Compressed Sensing for Image Reconstruction and Treatment Plan Optimization

Lei Xing
Stanford University
Radiation Oncology

The classical Shannon-Nyquist sampling theorem specifies that to avoid losing information when capturing a signal, one must sample at least two times faster than the signal bandwidth. In many applications, the Nyquist rate is so high that too many samples result. In other applications, such as medical imaging and radiation therapy treatment planning, increasing the sampling rate is either impractical or too expensive. Compressed sensing provides a practically valuable approach for finding optimal solutions with under-sampled data. In this talk, I will summarize our recent work on 3D and 4D cone beam CT reconstruction, TEM 3D reconstruction, metal artifacts removal in CT imaging and IMRT/VMAT inverse planning based on compressed sensing techniques. We show that effective utilization of prior knowledge of the systems through compressed sensing can greatly reduce the required number of measurement samples determined by the Shannon-Nyquist theorem. Compressed sensing has significant interactions and bearings on fields of radiation oncology and medical imaging.


Zhu L, Xing L. Search for IMRT inverse plans with piecewise constant fluence maps using compressed sensing techniques. Medical physics 2009;36:1895-1905.

Choi K, Wang J, Zhu L, Suh S, Boyd S, and Xing, Cone-Beam CT Dose Reduction by Compressed Sensing with Iterative Forward and Back-Projections, Medical Physics, submitted.

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