An efficient strategy based on an individualized selection of registration methods. Application to the coregistration of MR and SPECT images in neuro-oncology.

An efficient registration strategy is described that aims to help solve delicate medical imaging registration problems. It consists of running several registration methods for each dataset and selecting the best one for each specific dataset, according to an evaluation criterion. Finally, the quality of the registration results, obtained with the best method, is visually scored by an expert as excellent, correct or poor. The strategy was applied to coregister Technetium-99m Sestamibi SPECT and MRI data in the framework of a follow-up protocol in patients with high grade gliomas receiving antiangiogenic therapy. To adapt the strategy to this clinical context, a robust semi-automatic evaluation criterion based on the physiological uptake of the Sestamibi tracer was defined. A panel of eighteen multimodal registration algorithms issued from BrainVisa, SPM or AIR software environments was systematically applied to the clinical database composed of sixty-two datasets. According to the expert visual validation, this new strategy provides 85% excellent registrations, 12% correct ones and only 3% poor ones. These results compare favorably to the ones obtained by the globally most efficient registration method over the whole database, for which only 61% of excellent registration results have been reported. Thus the registration strategy in its current implementation proves to be suitable for clinical application.

A framework using multimodal imaging for longitudinal monitoring of patients in neuro-oncology. Application to a SPECT/MRI study.

This paper proposes a framework to assess the potential value of 99mTc Sestamibi SPECT in addition to Gadolinium-enhanced MRI for the monitoring of patients with high grade gliomas under antiangiogenic treatment. It includes: 1) multimodal and monomodal high precision registration steps achieved thanks to a registration strategy which selects the best method among several ones for each dataset, 2) tumor segmentation steps dedicated to each modality and 3) a tumor comparison step which consists in the computation of some global (volume, intensity) and local (matching and mismatching) quantitative indices to analyze the tumor using different imaging modalities and at different times during the treatment. Each step is checked via 2D and 3D visualization. This framework was applied to a database of fifteen patients. For all patients, except one, the tumor volumes decrease globally and locally. Furthermore, a high correlation (r=0.77) was observed between MRI and Sestamibi tumor volumes. Finally, local indices show some possible mismatches between MRI Gadolinium uptake and Sestamibi uptake, which need to be further investigated.

A new strategy to improve coregistration of SPECT and MR images in patients with high grade glioma.

This paper proposes a new strategy to optimize the coregistration of Technetium-99m Sestamibi SPECT and MRI data in case of patients with high grade glioma. It consists in a personalized approach which selects, for each data set, the best registration method among several ones. To achieve this selection, a quantitative dedicated evaluation criterion based on the average intensities within specific anatomical structures corresponding to physiological areas of uptake of Sestamibi was defined. The strategy was applied to sixty-two data sets using nine registration methods based on mutual information and chamfer distance registration approaches, with different settings. It was implemented within the Anatomist/Brainvisa environment, using its basic registration functions. The visual evaluation by experts indicated that this strategy provides 60% good quality registrations, and 26% intermediate quality ones. Compared to the single use of the best global registration method, the number of registrations of good quality was multiplied by 1.4 when using the data specific strategy.