Diffusion weighted MRI as an early predictor of tumor response to hypofractionated stereotactic boost for prostate cancer.

We evaluated the feasibility of using the kinetic of diffusion-weighted MRI (DWI) and the normalized apparent coefficient diffusion (ADC) map value as an early biomarker in patients treated by external beam radiotherapy (EBRT). Twelve patients were included within the frame of a multicenter phase II trial and treated for intermediate risk prostate cancer (PCa). Multiparametric MRI was performed before treatment (M0) and every 6 months until M24. Association between nADC and PSA or PSA kinetic was evaluated using the test of nullity of the Spearman correlation coefficient. The median rates of PSA at the time of diagnosis, two years and four years after EBRT were 9.29 ng/ml (range from 5.26 to 17.67), 0.68 ng/ml (0.07-2.7), 0.47 ng/ml (0.09-1.39), respectively. Median nADC increased from 1.14 × 10-3 mm2/s to 1.59 × 10-3 mm2/s between M0 and M24. Only one patient presented a decrease of nADC (1.35 × 10-3 mm2/s and 1.11 × 10-3 mm2/s at M0 and M12 respectively). The increase in nADC at M6 was correlated with PSA decrease at M18, M24 and M30 (p < 0.05). The increase in nADc at M12 was correlated with PSA decrease at M36 (p = 0.019). Early nADC variation were correlated with late PSA decrease for patients with PCa treated by EBRT.

Evidential Matrix Metrics as Distances Between Meta-Data Dependent Bodies of Evidence.

As part of the theory of belief functions, we address the problem of appraising the similarity between bodies of evidence in a relevant way using metrics. Such metrics are called evidential distances and must be computed from mathematical objects depicting the information inside bodies of evidence. Specialization matrices are such objects and, therefore, an evidential distance can be obtained by computing the norm of the difference of these matrices. Any matrix norm can be thus used to define a full metric. In this paper, we show that other matrices can be used to obtain new evidential distances. These are the α -specialization and α -generalization matrices and are closely related to the α -junctive combination rules. We prove that any L(1) norm-based distance thus defined is consistent with its corresponding α -junction. If α > 0 , these distances have in addition relevant variations induced by the poset structure of the belief function domain. Furthermore, α -junctions are meta-data dependent combination rules. The meta-data involved in α -junctions deals with the truthfulness of information sources. Consequently, the behavior of such evidential distances is analyzed in situations involving uncertain or partial meta-knowledge about information source truthfulness.

A new 3D-matching method of nonrigid and partially similar models using curve analysis.

The 3D-shape matching problem plays a crucial role in many applications, such as indexing or modeling, by example. Here, we present a novel approach to matching 3D objects in the presence of nonrigid transformation and partially similar models. In this paper, we use the representation of surfaces by 3D curves extracted around feature points. Indeed, surfaces are represented with a collection of closed curves, and tools from shape analysis of curves are applied to analyze and to compare curves. The belief functions are used to define a global distance between 3D objects. The experimental results obtained on the TOSCA and the SHREC07 data sets show that the system performs efficiently in retrieving similar 3D models.