A general method to derive tissue parameters for Monte Carlo dose calculation with multi-energy CT

Lalonde, Arthur; Bouchard, Hugo

doi:10.1088/0031-9155/61/22/8044

Cited by 62 publications

(157 citation statements)

References 45 publications

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“…The detailed steps for this procedure are summarized in Appendix A of Ref. , the eigentissues of soft tissues and bones derived from the recommended values of White and Woodard are given in Tables and of this same reference. In a scheme where the dimensionality of human tissue compositions must be reduced, the eigentissues are sorted in such a way that the variance of

y_{k}

within tissues is strictly decreasing with increasing k .…”

Section: Methodsmentioning

confidence: 99%

“…The partial densities

y_{k}

for k ∈ [1, K ] are considered variable and the values of

y_{k}

for k ∈ [ K +1, M ] are fixed to their average value and summed in one quantity

y_{0} = {false\sum}_{k = K + 1}^{M} {\bar{y}}_{k}

with

{\bar{y}}_{k}

being the partial density of the

k^{th}

eigentissue averaged over the human tissue database, as described in Eq. of reference. The eigentissue composition of the human tissue is then approximated with:

x \approx y_{0} {boldp}_{0} + {false\sum}_{k = 1}^{K} y_{k} {boldp}_{k}

where

p_{0}

is the electronic elemental fraction of the residual eigentissue and given by:

{boldp}_{0} = \frac{1}{y_{0}} \sum_{k = K + 1}^{M} {\bar{y}}_{k} {boldp}_{k} .

Equivalently, if only K independent information is available (i.e., K different values of u in each voxel), the CT data are decomposed using only the K most significant eigentissue partial densities

u \approx y_{0} f_{0}^{normaleigen} + {false\sum}_{k = 1}^{K} y_{k} f_{k}^{normaleigen}

this way optimizing information extraction from the measured data.…”

Section: Methodsmentioning

confidence: 99%

“…Several calibration methods were published in the past few years to estimate SPR from measured DECT data, all yielding different levels of accuracy and practicability . It was also recently shown theoretically that multi‐energy CT (MECT) could improve SPR estimation by using more than two spectra in a noise‐free environment …”

Section: Introductionmentioning

confidence: 99%

“…The estimator is also dependent of the noise level, according less importance to HU residuals when a higher noise is observed. For DECT applications, the method is compared with results obtained using a state‐of‐the‐art ED‐EAN formalism and using maximum‐likelihood ETD, as originally proposed . Furthermore, the potential gain in using more than two energies is investigated by simulating MECT data with different number of energy bins with an equivalent overall level of noise.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian approach to solve proton stopping powers from noisy multi‐energy CT data

2017

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y_{k}

within tissues is strictly decreasing with increasing k .…”

Section: Methodsmentioning

confidence: 99%

“…The partial densities

y_{k}

for k ∈ [1, K ] are considered variable and the values of

y_{k}

for k ∈ [ K +1, M ] are fixed to their average value and summed in one quantity

y_{0} = {false\sum}_{k = K + 1}^{M} {\bar{y}}_{k}

with

{\bar{y}}_{k}

being the partial density of the

k^{th}

eigentissue averaged over the human tissue database, as described in Eq. of reference. The eigentissue composition of the human tissue is then approximated with:

x \approx y_{0} {boldp}_{0} + {false\sum}_{k = 1}^{K} y_{k} {boldp}_{k}

where

p_{0}

is the electronic elemental fraction of the residual eigentissue and given by:

{boldp}_{0} = \frac{1}{y_{0}} \sum_{k = K + 1}^{M} {\bar{y}}_{k} {boldp}_{k} .

Equivalently, if only K independent information is available (i.e., K different values of u in each voxel), the CT data are decomposed using only the K most significant eigentissue partial densities

u \approx y_{0} f_{0}^{normaleigen} + {false\sum}_{k = 1}^{K} y_{k} f_{k}^{normaleigen}

this way optimizing information extraction from the measured data.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian approach to solve proton stopping powers from noisy multi‐energy CT data

2017

Self Cite

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“…In the presence of contrast agent, the reduced attenuation coefficients can also be expressed for each X‐ray tube j = {A,B} as a linear combination of the contribution of two materials,

u false(E_{j} false) = x_{normallung} f_{normallung} false(E_{j} false) + x_{normalC} f_{normalC} false(E_{j} false),

where

f_{lung}

and

f_{C}

are the relative electronic cross sections for the lung tissue and the contrast agent respectively;

x_{lung}

and

x_{C}

are the respective partial electron densities. The electron density

ρ_{e}

relative to water can also be computed by summing the partial electron density of each material,

{italicρ}_{normale} = x_{normallung} + x_{normalC} .

The values of

f_{lung}

and

f_{C}

are obtained following a stoichiometric calibration originally proposed by Lalonde and Bouchard and further adapted to the presence of iodine. Such CT calibration requires a set of tissue‐equivalent materials and contrast media of known composition to properly address their electronic cross section relative to water f .…”

Section: Methodsmentioning

confidence: 99%

Assessing lung function using contrast‐enhanced dual‐energy computed tomography for potential applications in radiation therapy

et al. 2017

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Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual‐energy CT

Kruis

2021

J Applied Clin Med Phys

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Over the past decade, spectral or dual-energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT. In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs-at-risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

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A general method to derive tissue parameters for Monte Carlo dose calculation with multi-energy CT

Cited by 62 publications

References 45 publications

A Bayesian approach to solve proton stopping powers from noisy multi‐energy CT data

A Bayesian approach to solve proton stopping powers from noisy multi‐energy CT data

Assessing lung function using contrast‐enhanced dual‐energy computed tomography for potential applications in radiation therapy

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual‐energy CT

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