Impact of glioma peritumoral edema, tumor size, and tumor location on alternating electric fields (AEF) therapy in realistic 3D rat glioma models: a computational study.

Objective.Alternating electric fields (AEF) therapy is a treatment modality for patients with glioblastoma. Tumor characteristics such as size, location, and extent of peritumoral edema may affect the AEF strength and distribution. We evaluated the sensitivity of the AEFs in a realistic 3D rat glioma model with respect to these properties.Approach.The electric properties of the peritumoral edema were varied based on calculated and literature-reported values. Models with different tumor composition, size, and location were created. The resulting AEFs were evaluated in 3D rat glioma models.Main results.In all cases, a pair of 5 mm diameter electrodes induced an average field strength >1 V cm-1. The simulation results showed that a negative relationship between edema conductivity and field strength was found. As the tumor core size was increased, the average field strength increased while the fraction of the shell achieving >1.5 V cm-1decreased. Increasing peritumoral edema thickness decreased the shell's mean field strength. Compared to rostrally/caudally, shifting the tumor location laterally/medially and ventrally (with respect to the electrodes) caused higher deviation in field strength.Significance.This study identifies tumor properties that are key drivers influencing AEF strength and distribution. The findings might be potential preclinical implications.

Electric field distributions in realistic 3D rat head models during alternating electric field (AEF) therapy: a computational study.

Objective.Application of alternating electrical fields (AEFs) in the kHz range is an established treatment modality for primary and recurrent glioblastoma. Preclinical studies would enable innovations in treatment monitoring and efficacy, which could then be translated to benefit patients. We present a practical translational process converting image-based data into 3D rat head models for AEF simulations and study its sensitivity to parameter choices.Approach.Five rat head models composed of up to 7 different tissue types were created, and relative permittivity and conductivity of individual tissues obtained from the literature were assigned. Finite element analysis was used to model the AEF strength and distribution in the models with different combinations of head tissues, a virtual tumor, and an electrode pair.Main results.The simulations allowed for a sensitivity analysis of the AEF distribution with respect to different tissue combinations and tissue parameter values.Significance.For a single pair of 5 mm diameter electrodes, an average AEF strength inside the tumor exceeded 1.5 V cm-1, expected to be sufficient for a relevant therapeutic outcome. This study illustrates a robust and flexible approach for simulating AEF in different tissue types, suitable for preclinical studies in rodents and translatable to clinical use.

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy.

Objective. To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP).Approach. Our model provides for selection of preferred ID parameters (Ip) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopicIpand macroscopic RTP. Selection ofIpis demonstrated using published cell survival measurements for protons through argon, comparing results for nineteenIp:Nk,k= 2, 3, …, 10, the number of ionizations in clusters ofkor more per particle, andFk,k= 1, 2, …, 10, the number of clusters ofkor more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation.Main results. The preferredIpwereN4andF5for aerobic cells,N5andF7for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferredNk. Conversely, there was no significant difference forF5for aerobic cells andF7for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for theseIphad the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert thatIpexist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation.Significance. Our model provides a practical means to select preferredIpfrom radiobiological data, and to convertIpto the macroscopic cluster dose for particle RTP.

Single proton LET characterization with the Timepix detector and artificial intelligence for advanced proton therapy treatment planning.

Objective.Protons have advantageous dose distributions and are increasingly used in cancer therapy. At the depth of the Bragg peak range, protons produce a mixed radiation field consisting of low- and high-linear energy transfer (LET) components, the latter of which is characterized by an increased ionization density on the microscopic scale associated with increased biological effectiveness. Prediction of the yield and LET of primary and secondary charged particles at a certain depth in the patient is performed by Monte Carlo simulations but is difficult to verify experimentally.Approach.Here, the results of measurements performed with Timepix detector in the mixed radiation field produced by a therapeutic proton beam in water are presented and compared to Monte Carlo simulations. The unique capability of the detector to perform high-resolution single particle tracking and identification enhanced by artificial intelligence allowed to resolve the particle type and measure the deposited energy of each particle comprising the mixed radiation field. Based on the collected data, biologically important physics parameters, the LET of single protons and dose-averaged LET, were computed.Main results.An accuracy over 95% was achieved for proton recognition with a developed neural network model. For recognized protons, the measured LET spectra generally agree with the results of Monte Carlo simulations. The mean difference between dose-averaged LET values obtained from measurements and simulations is 17%. We observed a broad spectrum of LET values ranging from a fraction of keVµm-1to about 10 keVµm-1for most of the measurements performed in the mixed radiation fields.Significance.It has been demonstrated that the introduced measurement method provides experimental data for validation of LETDor LET spectra in any treatment planning system. The simplicity and accessibility of the presented methodology make it easy to be translated into a clinical routine in any proton therapy facility.

Current status of the preclinical evaluation of alternating electric fields as a form of cancer therapy.

Exposing cancer cells to alternating electric fields of 100-300 kHz frequency and 1-4 V/cm strength has been shown to significantly reduce cancer growth in cell culture and in human patients. This form of anti-cancer therapy is more commonly referred to as tumor treating fields (TTFields), a novel treatment modality that has been approved by the U.S. Food and Drug Administration for use in patients with glioblastoma and malignant pleural mesothelioma. Pivotal trials in other solid organ cancer trials are underway. In regards to overall survival, TTFields alone is comparable to chemotherapy alone in recurrent glioblastoma. However, when combined with adjuvant chemotherapy, TTFields prolong median survival by 4.9 months in newly-diagnosed glioblastoma. TTFields hold promise as a therapeutic approach to numerous solid organ cancers. This review summarizes the current status of TTFields research at the preclinical level, highlighting recent aspects of a relatively complex working hypothesis. In addition, we point out the gaps between limited preclinical in vivo studies and the available clinical data. To date, no customized system for TTFields delivery in rodent models of glioblastoma has been presented. We aim to motivate the expansion of TTFields preclinical research and facilitate the availability of suitable hardware, to ultimately improve outcomes in patients with cancer.

Particle-Tracking Proton Computed Tomography-Data Acquisition, Preprocessing, and Preconditioning.

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography.

Previous work has shown that total variation superiorization (TVS) improves reconstructed image quality in proton computed tomography (pCT). The structure of the TVS algorithm has evolved since then and this paper investigated if this new algorithmic structure provides additional benefits to pCT image quality. Structural and parametric changes introduced to the original TVS algorithm included: (1) inclusion or exclusion of TV reduction requirement, (2) a variable number, N , of TV perturbation steps per feasibility-seeking iteration, and (3) introduction of a perturbation kernel . The structural change of excluding the TV reduction requirement check tended to have a beneficial effect for 3 ≤ N ≤ 6 and allows full parallelization of the TVS algorithm. Repeated perturbations per feasibility-seeking iterations reduced total variation (TV) and material dependent standard deviations for 3 ≤ N ≤ 6 . The perturbation kernel α , effectively equal to α = 0.5 in the original TVS algorithm, reduced TV and standard deviations as α was increased beyond α = 0.5 , but negatively impacted reconstructed relative stopping power (RSP) values for . The reductions in TV and standard deviations allowed feasibility-seeking with a larger relaxation parameter λ than previously used, without the corresponding increases in standard deviations experienced with the original TVS algorithm. This paper demonstrates that the modifications related to the evolution of the original TVS algorithm provide benefits in terms of both pCT image quality and computational efficiency for appropriately chosen parameter values.

Deciphering Biosignatures in Planetary Contexts.

Microbial life permeates Earth's critical zone and has likely inhabited nearly all our planet's surface and near subsurface since before the beginning of the sedimentary rock record. Given the vast time that Earth has been teeming with life, do astrobiologists truly understand what geological features untouched by biological processes would look like? In the search for extraterrestrial life in the Universe, it is critical to determine what constitutes a biosignature across multiple scales, and how this compares with "abiosignatures" formed by nonliving processes. Developing standards for abiotic and biotic characteristics would provide quantitative metrics for comparison across different data types and observational time frames. The evidence for life detection falls into three categories of biosignatures: (1) substances, such as elemental abundances, isotopes, molecules, allotropes, enantiomers, minerals, and their associated properties; (2) objects that are physical features such as mats, fossils including trace-fossils and microbialites (stromatolites), and concretions; and (3) patterns, such as physical three-dimensional or conceptual n-dimensional relationships of physical or chemical phenomena, including patterns of intermolecular abundances of organic homologues, and patterns of stable isotopic abundances between and within compounds. Five key challenges that warrant future exploration by the astrobiology community include the following: (1) examining phenomena at the "right" spatial scales because biosignatures may elude us if not examined with the appropriate instrumentation or modeling approach at that specific scale; (2) identifying the precise context across multiple spatial and temporal scales to understand how tangible biosignatures may or may not be preserved; (3) increasing capability to mine big data sets to reveal relationships, for example, how Earth's mineral diversity may have evolved in conjunction with life; (4) leveraging cyberinfrastructure for data management of biosignature types, characteristics, and classifications; and (5) using three-dimensional to n-D representations of biotic and abiotic models overlain on multiple overlapping spatial and temporal relationships to provide new insights.

Robust Iterative Methods: Convergence and Applications to Proton Computed Tomography.

Robust methods, such as Tikhonov regularization and Bounded data uncertainty, have been used extensively in relatively small problems involving dense matrices for many decades, but have not been used in large-scale iterative methods for image reconstruction in particle imaging until recently. In this case, robust methods may allow more accurate reconstruction of images in the presence of errors of both the energy measurement of the protons and ions but also in the estimated path taken by the proton or ion through the object. Robust systems may also be used when entire blocks of data are missing, or in low-dose reconstructions using a very small number of particles without substantial loss of image quality. In this contribution, we demonstrate that robust methods show great promise in proton/ion (particle) computed tomography (pCT), and, for the first time, that they can be proven to converge. Thus, the convergence of robust methods as well as benefits for reconstruction in uncertain systems is shown to constitute the main advantage for pCT reconstruction.

Microbial Community and Biochemical Dynamics of Biological Soil Crusts across a Gradient of Surface Coverage in the Central Mojave Desert.

In this study, we expand upon the biogeography of biological soil crusts (BSCs) and provide molecular insights into the microbial community and biochemical dynamics along the vertical BSC column structure, and across a transect of increasing BSC surface coverage in the central Mojave Desert, CA, United States. Next generation sequencing reveals a bacterial community profile that is distinct among BSCs in the southwestern United States. Distribution of major phyla in the BSC topsoils included Cyanobacteria (33 ± 8%), Proteobacteria (26 ± 6%), and Chloroflexi (12 ± 4%), with Phormidium being the numerically dominant genus. Furthermore, BSC subsurfaces contained Proteobacteria (23 ± 5%), Actinobacteria (20 ± 5%), and Chloroflexi (18 ± 3%), with an unidentified genus from Chloroflexi (AKIW781, order) being numerically dominant. Across the transect, changes in distribution at the phylum (p < 0.0439) and genus (p < 0.006) levels, including multiple biochemical and geochemical trends (p < 0.05), positively correlated with increasing BSC surface coverage. This included increases in (a) Chloroflexi abundance, (b) abundance and diversity of Cyanobacteria, (b) OTU-level diversity in the topsoil, (c) OTU-level differentiation between the topsoil and subsurface, (d) intracellular ATP abundances and catalase activities, and (e) enrichments in clay, silt, and varying elements, including S, Mn, Co, As, and Pb, in the BSC topsoils. In sum, these studies suggest that BSCs from regions of differing surface coverage represent early successional stages, which exhibit increasing bacterial diversity, metabolic activities, and capacity to restructure the soil. Further, these trends suggest that BSC successional maturation and colonization across the transect are inhibited by metals/metalloids such as B, Ca, Ti, Mn, Co, Ni, Mo, and Pb.

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.

Evaluation of mathematical algorithms for automatic patient alignment in radiosurgery.

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy.

Performance analysis of an optoelectronic localization system for monitoring brain lesioning with proton beams.

Monitoring of the target position relative to the beam delivery system is a crucial requirement for creating small functional lesions in the brain with any radiosurgery modality. We have studied the performance of an optoelectronic localization system for monitoring brain lesioning with narrow proton beams. The system consists of three high-resolution cameras and dedicated software to locate a marker set in space. We tested the accuracy of the system by performing marker distance measurements and monitoring prescribed marker shifts with two different camera configurations and four different calibration techniques. Our results show that the camera-based alignment system appears adequate for the proposed task.