Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

Automated Segmentation of the Pericardium Using a Feature Based Multi-atlas Approach

Multi-atlas segmentation is a widely used method that has proved to work well for the problem of segmenting organs in medical images. But standard methods are time consuming and the amount of data quickly grows to a point making use of these methods intractable. In this work we present a fully automatic method for segmentation of the pericardium in 3D CTA-images. We use a multi-atlas approach based on feature based registration (SURF) and use RANSAC to handle the large amount of outliers. The multi-atlas votes are fused by incorporating them into an MRF together with the intensity information of the target image and the optimal segmentation is found efficiently using graph cuts. We evaluate our method on a set of 10 CTA-volumes with manual expert delineation of the pericardium and we show that our method provides comparable results to a standard multi-atlas algorithm but at a large gain in computational efficiency

Exploiting Sparsity for Registration of Brain Tumor MR Images

[ANGLÈS] In medical imaging, sparsity has been used in the acquisition and reconstruction of MRI images, image denoising and face recognition among others. The aim of this thesis is to assess whether exploiting sparsity is a desirable property in the problem of brain tumor image registration. To this end, we consider tumor mass effect and tumor infiltration as two different tumor growing effects. In intensity-based nonrigid image registration, an optimization problem is defined by the minimization of a cost function with respect to the transformation parameters. This cost function consists of a dissimilarity term between the images being registered and a term that regularizes the transformation. Within this thesis, a modified L1 norm dissimilarity measure and a modified L1 regularization term are constructed. We compare the performance of different algorithms that combine these contributions with an L2 norm dissimilarity measure and diffusion regularizer for three different transformation models. Methods are tested on simulated brain tumor MR images and the validation of the registration is done by computing two dissimilarity distances between the deformation field obtained and a simulated ground truth. Results show that algorithms that use the modified L1 regularizer and a L2 dissimilarity measure recover the deformation of the tumor, while algorithms that use the modified L1 norm dissimilarity measure in some situations do not.[CASTELLÀ] En el procesado de imágenes médicas, se ha utilizado la 'sparsity' en la adquisición y la reconstrucción de imágenes MRI, en la eliminación del ruido en imágenes y en el reconocimiento facial, entre otros. El objetivo de la tesis es avaluar si el uso de la 'sparsity' es una propiedad deseada en el registro de imágenes de tumores cerebrales. Con esta finalidad, en esta tesis se consideran el efecto de masa tumoral i la infiltración de tumores como dos efectos diferentes del crecimiento del tumor. En el registro de imágenes no rígidas basadas en intensidad, un problema de optimización tiene que ser definido vía la minimización de una función de coste con respecto a los parámetros de la transformación. Esta función de coste consiste en un término de similitud entre las imágenes que se quieren registrar y un término adicional que regulariza la transformación. Dentro de esta tesis, se construyen un término de disimilitud norma L1-modificada y un término de regularización L1-modificada. Comparamos el rendimiento de diferentes algoritmos que combinan la contribución de estos dos términos, junto con el término de disimilitud norma L2 y el término de regularización de difusión, para tres posibles modelos de transformación diferentes. Los métodos se prueban en imágenes MR simuladas de tumores cerebrales y la validación de los registros es vía el cálculo de dos distancias de disimilitud entre el campo de deformación obtenido tras el registro y un 'ground truth' simulado. Los resultados muestran que los algoritmos que utilizan la regularización L1-modificada y la disimilitud norma L2 recuperan la transformación del tumor. Mientras que algoritmos que utilizan la norma L1-modificada como término de disimilitud tan solo lo hacen en situaciones puntuales.[CATALÀ] En el processament d'imatges mèdiques, s'ha utilitzat la 'sparsity' en l'adquisició i reconstrucció d'imatges MRI, en l'eliminació del soroll d'imatges i en el reconeixement de cares entre altres. L'objectiu d'aquesta tesi és avaluar si l'explotació de la 'sparsity' és una propietat desitjable en el registre d'imatges del tumor cerebral. Amb aquesta finalitat, en aquesta tesi es consideren l'efecte de massa tumoral i l'infiltració de tumor com a dos efectes de creixement de tumors diferents. En el registre d'imatges no rígides basades en l'intensitat, un problema d'optimització s'ha de definir via la minimització d'una funció de cost, amb respecte els paràmetres de la transformació. Aquesta funció de cost consisteix amb un terme de dissimilitud entre les imatges que són registrades, i un terme addicional que regularitza la transformació. Dins d'aquesta tesi, es construeixen el terme de dissimilitud norma L1-modificada i el terme de regularització L1-modificat. Comparem el rendiment de differents algoritmes que combinen la contribució d'aquests dos termes amb el terme de dissimilitud norma L2 i el terme de regularització de difusió, per a tres models de transformacions diferents. Els mètodes són provats amb imatges MR simulades de tumors cerebrals i la validació dels registres és via el càlcul de dues distàncies de dissimilitud entre el camp de deformació obtingut i un 'ground truth' simulat. Els resultats mostren que els algoritmes que utilitzen la regularització L1-modificat i la dissimilitud norma L2 recuperen la transformació del tumor. Mentre que algoritmes que utilitzen la norma L1-modificada com a mesura de dissimilitud tan sols ho fan en situacions puntuals

Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruc- tion of Breath-Hold Cardiac Cine MRI

Purpose: Compressed sensing methods with motion estimation and compensation techniques have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion from reconstructed images, especially at high acceleration factors. This work introduces a robust groupwise non-rigid motion estimation technique applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static sequence with highly sparse representation in temporally transformed domains. Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. Conclusions: Reconstruction methods based on groupwise registration show higher quality recon- structions for cardiac cine images than the pairwise counterparts tested

Multi-Atlas based Segmentation of Multi-Modal Brain Images

Brain image analysis is playing a fundamental role in clinical and population-based epidemiological studies. Several brain disorder studies involve quantitative interpretation of brain scans and particularly require accurate measurement and delineation of tissue volumes in the scans. Automatic segmentation methods have been proposed to provide reliability and accuracy of the labelling as well as performing an automated procedure. Taking advantage of prior information about the brain's anatomy provided by an atlas as a reference model can help simplify the labelling process. The segmentation in the atlas-based approach will be problematic if the atlas and the target image are not accurately aligned, or if the atlas does not appropriately represent the anatomical structure/region. The accuracy of the segmentation can be improved by utilising a group of atlases. Employing multiple atlases brings about considerable issues in segmenting a new subject's brain image. Registering multiple atlases to the target scan and fusing labels from registered atlases, for a population obtained from different modalities, are challenging tasks: image-intensity comparisons may no longer be valid, since image brightness can have highly diff ering meanings in dfferent modalities. The focus is on the problem of multi-modality and methods are designed and developed to deal with this issue specifically in image registration and label fusion. To deal with multi-modal image registration, two independent approaches are followed. First, a similarity measure is proposed based upon comparing the self-similarity of each of the images to be aligned. Second, two methods are proposed to reduce the multi-modal problem to a mono-modal one by constructing representations not relying on the image intensities. Structural representations work on the basis of using un-decimated complex wavelet representation in one method, and modified approach using entropy in the other one. To handle the cross-modality label fusion, a method is proposed to weight atlases based on atlas-target similarity. The atlas-target similarity is measured by scale-based comparison taking advantage of structural features captured from un-decimated complex wavelet coefficients. The proposed methods are assessed using the simulated and real brain data from computed tomography images and different modes of magnetic resonance images. Experimental results reflect the superiority of the proposed methods over the classical and state-of-the art methods

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup