Texture-based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs.

PURPOSE: Evaluation of contour accuracy in radiation therapy planning requires manual interaction and is one of the most limiting bottlenecks for online replanning. This study aims to develop an automatic approach to rapidly evaluate contour quality based on image texture features to facilitate the routine practice of online adaptive replanning (OLAR). METHOD: Fifty-five pancreas cancer patients were selected from a clinical database of patients treated at our institution from 2011 to 2018. For each patient, the pancreas head and duodenum were contoured in five images (one fraction per week) resulting in a total of 275 CT image sets with corresponding ground-truth contours. A second set of inaccurate contours was generated using deformable-image-registration-based contour propagation. Three subregions, core, inner shell and outer shell, were generated from the contour of each organ. Texture features were extracted from each subregion and descriptive features of each subregion were identified using the image set with corresponding ground-truth contours. A three-level decision tree model was constructed based on texture constraints empirically determined for the three subregions. The two datasets containing ground truth and inaccurate contours were merged. Randomized threefold cross-validation was performed and repeated three times. RESULTS: The first level of the decision tree utilizes textures derived from principal component analysis of a subset of extracted features from the core subregion (five PCs for pancreas head, seven PCs for duodenum). The second and third levels of the decision tree use gray-level co-occurrence matrix (GLCM)-based cluster prominence to reject inaccurate contours. The trained model identifies accurate and inaccurate contours with an average sensitivity/specificity of 85%/91% for the pancreas head and 92%/92% for the duodenum contours. The false-positive rate is 9% and 8% for pancreas head and duodenum, respectively. The execution time is less than 15 s using a standard desktop computer. CONCLUSION: Quantitative image features can be used to develop a model to rapidly validate the quality of an organ contour. Our model accurately classifies unseen contours as accurate or inaccurate with high sensitivity and specificity. As auto-segmentation continues to improve in quality and accuracy, this method may be integrated into a fully automatic pipeline for auto-segmentation, contour-quality evaluation and contour correction, which would replace the time-consuming manual review process, thereby facilitating the more routine practice of OLAR.

Time stability of delta-radiomics features and the impact on patient analysis in longitudinal CT images.

PURPOSE: This study first aims to show that the values of texture features extracted from phantoms are stable over clinical timescales. Second, that changes in patients' feature values over the course of radiation therapy (RT) are treatment induced and statistically significant. METHODS: The CT datasets of a 3D printed anatomically informed texture phantom containing liver and low-contrast modules, and the homogeneous module of the Catphan 500-Series phantom, were acquired once per week over the course of a 6-week period, to simulate the timescale of conventional RT duration. A Definition AS Open CT scanner on rails (Siemens) and our institution's standard abdominal protocol were used. In each phantom module, 8 regions of interest (20 cm 3 ) were selected and 50 texture features were extracted from each module over the longitudinal dataset. The time stability of each feature was evaluated. The expected variation over the treatment timescale was quantified for each texture (module). Subsequently, the pancreas heads of 10 patients who underwent RT for adenocarcinoma of the pancreas head with a pathologic response of at least "moderate" (grade 2), were contoured on the daily CTs acquired using the same scanner. The pancreas heads were contoured on one image per week. Mean CT number, skewness, kurtosis, and coarseness were extracted from these data. The phantom modules were shown to be accurate representations of these features in the pancreas data. The change in the feature value between fractions 2 and 26 was compared with the phantom data in order to identify significant changes in feature value. RESULTS: Of the 50 features examined in all 3 phantom modules, 47 were found to have zero time-trend when a fit assuming homogeneous variance was used. When a fit allowing for heterogeneous variance was used, 49 features were found to have zero time-trend. Features were stable and repeatable within a feature-specific confidence interval over the 6-week period of acquisition in all three phantom modules. Changes in feature value between fractions 2 and 26 were highly patient specific. Mean CT number was found to decrease significantly in 7 of 10 patients and increase significantly in one patient. Skewness increased significantly in one patient and decreased significantly in one patient. Kurtosis decreased significantly in four patients and increased significantly in one patient. Coarseness increased significantly in seven patients and decreased significantly in one patient. Only one patient experienced no significant changes in feature value. CONCLUSION: The CT texture feature measurements of phantoms are stable and repeatable within a feature-specific confidence interval in all three phantom modules. This suggests that the changes observed in features extracted from longitudinal patient CT data may be treatment induced, and demonstrates their potentiality for early assessment of treatment response.

Software platform for simulation of a prototype proton CT scanner.

PURPOSE: Proton computed tomography (pCT) is a promising imaging technique to substitute or at least complement x-ray CT for more accurate proton therapy treatment planning as it allows calculating directly proton relative stopping power from proton energy loss measurements. A proton CT scanner with a silicon-based particle tracking system and a five-stage scintillating energy detector has been completed. In parallel a modular software platform was developed to characterize the performance of the proposed pCT. METHOD: The modular pCT software platform consists of (1) a Geant4-based simulation modeling the Loma Linda proton therapy beam line and the prototype proton CT scanner, (2) water equivalent path length (WEPL) calibration of the scintillating energy detector, and (3) image reconstruction algorithm for the reconstruction of the relative stopping power (RSP) of the scanned object. In this work, each component of the modular pCT software platform is described and validated with respect to experimental data and benchmarked against theoretical predictions. In particular, the RSP reconstruction was validated with both experimental scans, water column measurements, and theoretical calculations. RESULTS: The results show that the pCT software platform accurately reproduces the performance of the existing prototype pCT scanner with a RSP agreement between experimental and simulated values to better than 1.5%. CONCLUSIONS: The validated platform is a versatile tool for clinical proton CT performance and application studies in a virtual setting. The platform is flexible and can be modified to simulate not yet existing versions of pCT scanners and higher proton energies than those currently clinically available.

An evaluation of spatial resolution of a prototype proton CT scanner.

PURPOSE: To evaluate the spatial resolution of proton CT using both a prototype proton CT scanner and Monte Carlo simulations. METHODS: A custom cylindrical edge phantom containing twelve tissue-equivalent inserts with four different compositions at varying radial displacements from the axis of rotation was developed for measuring the modulation transfer function (MTF) of a prototype proton CT scanner. Two scans of the phantom, centered on the axis of rotation, were obtained with a 200 MeV, low-intensity proton beam: one scan with steps of 4°, and one scan with the phantom continuously rotating. In addition, Monte Carlo simulations of the phantom scan were performed using scanners idealized to various degrees. The data were reconstructed using an iterative projection method with added total variation superiorization based on individual proton histories. Edge spread functions in the radial and azimuthal directions were obtained using the oversampling technique. These were then used to obtain the modulation transfer functions. The spatial resolution was defined by the 10% value of the modulation transfer function (MTF10%) in units of line pairs per centimeter (lp/cm). Data from the simulations were used to better understand the contributions of multiple Coulomb scattering in the phantom and the scanner hardware, as well as the effect of discretization of proton location. RESULTS: The radial spatial resolution of the prototype proton CT scanner depends on the total path length, W, of the proton in the phantom, whereas the azimuthal spatial resolution depends both on W and the position, u-, at which the most-likely path uncertainty is evaluated along the path. For protons contributing to radial spatial resolution, W varies with the radial position of the edge, whereas for protons contributing to azimuthal spatial resolution, W is approximately constant. For a pixel size of 0.625 mm, the radial spatial resolution of the image reconstructed from the fully idealized simulation data ranged between 6.31 ± 0.36 lp/cm for W = 197 mm i.e., close to the center of the phantom, and 13.79 ± 0.36 lp/cm for W = 97 mm, near the periphery of the phantom. The azimuthal spatial resolution ranged from 6.99 ± 0.23 lp/cm at u- = 75 mm (near the center) to 11.20 ± 0.26 lp/cm at u- = 20 mm (near the periphery). Multiple Coulomb scattering limits the radial spatial resolution for path lengths greater than approximately 130 mm, and the azimuthal spatial resolution for positions of evaluation greater than approximately 40 mm for W = 199 mm. The radial spatial resolution of the image reconstructed from data from the 4° stepped experimental scan ranged from 5.11 ± 0.61 lp/cm for W = 197 mm to 8.58 ± 0.50 lp/cm for W = 97 mm. In the azimuthal direction, the spatial resolution ranged from 5.37 ± 0.40 lp/cm at u- = 75 mm to 7.27 ± 0.39 lp/cm at u- = 20 mm. The continuous scan achieved the same spatial resolution as that of the stepped scan. CONCLUSIONS: Multiple Coulomb scattering in the phantom is the limiting physical factor of the achievable spatial resolution of proton CT; additional loss of spatial resolution in the prototype system is associated with scattering in the proton tracking system and inadequacies of the proton path estimate used in the iterative reconstruction algorithm. Improvement in spatial resolution may be achievable by improving the most likely path estimate by incorporating information about high and low density materials, and by minimizing multiple Coulomb scattering in the proton tracking system.

A Fast Experimental Scanner for Proton CT: Technical Performance and First Experience with Phantom Scans.

We report on the design, fabrication, and first tests of a tomographic scanner developed for proton computed tomography (pCT) of head-sized objects. After extensive preclinical testing, pCT is intended to be employed in support of proton therapy treatment planning and pre-treatment verification in patients undergoing particle-beam therapy. The scanner consists of two silicon-strip telescopes that track individual protons before and after the phantom, and a novel multistage scintillation detector that measures a combination of the residual energy and range of the proton, from which we derive the water equivalent path length (WEPL) of the protons in the scanned object. The set of WEPL values and the associated paths of protons passing through the object over a 360° angular scan are processed by an iterative, parallelizable reconstruction algorithm that runs on modern GP-GPU hardware. In order to assess the performance of the scanner, we have performed tests with 200 MeV protons from the synchrotron of the Loma Linda University Medical Center and the IBA cyclotron of the Northwestern Medicine Chicago Proton Center. Our first objective was calibration of the instrument, including tracker channel maps and alignment as well as the WEPL calibration. Then we performed the first CT scans on a series of phantoms. The very high sustained rate of data acquisition, exceeding one million protons per second, allowed a full 360° scan to be completed in less than 10 minutes, and reconstruction of a CATPHAN 404 phantom verified accurate reconstruction of the proton relative stopping power in a variety of materials.