Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

Taasti, V.; Hansen, David C.; Michalak, Gregory J.; Deisher, A.; Kruse, Jon J.; Muren, L.P.; Petersen, Jørgen B. B.; McCollough, Cynthia H.

doi:10.1002/mp.13173

Cited by 17 publications

(16 citation statements)

References 47 publications

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“…bin. Thus, it is necessary to test how the noise in the image affects the results [40,41]. CT numbers, including noise, were simulated to evaluate the robustness of the calculation method described in Sections 2.1 and 2.2.…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Figure 12 , it is thought that the three-PFM will be less affected by the noise contained in the CT numbers than the two-PFM. As Taasti et al [ 41 ] mentioned, this noise level would be expected to be twice that of single-energy CT. We calculated the SPR using a photon-counting CT system, but there seems to be room for improvement in the image-based calculations, such as the inclusion of noise and artefacts of the reconstructed images, to accurately calculate the effective atomic number and electron density.…”

Section: Discussionmentioning

confidence: 99%

“…Compared to single-energy CT or dual-energy CT, photon-counting CT is more susceptible to image noise because only a portion of the photon is counted for each energy bin. Thus, it is necessary to test how the noise in the image affects the results [ 40 , 41 ]. CT numbers, including noise, were simulated to evaluate the robustness of the calculation method described in Section 2.1 and Section 2.2 .…”

Section: Methodsmentioning

confidence: 99%

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Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method

Lee

Sunaguchi

Nagao

et al. 2021

Sensors

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The two-parameter-fitting method (PFM) is commonly used to calculate the stopping-power ratio (SPR). This study proposes a new formalism: a three-PFM, which can be used in multiple spectral computed tomography (CT). Using a photon-counting CT system, seven rod-shaped samples of aluminium, graphite, and poly(methyl methacrylate) (PMMA), and four types of biological phantom materials were placed in a water-filled sample holder. The X-ray tube voltage and current were set at 150 kV and 40 μA, respectively, and four CT images were obtained at four threshold settings. A semi-empirical correction method that corrects the difference between the CT values from the photon-counting CT images and theoretical values in each spectral region was also introduced. Both the two- and three-PFMs were used to calculate the effective atomic number and electron density from multiple CT numbers. The mean excitation energy was calculated via parameterisation with the effective atomic number, and the SPR was then calculated from the calculated electron density and mean excitation energy. Then, the SPRs from both methods were compared with the theoretical values. To estimate the noise level of the CT numbers obtained from the photon-counting CT, CT numbers, including noise, were simulated to evaluate the robustness of the aforementioned PFMs. For the aluminium and graphite, the maximum relative errors for the SPRs calculated using the two-PFM and three-PFM were 17.1% and 7.1%, respectively. For the PMMA and biological phantom materials, the maximum relative errors for the SPRs calculated using the two-PFM and three-PFM were 5.5% and 2.0%, respectively. It was concluded that the three-PFM, compared with the two-PFM, can yield SPRs that are closer to the theoretical values and is less affected by noise.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method

Lee

Sunaguchi

Nagao

et al. 2021

Sensors

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“…This results in a noise level of ≈100 HU per energy bin, which roughly matches experimental SPCCT noise levels reported in the literature, between 80 and 112 HU. 4,6 (a)…”

Section: Sinogram Generationmentioning

confidence: 99%

“…1,3 More recently, another MECT modality, spectral photon-counting computed tomography (SPCCT), which uses photon counting detectors to provide up to four energy-resolved measurements, has been shown to provide further benefits compared to DECT for tissue characterization and reducing proton range uncertainties. [4][5][6] In those studies, the benefits of MECT were obtained using post-reconstruction methods, where tissue characterization is performed on reconstructed CT number maps. The obtained benefits typically require the use of bespoke methods for tissue characterization with MECT.…”

Section: Introductionmentioning

confidence: 99%

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography

Simard

Bouchard

2022

J. Med. Imag.

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Purpose: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. Methods:The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: α for the quantitative prior, and β for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of α and β is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogrambased two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD.Results: Optimal performance with respect to bias or RMSE is achieved with different combinations of α and β on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%.Conclusions: Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.

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Error on the stopping power ratio of ERKODENT's mouthpiece for head and neck carbon ion radiotherapy treatment

Lee

Kanai

Souda

et al. 2023

J Applied Clin Med Phys

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The errors on the stopping power ratio (SPR) of mouthpiece samples from ERKODENT were evaluated. Erkoflex and Erkoloc‐pro from ERKODENT and samples that combined Erkoflex and Erkoloc‐pro were computed tomography (CT)‐scanned using head and neck (HN) protocol at the East Japan Heavy Ion Center (EJHIC), and the values were averaged to obtain the CT number. The integral depth dose of the Bragg curve with and without these samples was measured for 292.1, 180.9, and 118.8 MeV/u of the carbon‐ion pencil beam using an ionization chamber with concentric electrodes at the horizontal port of the EJHIC. The average value of the water equivalent length (WEL) of each sample was obtained from the difference between the range of the Bragg curve and the thickness of the sample. To calculate the difference between the theoretical and measured values, the theoretical CT number and SPR value of the sample were calculated using the stoichiometric calibration method. Compared with the Hounsfield unit (HU)‐SPR calibration curve used at the EJHIC, the SPR error on each measured and theoretical value was calculated. The WEL value of the mouthpiece sample had an error of approximately 3.5% in the HU‐SPR calibration curve. From this error, it was evaluated that for a mouthpiece with a thickness of 10 mm, a beam range error of approximately 0.4 mm can occur, and for a mouthpiece with a thickness of 30 mm, a beam range error of approximately 1 mm can occur. For a beam passing through the mouthpiece in HN treatment, it would be practical to consider a mouthpiece margin of 1 mm to avoid beam range errors if ions pass through the mouthpiece.

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Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

Abstract: In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

Cited by 17 publications

References 47 publications

Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method

Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography

Error on the stopping power ratio of ERKODENT's mouthpiece for head and neck carbon ion radiotherapy treatment

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