4D computed tomography reconstruction from few-projection data via temporal non-local regularization.
4D computed tomography (4D-CT) is an important modality in medical imaging due to its ability to resolve patient anatomy motion in each respiratory phase. Conventionally 4D-CT is accomplished by performing the reconstruction for each phase independently as in a CT reconstruction problem. We propose a new 4D-CT reconstruction algorithm that explicitly takes into account the temporal regularization in a non-local fashion. By imposing a regularization of a temporal non-local means (TNLM) form, 4D-CT images at all phases can be reconstructed simultaneously based on extremely under-sampled x-ray projections. Our algorithm is validated in one digital NCAT thorax phantom and two real patient cases. It is found that our TNLM algorithm is capable of reconstructing the 4D-CT images with great accuracy. The experiments also show that our approach outperforms standard 4D-CT reconstruction methods with spatial regularization of total variation or tight frames.
Jia
Xun
X
Department of Radiation Oncology, University of California, San Diego, La Jolla, CA 92037-0843, USA.
Lou
Yifei
Y
Dong
Bin
B
Tian
Zhen
Z
Jiang
Steve
S
D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*