Search

Ce bref état de l’art en imagerie médicale est abordé en trois volets. Le premier concerne la chaîne à travers laquelle il faut l’appréhender : des capteurs à la reconstruction, de l’analyse à l’action. Cette chaîne relève en grande partie du traitement de l’information. Le second porte sur les différents principes physiques mis en œuvre (résonance magnétique, acoustique, optique, etc.) et couvre les principales modalités d’imagerie. Le dernier traite de la place de l’imagerie dans les procédures thérapeutiques et en particulier des nouvelles formes de thérapie physique. Cet article s’attache en même temps à donner les perspectives ouvertes par les recherches actuelles.

Projection incompleteness in x-ray computed tomography (CT) often relates to sparse sampling or detector gaps and leads to degraded reconstructions with severe streak and ring artifacts. To suppress these artifacts, this study develops a new sinogram inpainting strategy based on sinusoid-like curve decomposition and eigenvector-guided interpolation, where each missing sinogram point is considered located within a group of sinusoid-like curves and estimated from eigenvector-guided interpolation to preserve the sinogram texture continuity. The proposed approach is evaluated on real two-dimensional fan-beam CT data, for which the projection incompleteness, due to sparse sampling and symmetric detector gaps, is simulated. A Compute Unified Device Architecture (CUDA)-based parallelization is applied on the operations of sinusoid fittings and interpolations to accelerate the algorithm. A comparative study is then conducted to evaluate the proposed approach with two other inpainting methods and with a compressed sensing iterative reconstruction. Qualitative and quantitative performances demonstrate that the proposed approach can lead to efficient artifact suppression and less structure blurring.