Jean-Michel Rouet

Efficient model-based quantification of left ventricular function in 3-D echocardiography

Quantitative functional analysis of the left ventricle plays a very important role in the diagnosis of heart diseases. While in standard two-dimensional echocardiography this quantification is limited to rather crude volume estimation, three-dimensional (3-D) echocardiography not only significantly improves its accuracy but also makes it possible to derive valuable additional information, like various wall-motion measurements. In this paper, we present a new efficient method for the functional evaluation of the left ventricle from 3-D echographic sequences. It comprises a segmentation step that is based on the integration of 3-D deformable surfaces and a four-dimensional statistical heart motion model. The segmentation results in an accurate 3-D + time left ventricle discrete representation. Functional descriptors like local wall-motion indexes are automatically derived from this representation. The method has been successfully tested both on electrocardiography-gated and real-time 3-D data. It has proven to be fast, accurate, and robust.

Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.

Diagnosing breast cancer using independent diffuse optical tomography and x-ray mammography scans

We have previously demonstrated the utilization of spatially co-registered diffuse optical tomography (DOT) and digital breast tomosynthesis (DBT) for joint breast cancer diagnosis. However, clinical implementation of such a multi-modality approach may require development of integrated DOT/DBT imaging scanners, which can be costly and time-consuming. Exploring effective image registration methods that combine the diagnostic information from a standalone DOT measurement and a separate mammogram can be a cost-effective solution, which may eventually enable adding functional optical assessment to all previously installed digital mammography systems. In this study, we investigate a contour-based image registration method to convert independent optical and x-ray scans into co-registered datasets that can benefit from a joint image analysis. The breast surface used in 3D optical DOT reconstruction is registered with the breast contour line extracted from an x-ray mammogram acquired separately. This allows us to map the 2D mammogram to the optical measurement space and build structural constraints for optical image reconstruction. A non-linear reconstruction utilizing structure-priors is then performed to produce hemoglobin maps with improved resolution. To validate this approach, we used a set of tumor patient measurements with simultaneous DOT/DBT and separate 2D mammographic scans. The images recovered from the registration procedure derived from DOT and 2D mammogram present similar image quality compared to those recovered from co-registered DOT/DBT measurements.

Impact of respiratory motion correction on the detection of small lesions in whole-body PET imaging: A simulation study

Respiratory motion in Positron Emission Tomography leads to reduced image quality, influencing this way the quantitative accuracy of PET measurements, as shown in numerous studies. However, only few results have been published on its impact on lesion detection. This study intends to evaluate the impact of motion correction on the detection of small lesions (between 8 and 12 mm diameter) using a Computed-Aided Detection (CAD) system on FDG whole-body simulated PET images. We evaluate two types of motion correction techniques, both using motion fields derived from the reconstruction of gated PET images. The first technique consists in averaging the coregistered gated reconstructed PET images, while the second method integrates the motion fields during the iterative reconstruction process.

Semi-automatic abdominal aortic aneurysms geometry assessment based on 3D ultrasound

This paper presents a new approach for improving the surveillance of the size of abdominal aortic aneurysms (AAA). Use of 3D ultrasound imaging combined with semi-automatic quantification provides automatic selection of the optimal plane for diameter measurement. Quantification parameters are defined to characterize the aneurysm with more accuracy. Volume imaging also provides 3D visualization of the AAA geometry and CT-like multi-planar reconstructions. Multiple volume registrations are proposed to overcome limited field of view issues. Quantification results show good correlation with 2D reference measurements and obtained Pearson correlation coefficients are significant for 30 patients.

Characterizing breast lesions through robust multimodal data fusion using independent diffuse optical and x-ray breast imaging.

To enable tissue function-based tumor diagnosis over the large number of existing digital mammography systems worldwide, we propose a cost-effective and robust approach to incorporate tomographic optical tissue characterization with separately acquired digital mammograms. Using a flexible contour-based registration algorithm, we were able to incorporate an independently measured two-dimensional x-ray mammogram as structural priors in a joint optical/x-ray image reconstruction, resulting in improved spatial details in the optical images and robust optical property estimation. We validated this approach with a retrospective clinical study of 67 patients, including 30 malignant and 37 benign cases, and demonstrated that the proposed approach can help to distinguish malignant from solid benign lesions and fibroglandular tissues, with a performance comparable to the approach using spatially coregistered optical/x-ray measurements.

Incorporating patient-specific variability in the oncoPET_DB database

The purpose of this study was to generate a series of simulated 18F-FDG PET oncology images incorporating variability of patient anatomy and 18F-FDG uptake in the main organs as well as realistic respiratory motion. These data are based on the XCAT model adapted to a series of clinical CT data and on patient respiratory cycles derived from spirometry records. Spherical 8 and 12 mm diameter lesions of homogeneous uptake were randomly distributed in the lungs and the liver, which are both significantly impacted by respiratory motion. Five different lesion contrasts per diameter and per organ were sampled based on a human observer detection study so as to cover the entire range of detectability. The simulated database was generated with the PET-SORTEO Monte-Carlo simulation tool that was fully validated against the geometry of the GEMINI system (Philips Healthcare, Cleveland, OH). The simulated database is actually composed of 15 whole body PET simulated images with a total of 107 tumors in the liver and 173 tumors in the lungs. These data will complete the first series of images of the oncoPET_DB database (www.creatis.insa-lyon.fr/oncoPET_DB) by providing the raw list-mode files of the 15 models as well as four sets of reconstruction per model. These four series include the extreme cases of respiration-free (non breathing) and free-respiration (no correction) images and two respiratory motion corrected images based on the estimation of elastic transformation fields applied either on the reconstruction system matrix, during reconstruction, or a posteriori, on the reconstructed PET gated images. Perspectives include performing a human FROC study on the 15 images to validate the lesion contrast calibration.