Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelet

This work is to investigate the feasibility of improving megavoltage imaging quality for TomoTherapy using a novel reconstruction technique based on tensor framelet, with either full-view or partial-view data.

The reconstruction problem is formulated as a least-square L1-type optimization problem, with the tensor framelet for the image regularization, which is a generalization of L1, total variation, and wavelet. The high-order derivatives of the image are simultaneously regularized in L1 norm at multilevel along the x, y, and z directions. This convex formulation is efficiently solved using the Split Bregman method. In addition, a GPU-based parallel algorithm was developed to accelerate image reconstruction. The new method was compared with the filtered backprojection and the total variation based method in both phantom and patient studies with full or partial projection views.

The tensor framelet based method improved the image quality from the filtered backprojection and the total variation based method. The new method was robust when only 25% of the projection views were used. It required ∼2 min for the GPU-based solver to reconstruct a 40-slice 1 mm-resolution 350×350 3D image with 200 projection views per slice and 528 detection pixels per view.

The authors have developed a GPU-based tensor framelet reconstruction method with improved image quality for the megavoltage CT imaging on TomoTherapy with full or undersampled projection views. In particular, the phantom and patient studies suggest that the imaging quality enhancement via tensor framelet method is prominent for the low-dose imaging on TomoTherapy with up to a 75% projection view reduction.