title: Dose reconstruction for real-time patient-specific dose estimation in CT.
abstract: OBJECTIVE
  Many recent computed tomography (CT) dose reduction approaches belong to one of three categories: statistical reconstruction algorithms, efficient x-ray detectors, and optimized CT acquisition schemes with precise control over the x-ray distribution. The latter category could greatly benefit from fast and accurate methods for dose estimation, which would enable real-time patient-specific protocol optimization.
  
  
  METHODS
  The authors present a new method for volumetrically reconstructing absorbed dose on a per-voxel basis, directly from the actual CT images. The authors' specific implementation combines a distance-driven pencil-beam approach to model the first-order x-ray interactions with a set of Gaussian convolution kernels to model the higher-order x-ray interactions. The authors performed a number of 3D simulation experiments comparing the proposed method to a Monte Carlo based ground truth.
  
  
  RESULTS
  The authors' results indicate that the proposed approach offers a good trade-off between accuracy and computational efficiency. The images show a good qualitative correspondence to Monte Carlo estimates. Preliminary quantitative results show errors below 10%, except in bone regions, where the authors see a bigger model mismatch. The computational complexity is similar to that of a low-resolution filtered-backprojection algorithm.
  
  
  CONCLUSIONS
  The authors present a method for analytic dose reconstruction in CT, similar to the techniques used in radiation therapy planning with megavoltage energies. Future work will include refinements of the proposed method to improve the accuracy as well as a more extensive validation study. The proposed method is not intended to replace methods that track individual x-ray photons, but the authors expect that it may prove useful in applications where real-time patient-specific dose estimation is required.
authors.affiliation: Image Reconstruction Laboratory, GE Global Research, Niskayuna, New York 12309.
authors.forename: Bruno
authors.initials: B
authors.lastname: De Man
authors.affiliation: X-ray and CT Laboratory, GE Global Research, Shanghai 201203, China.
authors.forename: Mingye
authors.initials: M
authors.lastname: Wu
authors.affiliation: Radiation Systems Laboratory, GE Global Research, Niskayuna, New York 12309.
authors.forename: Paul
authors.initials: P
authors.lastname: FitzGerald
authors.affiliation: Divisions of Thoracic and Cardiac Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114.
authors.forename: Mannudeep
authors.initials: M
authors.lastname: Kalra
authors.affiliation: Image Reconstruction Laboratory, GE Global Research, Niskayuna, New York 12309.
authors.forename: Zhye
authors.initials: Z
authors.lastname: Yin
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D013902:Radiography, Thoracic / Q000295:instrumentation / Q000379:methods; D011874:Radiometry / Q000295:instrumentation / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*