DCs and macrophages at the lung level. These immunological synapse-like structures are promoted by infection and thus display a functional role unknown until now. The formation of macrophages-DCs immunological synapses not only could represent a missing-link in figuring out the B. anthracis spore penetration mechanisms at the lung level, but more importantly could lead to a better understanding of the immune response associated with pulmonary infections.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF