Intravenous contrast-enhanced cone beam computed tomography (IVCBCT) of intrahepatic tumors and vessels

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Simultaneous Image Reconstruction and Element Decomposition for Iodine Contrast Agent Visualization in Multienergy Element-Resolved Cone Beam CT

Iodine contrast agent is widely used in liver cancer radiotherapy at CT simulation stage to enhance detectability of tumor. However, its application in cone beam CT (CBCT) for image guidance before treatment delivery is still limited because of poor image quality and excessive dose of contrast agent during multiple treatment fractions. We previously developed a multienergy element-resolved (MEER) CBCT framework that included x-ray projection data acquisition on a conventional CBCT platform in a kVp-switching model and a dictionary-based image reconstruction algorithm that simultaneously reconstructed x-ray attenuation images at each kilovoltage peak (kVp), an electron density image, and elemental composition images. In this study, we investigated feasibility using MEER-CBCT for low-concentration iodine contrast agent visualization. We performed simulation and experimental studies using a phantom with inserts containing water and different concentrations of iodine solution and the MEER-CBCT scan with 600 projections in a full gantry rotation, in which the kVp level sequentially changed among 80, 100, and 120 kVps. We included iodine material in the dictionary of the reconstruction algorithm. We analyzed iodine detectability as quantified by contrast-to-noise ratio (CNR) and compared results with those of CBCT images reconstructed by the standard filter back projection (FBP) method with 600 projections. MEER-CBCT achieved similar contrast enhancement as FBP method but significantly higher CNR. At 2.5% iodine solution concentration, FBP method achieved 170 HU enhancement and CNR of 2.0, considered the standard CNR for successful tumor visualization. MEER-CBCT achieved the same CNR but at ~6.3 times lower iodine concentration of 0.4%.

Technical Note: Evaluation of kV CBCT enhancement using a liver-specific contrast agent for stereotactic body radiation therapy image guidance

PURPOSE

To evaluate possible use for cone-beam computed tomography (CBCT) guidance, this phantom study evaluated the contrast enhancement provided by Gadoxetate Disodium (Primovist^® CAN/EU, or Eovist^® USA, Bayer Healthcare, Leverkusen, Germany), a contrast agent that is taken up selectively by liver cells and is retained for up to an hour. Image quality from CBCT was benchmarked against helical fan-beam computed tomography for two phantom geometries.

METHODS AND MATERIALS

Concentrations were diluted to 0.0125-0.1 mmol per kilogram of body weight (mmol/kg) corresponding to expected physiological concentrations in the liver. Kilovoltage CBCT imaging parameters of x-ray tube potential, current, and filtration were investigated using clinically available options on a TrueBeam STx linear accelerator CBCT platform. Two phantoms were created, a cylindrical idealized imaging geometry and an ellipsoidal more realistic abdominal geometry. All parameters were optimized according to the contrast-to-noise ratio (CNR) image quality metric, as a function of concentration, following the Rose criterion for CNR.

RESULTS

Acceptable CNR was defined as greater than or equal to three, in accordance with the Rose criterion for CNR. These were found in a range of expected liver concentrations of 0.025-0.1 mmol/kg for a tube potential of 100 kVp, half-fan bowtie filtration and tube currents giving exposures between 2025 and 5085 mAs. Linear correlations were found for all CNR as a function of concentration, in agreement with the literature.

CONCLUSION

Based on this phantom study, with appropriate selection of imaging protocol, Gadoxetate Disodium may provide useful liver CBCT enhancement at physiologically achievable liver concentrations.

Fast elastic registration of soft tissues under large deformations

A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance.