PMID- 28752662
OWN - NLM
STAT- MEDLINE
DCOM- 20180523
LR  - 20220408
IS  - 2473-4209 (Electronic)
IS  - 0094-2405 (Linking)
VI  - 44
IP  - 10
DP  - 2017 Oct
TI  - A Bayesian approach to solve proton stopping powers from noisy multi-energy CT 
      data.
PG  - 5293-5302
LID - 10.1002/mp.12489 [doi]
AB  - PURPOSE: To propose a new formalism allowing the characterization of human 
      tissues from multienergy computed tomography (MECT) data affected by noise and to 
      evaluate its performance in estimating proton stopping powers (SPR). METHODS: A 
      recently published formalism based on principal component analysis called 
      eigentissue decomposition (ETD) is adapted to the context of noise using a 
      Bayesian estimator. The method, named Bayesian ETD, uses the maximum a posteriori 
      fractions of eigentissues in each voxel to determine physical parameters relevant 
      for proton beam dose calculation. Simulated dual-energy computed tomography 
      (DECT) data are used to evaluate the performance of the proposed method to 
      estimate SPR and to compare it to the initially proposed maximum-likelihood ETD 
      and to a state-of-the-art rho(e)  - Z formalism. To test the robustness of each 
      method towards clinical reality, three different levels of noise are implemented, 
      as well as variations in elemental composition and density of reference tissues. 
      The impact of using more than two energy bins to determine SPR is also 
      investigated by simulating MECT data using two to five energy bins. Finally, the 
      impact of using MECT over DECT for range prediction is evaluated using a 
      probabilistic model. RESULTS: For simulated DECT data of reference tissues, the 
      Bayesian ETD approach systematically gives lower root-mean-square (RMS) errors 
      with negligible bias. For a medium level of noise, the RMS errors on SPR are 
      found to be 2.78%, 2.76% and 1.53% for rho(e)  - Z, maximum-likelihood ETD, and 
      Bayesian ETD, respectively. When variations are introduced to the elemental 
      composition and density, all implemented methods give similar performances at low 
      noise. However, for a medium noise level, the proposed Bayesian method 
      outperforms the two others with a RMS error of 1.94%, compared to 2.79% and 2.78% 
      for rho(e)  - Z and maximum-likelihood ETD, respectively. When more than two energy 
      spectra are used, the Bayesian ETD is able to reduce RMS error on SPR using up to 
      five energy bins. In terms of range prediction, Bayesian ETD with four energy 
      bins in realistic conditions reduces proton beam range uncertainties by a factor 
      of up to 1.5 compared to rho(e)  - Z. CONCLUSION: The Bayesian ETD is shown to be 
      more robust against noise than similar methods and a promising approach to 
      extract SPR from noisy DECT data. In the advent of commercially available 
      multi-energy CT or photon-counting CT scanners, the Bayesian ETD is expected to 
      allow extracting more information and improve the precision of proton therapy 
      beyond DECT.
CI  - (c) 2017 American Association of Physicists in Medicine.
FAU - Lalonde, Arthur
AU  - Lalonde A
AD  - Departement de Physique, Universite de Montreal, Pavillon Roger-Gaudry, 2900 
      Boulevard Edouard-Montpetit, Montreal, Quebec, H3T 1J4, Canada.
FAU - Bar, Esther
AU  - Bar E
AD  - National Physical Laboratory, Acoustics and Ionising Radiation Team, Hampton 
      Road, Teddington, TW11 0LW, United Kingdom.
AD  - Department of Medical Physics and Biomedical Engineering, University College 
      London, Gower Street, London, WC1E 6BT, United Kingdom.
FAU - Bouchard, Hugo
AU  - Bouchard H
AD  - Departement de Physique, Universite de Montreal, Pavillon Roger-Gaudry, 2900 
      Boulevard Edouard-Montpetit, Montreal, Quebec, H3T 1J4, Canada.
AD  - Centre de recherche du Centre hospitalier de l'Universite de Montreal, Montreal, 
      QC H2X 0A9, Canada.
LA  - eng
PT  - Journal Article
DEP - 20170904
PL  - United States
TA  - Med Phys
JT  - Medical physics
JID - 0425746
RN  - 0 (Protons)
SB  - IM
MH  - Bayes Theorem
MH  - Image Processing, Computer-Assisted/*methods
MH  - Phantoms, Imaging
MH  - *Protons
MH  - Signal-To-Noise Ratio
MH  - *Tomography, X-Ray Computed
MH  - Uncertainty
OTO - NOTNLM
OT  - dual-energy CT
OT  - multi-energy CT
OT  - photon-counting CT
OT  - proton stopping power
OT  - proton therapy
EDAT- 2017/07/29 06:00
MHDA- 2018/05/24 06:00
CRDT- 2017/07/29 06:00
PHST- 2017/03/12 00:00 [received]
PHST- 2017/07/06 00:00 [revised]
PHST- 2017/07/23 00:00 [accepted]
PHST- 2017/07/29 06:00 [pubmed]
PHST- 2018/05/24 06:00 [medline]
PHST- 2017/07/29 06:00 [entrez]
AID - 10.1002/mp.12489 [doi]
PST - ppublish
SO  - Med Phys. 2017 Oct;44(10):5293-5302. doi: 10.1002/mp.12489. Epub 2017 Sep 4.