Catálogo de publicaciones - libros

Significant research has been conducted in radiation beam gating technology to manage target and organ motions in radiotherapy treatment of cancer patients. As more and more on-board imagers are installed onto linear accelerators, fluoroscopic imaging becomes readily available at the radiation treatment stage. Thus, beam gating parameters, such as beam-on timing and beam-on window can be potentially determined by employing image registration between treatment planning CT images and fluoroscopic images. We propose a new registration method on deformable soft tissue between fluoroscopic images and DRR (Digitally Reconstructed Radiograph) images from planning CT images using active shape models. We present very promising results of our method applied to 30 clinical datasets. These preliminary results show that the method is very robust for the registration of deformable soft tissue. The proposed method can be used to determine beam-on timing and treatment window for radiation beam gating technology, and can potentially greatly improve radiation treatment quality.

Automatic classification of neurological tissues is a first step to many structural analysis pipelines. Most computational approaches are designed to extract the best possible classification results out of MR data acquired with standard clinical protocols. We observe that the characteristics of the latter owe more to the historical circumstances under which they were developed and the visual appreciation of the radiographer who acquires the images than to the optimality with which they can be classified with an automatic algorithm.

We submit that better performances could be obtained by considering the acquisition and analysis processes rather than optimising them independently. Here, we propose such a joint approach to MR tissue classification in the form of a fast MR sequence, which nulls the magnitude and changes the sign of the phase at the boundary between tissue types. A simple phase-based thresholding algorithm then suffices to segment the tissues. Preliminary results show promises to simplify and shorten the overall classification process.

We present a new functional magnetic resonance imaging (fMRI) analysis method that incorporates both spatial and temporal dynamics of blood-oxygen-level dependent (BOLD) signals within a region of interest (ROI). 3D moment descriptors are used to characterize the spatial changes in BOLD signals over time. The method is tested on fMRI data collected from eight healthy subjects performing a bulb-squeezing motor task with their right-hand at various frequencies. Multiple brain regions including the left cerebellum, both primary motor cortices (M1), both supplementary motor areas (SMA), left prefrontal cortex (PFC), and left anterior cingulate cortex (ACC) demonstrate significant task-related changes. Furthermore, our method is able to discriminate differences in activation patterns at the various task frequencies, whereas using a traditional intensity based method, no significant activation difference is detected. This suggests that temporal dynamics of the spatial distribution of BOLD signal provide additional information regarding task-related activation thus complementing conventional intensity-based approaches.

This paper describes a method for automatic detection of contractions in the small bowel through analyzing Wireless Capsule Endoscopic images. Based on the characteristics of contraction images, a coherent procedure that includes analyzes of the temporal and spatial features is proposed. For temporal features, the image sequence is examined to detect candidate contractions through the changing number of edges and an evaluation of similarities between the frames of each possible contraction to eliminate cases of low probability. For spatial features, descriptions of the directions at the edge pixels are used to determine contractions utilizing a classification method. The experimental results show the effectiveness of our method that can detect a total of 83% of cases. Thus, this is a feasible method for developing tools to assist in diagnostic procedures in the small bowel.

Computed tomography (CT) images of the lungs provide high resolution views of the airways. Quantitative measurements such as lumen diameter and wall thickness help diagnose and localize airway diseases, assist in surgical planning, and determine progress of treatment. Automated quantitative analysis of such images is needed due to the number of airways per patient. We present an approach involving dynamic programming coupled with boundary-specific cost functions that is capable of differentiating inner and outer borders. The method allows for precise delineation of the inner lumen and outer wall. The results are demonstrated on synthetic data, evaluated on human datasets compared to human operators, and verified on phantom CT scans to sub-voxel accuracy.

Dry Eye Syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. Nevertheless, there is still no objective test that provides reliable results. We have developed a new method for the automated detection of dry areas in videos taken after instilling fluorescein in the tear film. The method consists of a multi-step algorithm to first locate the iris in each image, then align the images and finally analyze the aligned sequence in order to find the regions of interest. Since the fluorescein spreads on the ocular surface of the eye the edges of the iris are fuzzy making the detection of the iris challenging. We use RANSAC to first detect the upper and lower eyelids and then the iris. Then we align the images by finding differences in intensities at different scales and using a least squares optimization method (Levenberg-Marquardt), to overcome the movement of the iris and the camera. The method has been tested on videos taken from different patients. It is demonstrated to find the dry areas accurately and to provide a measure of the extent of the disease.

Ultrasound Myocardial Elastography (UME) and Tagged Magnetic Resonance Imaging (tMRI) are two imaging modalities that were developed in the recent years to quantitatively estimate the myocardial deformations. Tagged MRI is currently considered as the gold standard for myocardial strain mapping in vivo. However, despite the low SNR nature of ultrasound signals, echocardiography enjoys the wides- pread availability in the clinic, as well as its low cost and high temporal resolution. Comparing the strain estimation performances of the two techniques has been of great interests to the community. In order to assess the cardiac deformation across different imaging modalities, in this paper, we developed a semi-automatic intensity and gradient based registration framework that rigidly registers the 3D tagged MRIs with the 2D ultrasound images. Based on the two registered modalities, we conducted spatially and temporally more detailed quantitative strain comparison of the RF-based UME technique and tagged MRI. From the experimental results, we conclude that qualitatively the two modalities share similar overall trends. But error and variations in UME accumulate over time. Quantitatively tMRI is more robust and accurate than UME.

A major challenge in contemporary magnetic resonance imaging (MRI) lies in providing the highest resolution exam possible in the shortest acquisition period. Recently, several authors have proposed the use of L-norm minimization for the reconstruction of sparse MR images from highly-undersampled k-space data. Despite promising results demonstrating the ability to accurately reconstruct images sampled at rates significantly below the Nyquist criterion, the extensive computational complexity associated with the existing framework limits its clinical practicality. In this work, we propose an alternative recovery framework based on homotopic approximation of the L-norm and extend the reconstruction problem to a multiscale formulation. In addition to several interesting theoretical properties, practical implementation of this technique effectively resorts to a simple iterative alternation between bilteral filtering and projection of the measured k-space sample set that can be computed in a matter of seconds on a standard PC.

Assessing the quality of motion estimation in the lung remains challenging. We approach the problem by imaging isolated porcine lungs within an artificial thorax with four-dimensional computed tomography (4DCT). Respiratory kinematics are estimated via pairwise non-rigid registration using different metrics and image resolutions. Landmarks are manually identified on the images and used to assess accuracy by comparing known displacements to the registration-derived displacements. We find that motion quantitation becomes less precise as the inflation interval between images increases. In addition, its sensitivity to image resolution varies anatomically. Mutual information and cross-correlation perform similarly, while mean squares is significantly poorer. However, none of the metrics compensate for the difficulty of registering over a large inflation interval. We intend to use the results of these experiments to more effectively and efficiently quantify pulmonary kinematics in future, and to explore additional parameter combinations.

Characterization and quantification of diffuse parenchymal lung disease (DPLD) severity using MDCT, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. This paper develops a 3D automated approach for detection and diagnosis of DPLDs (emphysema, fibrosis, honeycombing, ground glass).The proposed methodology combines multi-resolution image decomposition based on 3D morphological filtering, and graph-based classification for a full characterization of the parenchymal tissue. The very promising results obtained on a small patient database are good premises for a near implementation and validation of the proposed approach in clinical routine.