Graphical 3D medical image registration and quantification

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

The importance of postoperative CT image fusion verification of stereotactic interstitial irradiation for brain tumors

PURPOSE

The aim of our study was verification of the position of implanted catheters with (125)I seeds after the catheter implantation for the brachytherapy of brain tumors.

METHODS AND MATERIALS

The fusion of the CT image used at planning and after the implantation of the catheters enabled us to verify the position of the catheters containing the isotopes. After this, the tumor volume covered by the prescribed dose (TV(PD)) and the normal tissue volume covered by the prescribed dose (NTV(PD)) were compared between the plan and the actual result. The image fusion was performed by the BrainLab-Target 1.19 software on an Alfa 430 (Digital) workstation.

RESULTS

The position of the catheters was adjusted in 14 (20%) of the 70 image fusion cases being studied. The position of 16 of the 116 catheters (13.8%) required adjustment after the fusion of control images in the 70 cases studied. The Student t probe revealed a significant difference between the TV(PD) values of the reality and the plans (75.8% vs. 92.4%, p < 0.0001). There was a significant difference between values of the real performances and planning for NTV(PD) (86.8% vs. 76%, p = 0.001) and for the conformity index (0.37 vs. 0.54, p = 0.0001), too.

CONCLUSION

The application of the interstitial irradiation with CT control allows us to identify and correct possible inaccuracies in catheter positioning during the operation. The procedure then becomes far more accurate and reliable, and as such, the irradiation becomes more conformal than without catheter adjustment.

A versatile functional-anatomic image fusion method for volume data sets

We describe and validate a volumetric three-dimensional registration method, and compare it to our previously validated two-dimensional/three-dimensional method. CT/MRI and SPECT data from 14 patients were interactively fused using a polynomial warping technique. Registration accuracy was confirmed visually and by a nonsignificant F value from multivariate analysis of the transformed landmarks, a significant difference of the squared sum of intensity differences between the transformed/untransformed and the reference volume both at the 0.05 (p > 0.05) confidence level and an average 31% improvement of the correlation coefficient and cross correlation. For the two-dimensional/three-dimensional method, ROI center-to-center distance ranged from 1.42 to 11.32 mm (for liver) with an average of 6.13 mm +/- 3.09 mm. The average ROI overlap was 92.51% with a 95% confidence interval of 90.20-96.88%. The new method is superior because it operates on the true three-dimensional volume. Both methods give good registration results, take 10 to 30 min, and require anatomic knowledge.