Vessel-based fast deformable registration with minimal strain energy

Multi-atlas cardiac PET segmentation

PURPOSE

We propose a multi-atlas based segmentation method for cardiac PET and SPECT images to deal with the high variability of tracer uptake characteristics in myocardium. In addition, we verify its performance by comparing it to the manual segmentation and single-atlas based approach, using dynamic myocardial PET.

METHODS

Twelve left coronary artery ligated SD rats underwent ([¹⁸F]fluoropentyl) triphenylphosphonium salt PET/CT scans. Atlas-based segmentation is based on the spatial normalized template with pre-defined region-of-interest (ROI) for each anatomical or functional structure. To generate multiple left ventricular (LV) atlases, each LV image was segmented manually and divided into angular segments. The segmentation methods performances were compared in regional count information using leave-one-out cross-validation. Additionally, the polar-maps of kinetic parameters were estimated.

RESULTS

In all images, the highest r² template yielded the lowest root-mean-square error (RMSE) between the source image and the best-matching templates ranged between 0.91-0.97 and 0.06-0.11, respectively. The single-atlas and multi-atlas based ROIs yielded remarkably different perfusion distributions: only the multi-atlas based segmentation showed equivalent high correlation results (r² = 0.92) with the manual segmentation compared with the single-atlas based (r² = 0.88). The high perfusion value underestimation was remarkable in single-atlas based segmentation.

CONCLUSIONS

The main advantage of the proposed multi-atlas based cardiac segmentation method is that it does not require any prior information on the tracer distribution to be incorporated into the image segmentation algorithms. Therefore, the same procedure suggested here is applicable to any other cardiac PET or SPECT imaging agents without modification.

Automatic error correction using adaptive weighting for vessel-based deformable image registration

In this paper, we extend our previous work on deformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embedded tumors, which is common in clinical applications. It is a very challenging task since the registration method that works for homogenous tissues may not work well with inhomogenous tissues. The maximum error normally occurs in the regions with tumors and often exceeds the acceptable error threshold. In this paper, we propose a new error correction method with adaptive weighting to reduce the maximum registration error. Our previous fast deformable registration method is used in the inner loop. We have also proposed a new evaluation metric average error of deformation field (AEDF) to evaluate the registration accuracy in regions between vessels and bifurcation points. We have validated the proposed method using liver MR images from human subjects. AEDF results show that the proposed method can greatly reduce the maximum registration errors when compared with the previous method with no adaptive weighting. The proposed method has the potential to be used in clinical applications to reduce registration errors in regions with tumors.