Dual‐layer spectral CT for proton, helium, and carbon ion beam therapy planning of brain tumors

Longarino, Friderike K.; Tessonnier, Thomas; Mein, Stewart; Harrabi, Semi; Debus, Jürgen; Stiller, Wolfram; Mairani, A.

doi:10.1002/acm2.13465

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…16 MECT enables more sophisticated SPR prediction methods compared to SECT and has been shown to improve SPR prediction, 17 for both adult and pediatric tissues, 15 as well as for implant materials. 18,19 MECT with two energy measurements is usually called dual-energy CT (DECT) and has until the recent introduction of CT scanners with photon counting detectors (PCCT) 20 been the only commercially available form of MECT. 21 The different DECT techniques can produce various image types, where virtually monoenergetic (VM) images, with photon energies in various ranges between 35 and 200 keV are common for all.…”

Section: Introductionmentioning

confidence: 99%

“…The possibility to obtain additional information about the patient's tissues by performing multiple attenuation measurements with different x‐ray energies (multi‐energy CT; MECT) has been known since the invention of CT in the early 1970s 16 . MECT enables more sophisticated SPR prediction methods compared to SECT and has been shown to improve SPR prediction, 17 for both adult and pediatric tissues, 15 as well as for implant materials 18,19 . MECT with two energy measurements is usually called dual‐energy CT (DECT) and has until the recent introduction of CT scanners with photon counting detectors (PCCT) 20 been the only commercially available form of MECT 21 .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Pettersson,

Thilander‐Klang,

Bäck

2024

Medical Physics

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BackgroundProton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single‐energy computed tomography (SECT). Various studies have shown that multi‐energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age‐related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection‐based MECT systems for SPR prediction.PurposeTo present and evaluate a novel proton SPR prediction method based on MD images from dual‐energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.MethodsA theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non‐tissue materials, by minimizing the root‐mean‐square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD‐SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data‐fitting. The goodness of fit of the MD‐SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head‐ and an abdomen‐sized phantom setup, were acquired with a clinical projection‐based fast‐kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non‐tissue materials in the phantoms, as well as used for input to the MD‐SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image‐based consecutive scan DECT for the same phantom setups.ResultsThe predicted SPRs of the tissue surrogates were similar for MD‐SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non‐adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between −0.7% and −1.8%. The SPRs of the non‐tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD‐SPR images, compared to 6.8% for DirectSPR.ConclusionsThe MD‐SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non‐tissue materials.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Pettersson,

Thilander‐Klang,

Bäck

2024

Medical Physics

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Feasibility study of using triple-energy CT images for improving stopping power estimation

Kim

Cho

2023

Nuclear Engineering and Technology

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Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning

et al. 2022

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In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

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Dual‐layer spectral CT for proton, helium, and carbon ion beam therapy planning of brain tumors

Cited by 3 publications

References 49 publications

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Feasibility study of using triple-energy CT images for improving stopping power estimation

Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning

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