TY - CHAP

T1 - Analytic Cone-Beam CT reconstructions

AU - Song, Bongyong

AU - Nam, Wooseok

AU - Park, Justin C.

AU - Song, William Y.

N1 - Publisher Copyright:
© 2016 by Taylor & Francis Group, LLC.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - THE 3D CONE BEAM computed tomography (CBCT) reconstructions can be done either analytically or iteratively. An analytic approach has an explicit formula for reconstructing the 3D volumetric images from a set of x-ray projection data. In particular, the well-known Feldkamp, Davis, and Kress (FDK) algorithm [1] offers a computationally efficient approximate formula that has an advantage of obtaining fair quality 3D images without requiring excess computations. Given the excessive number of voxels to be reconstructed for a CBCT image (often a few tens of millions of voxels), this low complexity formula has been the most commonly used CBCT reconstruction method in practice. It will be shown in Section 3.4 that, by using a currently available off-the-shelf graphics processing unit (GPU) computer, near real-time 3D CBCT reconstruction is possible. This computational advantage came from the fact that the reconstruction algorithm is derived from a simple yet neat mathematical x-ray projection model that includes an infinitesimal focal spot of the x-ray source, pencil beams from the source without any scattering, no measurement noise at the detector, etc. For this reason, the analytic method is not too flexible to incorporate various nonideal factors in the real system or to leverage possible additional information that can be used for further improving the image quality.

AB - THE 3D CONE BEAM computed tomography (CBCT) reconstructions can be done either analytically or iteratively. An analytic approach has an explicit formula for reconstructing the 3D volumetric images from a set of x-ray projection data. In particular, the well-known Feldkamp, Davis, and Kress (FDK) algorithm [1] offers a computationally efficient approximate formula that has an advantage of obtaining fair quality 3D images without requiring excess computations. Given the excessive number of voxels to be reconstructed for a CBCT image (often a few tens of millions of voxels), this low complexity formula has been the most commonly used CBCT reconstruction method in practice. It will be shown in Section 3.4 that, by using a currently available off-the-shelf graphics processing unit (GPU) computer, near real-time 3D CBCT reconstruction is possible. This computational advantage came from the fact that the reconstruction algorithm is derived from a simple yet neat mathematical x-ray projection model that includes an infinitesimal focal spot of the x-ray source, pencil beams from the source without any scattering, no measurement noise at the detector, etc. For this reason, the analytic method is not too flexible to incorporate various nonideal factors in the real system or to leverage possible additional information that can be used for further improving the image quality.

UR - http://www.scopus.com/inward/record.url?scp=85054247579&partnerID=8YFLogxK

U2 - 10.1201/b18968

DO - 10.1201/b18968

M3 - Chapter

AN - SCOPUS:85054247579

SN - 9781482244786

SP - 31

EP - 46

BT - Graphics Processing Unit-Based High Performance Computing in Radiation Therapy

PB - CRC Press

ER -