Fifty-sixth annual meeting of the American association of physicists in medicine
SU-D-17A-02: Four-Dimensional CBCT Using Conventional CBCT Dataset and Iterative Subtraction Algorithm of a Lung Patient
The Iterative Subtraction Algorithm (ISA) method generates retrospectively a pre-selected motion phase cone-beam CT image from the full motion cone-beam CT acquired at standard rotation speed. This work evaluates ISA method with real lung patient data.
The goal of the ISA algorithm is to extract motion and no- motion components form the full reconstruction CBCT. The workflow consists of subtracting from the full CBCT all of the undesired motion phases and obtain a motion de-blurred single-phase CBCT image, followed by iteration of this subtraction process. ISA is realized as follows: 1) The projections are sorted to various phases, and from all phases, a full reconstruction is performed to generate an image CTM. 2) Generate forward projections of CTM at the desired phase projection angles, the subtraction of projection and the forward projection will reconstruct a CTSub1, which diminishes the desired phase component. 3) By adding back the CTSub1 to CTm, no motion CBCT, CTS1, can be computed. 4) CTS1 still contains residual motion component. 5) This residual motion component can be further reduced by iteration. The ISA 4DCBCT technique was implemented using Varian Trilogy accelerator OBI system. To evaluate the method, a lung patient CBCT dataset was used. The reconstruction algorithm is FDK.
The single phase CBCT reconstruction generated via ISA successfully isolates the desired motion phase from the full motion CBCT, effectively reducing motion blur. It also shows improved image quality, with reduced streak artifacts with respect to the reconstructions from unprocessed phase-sorted projections only.
A CBCT motion de-blurring algorithm, ISA, has been developed and evaluated with lung patient data. The algorithm allows improved visualization of a single phase motion extracted from a standard CBCT dataset.
This study has been supported by National Institute of Health through R01CA133539.