3D Brain SPECT imagery is a well established functional imaging method which has become a great help to physicians in the diagnosis of several neurological and cerebrovascular diseases. However, mainly due to the effects of attenuation and the scattering of emitted photons, inherent to this imaging process, 3D SPECT images are generally blurred and exhibit poor spatial resolution. This leads to substantial errors in measurements of regional brain blood flow, and therefore in the estimations of brain activity. In order to improve the resolution of these images and then to facilitate their interpretation, we herein propose an original extension of the NAS-RIF (Recursive Inverse Filtering) deconvolution technique proposed by Kundur and Hatzinakos [1]. The proposed extension allows to efficiently integrate, in the deconvolution process, a set of soft constraints given by a probabilistic MRI atlas containing experts's prior knowledge about the spatial localization of the different brain structures (or tissue classes). This extension has three interesting properties ; first it allows to exploit (or fuse) reliable anatomical and (high resolution) geometrical information extracted horn a probabilistic 3D MRI atlas. Second, it allows to incorporate, into the NAS-RIF method, a regularization term which efficiently stabilizes the inverse solution. Third and contrary to multi-modal restoration techniques, it does not require a MRI scan of the patient. This method has been successfully tested on numerous real brain SPECT images (of different patients suffering from epilepsy), yielding promising restoration results.

Nowadays, brain 3D SPECT is a well established functional imaging method that is widely used in clinical settings for the assessment of neurological and cerebrovascular diseases. However, due to the scattering of the emitted photons, inherent to this imaging process, brain 3D SPECT images exhibit poor spatial and inter-slice resolution. More precisely, SPECT images are blurred, leading to substantial errors in measurement of regional brain activity and making difficult and subjective, a reliable and accurate diagnosis by the nuclear physician. In order to improve the resolution of these images and then to facilitate their interpretation, we herein propose an original extension of the NAS-RIF deconvolution technique of Kundur and Hatzinakos. The proposed extension has two interesting properties; it allows to exploit or fuse anatomical and geometrical information extracted from a high resolution anatomical magnetic resonance (MR) image and also to efficiently incorporate, into the NAS-RIF method, a regularization term to stabilize the inverse solution. In our application, this anatomical-based regularization term exploits the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MR and SPECT data volume coming from a same patient. This method has been successfully tested on numerous pairs of brain MR and SPECT images of different patients, yielding very promising restoration results.

In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the stochastic version of the expectation maximization (SEM) algorithm. Parameters of this mixture model are herein used to constrain the 3D reconstruction problem of scoliotic rib cage from a pair of planar and conventional calibrated radiographic images (postero-anterior with normal incidence (I/sub PA/) and lateral (I/sub LAT/)). More precisely, the proposed PPCA mixture model is herein robustly exploited for dimensionality reduction and to get a set of probabilistic prior models associated to each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood and for each considered class-conditional prior model, the proposed 3D reconstruction is stated as an energy function minimization problem, which is solved with a stochastic optimization algorithm. The optimal 3D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3D method of reconstruction has been successfully tested on several biplanar radiographic images, yielding very promising results.

In this paper, we propose a new and accurate 3D reconstruction technique for the scoliotic spine from a pair planar and conventional radiographic images (postero-anterior and lateral). The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3D templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. 3D reconstruction is then refined for each vertebra, by a template on which nonlinear admissible global deformations are modeled, with statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. This unsupervised coarse-to-fine 3D reconstruction procedure is stated as a double energy function minimization problems efficiently solved with a stochastic optimization algorithm. The proposed method, tested on several pairs of biplanar radiographic images with scoliotic deformities, is comparable in terms of accuracy with the classical CT-scan technique while being unsupervised and requiring a lower amount of radiation for the patient.

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.