Christian Möhler

Range prediction for tissue mixtures based on dual-energy CT.

The use of dual-energy CT (DECT) potentially decreases range uncertainties in proton and ion therapy treatment planning via determination of the involved physical target quantities. For eventual clinical application, the correct treatment of tissue mixtures and heterogeneities is an essential feature, as they naturally occur within a patients CT. Here, we present how existing methods for DECT-based ion-range prediction can be modified in order to incorporate proper mixing behavior on several structural levels. Our approach is based on the factorization of the stopping-power ratio into the relative electron density and the relative stopping number. The latter is confined for tissue between about 0.95 and 1.02 at a therapeutic beam energy of 200 MeV u(-1) and depends on the I-value. We show that convenient mixing and averaging properties arise by relating the relative stopping number to the relative cross section obtained by DECT. From this, a maximum uncertainty of the stopping-power ratio prediction below [Formula: see text] is suggested for arbitrary mixtures of human body tissues.

Dual-energy CT based proton range prediction in head and pelvic tumor patients

BACKGROUND AND PURPOSE To reduce range uncertainty in particle therapy, an accurate computation of stopping-power ratios (SPRs) based on computed tomography (CT) is crucial. Here, we assess range differences between the state-of-the-art CT-number-to-SPR conversion using a generic Hounsfield look-up table (HLUT) and a direct patient-specific SPR prediction (RhoSigma) based on dual-energy CT (DECT) in 100 proton treatment fields. MATERIAL AND METHODS For 25 head-tumor and 25 prostate-cancer patients, the clinically applied treatment plan, optimized using a HLUT, was recalculated with RhoSigma as CT-number-to-SPR conversion. Depth-dose curves in beam direction were extracted for both dose distributions in a regular grid and range deviations were determined and correlated to SPR differences within the irradiated volume. RESULTS Absolute (relative) mean water-equivalent range shifts of 1.1mm (1.2%) and 4.1mm (1.7%) were observed in the head-tumor and prostate-cancer cohort, respectively. Due to the case dependency of a generic HLUT, range deviations within treatment fields strongly depend on the tissues traversed leading to a larger variation within one patient than between patients. CONCLUSIONS The magnitude of patient-specific range deviations between HLUT and the more accurate DECT-based SPR prediction is clinically relevant. A clinical application of the latter seems feasible as demonstrated in this study using medically approved systems from CT acquisition to treatment planning.

Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy

PURPOSE Phantom surrogates were developed to allow multimodal [computed tomography (CT), magnetic resonance imaging (MRI), and teletherapy] and anthropomorphic tissue simulation as well as materials and methods to construct deformable organ shapes and anthropomorphic bone models. METHODS Agarose gels of variable concentrations and loadings were investigated to simulate various soft tissue types. Oils, fats, and Vaseline were investigated as surrogates for adipose tissue and bone marrow. Anthropomorphic shapes of bone and organs were realized using 3D-printing techniques based on segmentations of patient CT-scans. All materials were characterized in dual energy CT and MRI to adapt CT numbers, electron density, effective atomic number, as well as T1- and T2-relaxation times to patient and literature values. RESULTS Soft tissue simulation could be achieved with agarose gels in combination with a gadolinium-based contrast agent and NaF to simulate muscle, prostate, and tumor tissues. Vegetable oils were shown to be a good representation for adipose tissue in all modalities. Inner bone was realized using a mixture of Vaseline and K2HPO4, resulting in both a fatty bone marrow signal in MRI and inhomogeneous areas of low and high attenuation in CT. The high attenuation of outer bone was additionally adapted by applying gypsum bandages to the 3D-printed hollow bone case with values up to 1200 HU. Deformable hollow organs were manufactured using silicone. Signal loss in the MR images based on the conductivity of the gels needs to be further investigated. CONCLUSIONS The presented surrogates and techniques allow the customized construction of multimodality, anthropomorphic, and deformable phantoms as exemplarily shown for a pelvic phantom, which is intended to study adaptive treatment scenarios in MR-guided radiation therapy.

Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues

An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissue samples were prepared for measurement, comprising five samples each of 13 tissue types representing about 80% of the total body mass (three different muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone). Using a standard stoichiometric calibration for single-energy CT (SECT) as well as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all tissues and then compared to the measured reference. With the SECT approach, the SPRs of all tissues were predicted with a mean error of (-0.84  ±  0.12)% and a mean absolute error of (1.27  ±  0.12)%. In contrast, the DECT-based SPR predictions were overall consistent with the measured reference with a mean error of (-0.02  ±  0.15)% and a mean absolute error of (0.10  ±  0.15)%. Thus, in this study, the potential of DECT to decrease range uncertainty could be confirmed in biological tissue.

Methodological accuracy of image‐based electron density assessment using dual‐energy computed tomography

Purpose Electron density is the most important tissue property influencing photon and ion dose distributions in radiotherapy patients. Dual‐energy computed tomography (DECT) enables the determination of electron density by combining the information on photon attenuation obtained at two different effective x‐ray energy spectra. Most algorithms suggested so far use the CT numbers provided after image reconstruction as input parameters, i.e., are imaged‐based. To explore the accuracy that can be achieved with these approaches, we quantify the intrinsic methodological and calibration uncertainty of the seemingly simplest approach. Methods In the studied approach, electron density is calculated with a one‐parametric linear superposition (‘alpha blending’) of the two DECT images, which is shown to be equivalent to an affine relation between the photon attenuation cross sections of the two x‐ray energy spectra. We propose to use the latter relation for empirical calibration of the spectrum‐dependent blending parameter. For a conclusive assessment of the electron density uncertainty, we chose to isolate the purely methodological uncertainty component from CT‐related effects such as noise and beam hardening. Results Analyzing calculated spectrally weighted attenuation coefficients, we find universal applicability of the investigated approach to arbitrary mixtures of human tissue with an upper limit of the methodological uncertainty component of 0.2%, excluding high‐Z elements such as iodine. The proposed calibration procedure is bias‐free and straightforward to perform using standard equipment. Testing the calibration on five published data sets, we obtain very small differences in the calibration result in spite of different experimental setups and CT protocols used. Employing a general calibration per scanner type and voltage combination is thus conceivable. Conclusion Given the high suitability for clinical application of the alpha‐blending approach in combination with a very small methodological uncertainty, we conclude that further refinement of image‐based DECT‐algorithms for electron density assessment is not advisable.

Comment on: Dosimetric comparison of stopping-power calibration with dual-energy CT and single-energy CT in proton therapy treatment planning [Med. Phys. 43(6), 2845-2854 (2016)]

A dosimetric comparison of proton treatment planning based on single-energy CT (SECT) and dual-energy CT (DECT) was recently published by Zhu and Penfold1 in Medical Physics. In this study, the polymer phantom Catphan Module 404 (The Phantom Laboratory, Salem, NY, USA) of known material composition was used to demonstrate an improved accuracy of dose calculation using DECT instead of SECT. To confirm this result in a more realistic human case, the authors show for a single axial CT slice the dose difference of a SECT- and DECT-based treatment plan using the anthropomorphic phantom Rando (Radiological Support Devices, Inc., CA, USA) of unknown composition. This article is protected by copyright. All rights reserved.

OC-0154: Clinical use of dual-energy CT for proton treatment planning to reduce CT-based range uncertainties

Material and Methods: A set of teeth containing an amalgamfilled removable tooth and an artificial polycaprolactone tumour was placed in water and CT scanned (Siemens Somatom Definition AS) at 120 kVp, 80 kVp, and 140 kVp. The two latter scans were used to reconstruct virtual monochromatic (VM) images. All image sets were additionally reconstructed with metal artefact reduction (MAR) software (iMAR, Siemens Healthcare). The following 4 MAR reconstructions were studied: 1) 130 keV VM 2) 70 keV VM with MAR, 3) 120 kVp with MAR, 4) 130 keV VM with MAR. A conventional 120 kVp CT was also taken and a 120 kVp image where the metal tooth was removed was used as control. 3 oncologists and 2 radiologists contoured the tumour volume on all 6 image sets while blinded to the image reconstruction type. A 7th high-quality image of only the artificial tumour was contoured to obtain the true shape of the tumour. Maximal Hausdorff distances and DICE coefficients of the 5 delineated contours compared to the true contour was were used to quantify delineation accuracy in all 6 image sets. Statistically, a Friedman-test was used for primary comparisons and a Nemenyi-test is performed for pairwise post hoc analysis.