Real-time automatic temperature regulation during in vivo MRI-guided laser-induced thermotherapy (MR-LITT).

Precise control of tissue temperature during Laser-Induced Thermotherapy (LITT) procedures has the potential to improve the clinical efficiency and safety of such minimally invasive therapies. We present a method to automatically regulate in vivo the temperature increase during LITT using real-time rapid volumetric Magnetic Resonance thermometry (8 slices acquired every second, with an in-plane resolution of 1.4 mmx1.4 mm and a slice thickness of 3 mm) using the proton-resonance frequency (PRF) shift technique. The laser output power is adjusted every second using a feedback control algorithm (proportional-integral-derivative controller) to force maximal tissue temperature in the targeted region to follow a predefined temperature-time profile. The root-mean-square of the difference between the target temperature and the measured temperature ranged between 0.5 °C and 1.4 °C, for temperature increases between + 5 °C to + 30 °C above body temperature and a long heating duration (up to 15 min), showing excellent accuracy and stability of the method. These results were obtained on a 1.5 T clinical MRI scanner, showing a potential immediate clinical application of such a temperature controller during MR-guided LITT.

Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences.

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.

Mean arterial pressure change associated with cerebral blood flow in healthy older adults.

We investigate over a 12-year period the association between regional cerebral blood flow (CBF) and cardiovascular risk factors in a prospective cohort of healthy older adults (81.96 ± 3.82 year-old) from the Cognitive REServe and Clinical ENDOphenotype (CRESCENDO) study. Cardiovascular risk factors were measured over 12 years, and gray matter CBF was measured at the end of the study from high-resolution magnetic resonance imaging using arterial spin labeling. The association between cardiovascular risk factors, their long-term change, and CBF was assessed using multivariate linear regression models. Women were observed to have higher CBF than men (p < 0.05). Increased mean arterial pressure (MAP) over the 12-year period was correlated with a low cerebral blood flow (p < 0.05, R(2) = 0.21), whereas no association was detected between CBF and MAP at the time of imaging. High levels of glycemia tended to be associated with low cerebral blood flow values (p < 0.05). Age, alcohol consumption, smoking status, body mass index, history of cardiovascular disease, and hypertension were not associated with CBF. Our main result suggests that change in MAP is the most significant predictor of future CBF in older adults.

A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.

Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.

Joint tumor segmentation and dense deformable registration of brain MR images.

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Graph based spatial position mapping of low-grade gliomas.

Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions.

Three-dimensional analysis of the intrinsic anatomy of the metatarsal bones.

Knowledge of the anatomy of the forefoot is important for understanding its mechanical pathology and developing specific surgical procedures. The aim of this study was to quantify 3-dimensional morphological parameters, which were proposed for the characterization of the metatarsal intrinsic anatomy. Thirty-five metatarsal bones prepared from 7 cadaver specimens were analyzed according to a new 3-dimensional computer-aided (CA) methodology. Manual and CA measurement techniques were compared. The reality of an intrinsic axial torsion of the metatarsals was underlined with mean values between 3.2 degrees and 57.7 degrees. Using the CA method, the reliability was excellent (intraclass correlation coefficient, 0.98) and significantly better than the manual method (P < .1E-12). With specific consideration of the second metatarsal intrinsic morphology, we emphasized its mechanical function. These results reflect the possibilities of CA systems. These data, which were carried out on specific anatomical characteristics of the metatarsal bones, can improve the metatarsalgia surgical procedures.