Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography

Zhicong Yu, Shuai Leng, Zhoubo Li, Cynthia H. McCollough

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.

Original languageEnglish (US)
Pages (from-to)6707-6732
Number of pages26
JournalPhysics in medicine and biology
Issue number18
StatePublished - Aug 23 2016


  • computed tomography
  • noise reduction
  • photon counting
  • spectral PICCS

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography'. Together they form a unique fingerprint.

Cite this