Fusion of autoradiographies with an MR volume using 2-D and 3-D linear transformations

In the past years, the development of 3-D medical imaging devices has given access to the 3-D imaging of in vivo tissues, from an anatomical (MR, CT) or even functional point of view (fMRI, PET, SPECT). However, despite huge technological progress, the resolution of these images is still not sufficient to image to anatomical or functional details, that can only be revealed by in vitro imaging (e.g. histology, autoradiography), eventually enhanced by staining. The deep motivation of this work is the comparison of activations detected by fMRI series analysis to the ones that can be observed in autoradiographic images. The aim of the presented work is to fuse the autoradiographic data with the pre-mortem anatomical MR image, to facilitate the above mentioned comparison. First, we reconstruct a 3-D volume, coherent both in geometry and intensity, from the 2-D autoradiographic sections. Second, this volume is fused with the MR image, that allows to geometrically correct the reconstruction to make it comparable to the MR image. We show that this fusion can be achieved by using only simple global transformations (rigid and/or affine, 2-D and 3-D), yielding a very satisfactory result

Intra-operative Registration for Stereotactic Procedures driven by a combined Biomechanical Brain and CSF Model

International audienceDuring stereotactic neurosurgery, the brain shift could affect the accuracy of the procedure. However, this deformation of the brain is not often considered in the pre-operative planning step or intra-operatively, and may lead to surgical complications, side effects or ineffectiveness. In this paper, we present a method to update the pre-operative planning based on a physical simulation of the brain shift. Because the simulation requires unknown input parameters, the method relies on a parameter estimation process to compute the intracranial state that matches the partial data taken from intra-operative modalities. The simulation is based on a biomechanical model of the brain and the cerebro-spinal fluid. In this paper, we show on an anatomical atlas that the method is numerically sound

Automated Piecewise Affine Registration of Biological Images

This report tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or different modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Our clustering algorithm relies on the Earth mover's distribution metric and is additionally guided by robust least-square estimation of the transformations associated with each cluster. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using simulated and real medical images, including MRI, autoradiography, histology and cryosection data. We also detail the reconstruction of a 3-D volume from a series of 2-D histological sections and compare it against a reconstruction obtained with a global rigid approach

Une nouvelle méthode de recalage en imagerie cérébrale adaptée à la cartographie individuelle des ganglions de la base par déformation d atlas anatomiques

ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Lesions in deep gray nuclei after severe traumatic brain injury predict neurologic outcome

International audiencePURPOSE: This study evaluates the correlation between injuries to deep gray matter nuclei, as quantitated by lesions in these nuclei on MR T2 Fast Spin Echo (T2 FSE) images, with 6-month neurological outcome after severe traumatic brain injury (TBI). MATERIALS AND METHODS: Ninety-five patients (80 males, mean age = 36.7y) with severe TBI were prospectively enrolled. All patients underwent a MR scan within the 45 days after the trauma that included a T2 FSE acquisition. A 3D deformable atlas of the deep gray matter was registered to this sequence; deep gray matter lesions (DGML) were evaluated using a semi-quantitative classification scheme. The 6-month outcome was dichotomized into unfavorable (death, vegetative or minimally conscious state) or favorable (minimal or no neurologic deficit) outcome. RESULTS: Sixty-six percent of the patients (63/95) had both satisfactory registration of the 3D atlas on T2 FSE and available clinical follow-up. Patients without DGML had an 89% chance (P = 0.0016) of favorable outcome while those with bilateral DGML had an 80% risk of unfavorable outcome (P = 0.00008). Multivariate analysis based on DGML accurately classified patients with unfavorable neurological outcome in 90.5% of the cases. CONCLUSION: Lesions in deep gray matter nuclei may predict long-term outcome after severe TBI with high sensitivity and specificity

Demographic characteristics of patients involved in our study.

<p>Demographic characteristics of patients involved in our study.</p

DGML semi-quantitative scale.

<p><b>A</b>. No lesion; <b>B</b>. Punctuate lesion; <b>C</b>. Lesion involving less than 1/3^rd of the nucleus. <b>D</b>. Lesion involving between 1/3^rd and 2/3^rd of the nucleus. <b>E</b>. Lesion of more than 2/3^rd of the nucleus. <b>F</b>. Total involvement of the nucleus.</p

Cross validation (Jacknife test).

<p>In each validation test, we used data from all but one subject (S -1 of the S subjects) to train the classifier.</p