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To improve non-invasively the specificity in the diagnosis of breast cancer after a positive screening mammography or doubt/suspicious ultrasound examination, the SOLUS project developed a multimodal imaging system that combines: Bmode ultrasound (US) scans (to assess morphology), Color Doppler (to visualize vascularization), shear-wave elastography (to measure stiffness), and time domain multi-wavelength diffuse optical tomography (to estimate tissue composition in terms of oxy- and deoxy-hemoglobin, lipid, water, and collagen concentrations). The multimodal probe arranges 8 innovative photonic modules (optodes) around the US transducer, providing capability for optical tomographic reconstruction. For more accurate estimate of lesion composition, US-assessed morphological priors can be used to guide the optical reconstructions. Each optode comprises: i) 8 picosecond pulsed laser diodes with different wavelengths, covering a wide spectral range (635-1064 nm) for good probing of the different tissue constituents; ii) a large-area (variable, up to 8.6 mm2 ) fast-gated digital Silicon Photomultiplier; iii) the acquisition electronics to record the distribution of time-of-flight of the re-emitted photons. The optode is the basic element of the optical part of the system, but is also a stand-alone, ultra-compact (about 4 cm3 ) device for time domain multi-wavelength diffuse optics, with potential application in various fields.

Blood oxygenation in depth is a critical parameter to be monitored in many clinical applications. We are developing a pre-clinical instrument to monitor oxygenation up to 1.5 cm deep in biological tissues. The measures, based on time of flight, are in vivo, noninvasive and without injection of any contrast agent. Developments have been implemented in a tomographic reconstruction algorithm to make it reconstruct chromophores by layers. These developments are presented here along with simulations we used to set up the algorithm. Buried flaps on 20 pigs were undergone for pre-clinical tests of our reconstruction algorithm and instrument.

With the emergence of photonics, new technologies and fast computed methods, optical imaging becomes a promising tool for medical applications. We focus here our interest on prostate cancer which is the most common one in the male population and which suffers from a lack of clinical imaging techniques. Hence, the current approach to localize tumors inside the organ relies on an excessive use of biopsies to sample the prostate until reaching the suspicious region. This process is obviously invasive for the patients and requires often to be repeated. Some methods based on diffuse optics tomography give encouraging results [1] but still require an acquisition duration not compatible with a clinical intervention. For faster localization of the tumor, we propose to use time resolved fluorescent tomography preceded by an injection of a fluorescent marker to the patient. The goal of our approach is to perform a single biopsy, by guiding it towards the identified fluorescent region. The detection is coupled with ultrasound imaging which is a current and convenient technique in interventional radiology.

With an optimized Time-Resolved acquisition system and probe, we detected and identified both arterial and venous occlusions and recoveries of deep buried flaps. Our results were cross-validated with a PtO2 clinical LICOX invasive reference.

Developable surfaces have been extensively studied in computer graphics because they are involved in a large body of applications. This type of surfaces has also been used in computer vision and document processing in the context of three-dimensional (3D) reconstruction for book digitization and augmented reality. Indeed, the shape of a smoothly deformed piece of paper can be very well modeled by a developable surface. Most of the existing developable surface parameterizations do not handle boundaries or are driven by overly large parameter sets. These two characteristics become issues in the context of developable surface reconstruction from real observations. Our main contribution is a generative model of bounded developable surfaces that solves these two issues. Our model is governed by intuitive parameters whose number depends on the actual deformation and including the “flat shape boundary”. A vast majority of the existing image-based paper 3D reconstruction methods either require a tightly controlled environment or restricts the set of possible deformations. We propose an algorithm for reconstructing our model's parameters from a general smooth 3D surface interpolating a sparse cloud of 3D points. The latter is assumed to be reconstructed from images of a static piece of paper or any other developable surface. Our 3D reconstruction method is well adapted to the use of keypoint matches over multiple images. In this context, the initial 3D point cloud is reconstructed by structure-from-motion for which mature and reliable algorithms now exist and the thin-plate spline is used as a general smooth surface model. After initialization, our model's parameters are refined with model-based bundle adjustment. We experimentally validated our model and 3D reconstruction algorithm for shape capture and augmented reality on seven real datasets. The first six datasets consist of multiple images or videos and a sparse set of 3D points obtained by structure-from-motion. The last dataset is a dense 3D point cloud acquired by structured light. Our implementation has been made publicly available on the authors' web home pages. Copyright © 2012 John Wiley & Sons, Ltd.

We present a monocular 3D reconstruction algorithm for inextensible deformable surfaces. It uses point correspondences between a single image of the deformed surface taken by a camera with known intrinsic parameters and a template. The main assumption we make is that the surface shape as seen in the template is known. Since the surface is inextensible, its deformations are isometric to the template. We exploit the distance preservation constraints to recover the 3D surface shape as seen in the image. Though the distance preservation constraints have already been investigated in the literature, we propose a new way to handle them. Spatial smoothness priors are easily incorporated, as well as temporal smoothness priors in the case of reconstruction from a video. The reconstruction can be used for 3D augmented reality purposes thanks to a fast implementation. We report results on synthetic and real data. Some of them are compared to stereo-based 3D reconstructions to demonstrate the efficiency of our method.

International audience; Thin-Plate Spline warps have been shown to be very effective as a parameterized model of the optic flow field between images of various deforming surfaces. Examples include a sheet of paper being manually handled. Recent work has used such warps for images of smooth rigid surfaces. Standard Thin-Plate Spline warps are not rigid, in the sense that they do not satisfy the epipolar geometry constraint, and are intrinsically affine, in the sense of the affine camera model. We propose three types of warps based on the Thin-Plate Spline. The first one is a flexible rigid warp. It describes the optic flow field induced by a smooth rigid surface, and satisfies the affine epipolar geometry constraint. The second and third ones extend the standard Thin-Plate Spline and the proposed rigid flexible warp to the perspective camera model. The properties of these warps are studied in details, and a hierarchy is defined. Experimental results on simulated and real data are reported, showing that the proposed warps outperform the standard one in several cases of interest.

Smoothly bent paper-like surfaces are developable. They are however difficult to minimally parameterize since the number of meaningful parameters is intrinsically dependent on the actual deformation. Previous generative models are either incomplete, i.e. limited to subsets of developable surfaces, or depend on huge parameter sets. We propose a generative model governed by a quasi-minimal set of intuitive parameters, namely rules and angles. More precisely, a flat mesh is bent along guiding rules, while a number of extra rules controls the level of smoothness. The generated surface is guaranteed to be developable. A fully automatic multi-camera three dimensional reconstruction algorithm, including model-based bundle-adjustment, demonstrates our model on real images.

Registering images of a deforming surface is a well-studied problem. It is common practice to describe the image deformation fields with Thin-Plate Splines. This has the advantage to involve small numbers of parameters, but has the draw-back that the 3D surface is not explicitly reconstructed. We propose an image deformation model combining Thin-Plate Splines with 3D entities ? a 3D control mesh and a camera ? overcoming the above mentioned drawback. An original solution to the non-rigid image registration problem using this model is proposed and demonstrated on simulated and real data.