Localization-based superresolution optical imaging is rapidly gaining popularity, yet limited availability of genetically encoded photoactivatable fluorescent probes with distinct emission spectra impedes simultaneous visualization of multiple molecular species in living cells. We introduce PAmKate, a monomeric photoactivatable far-red fluorescent protein, which facilitates simultaneous imaging of three photoactivatable proteins in mammalian cells using fluorescence photoactivation localization microscopy (FPALM). Successful probe identification was achieved by measuring the fluorescence emission intensity in two distinct spectral channels spanning only ~100 nm of the visible spectrum. Raft-, non-raft-, and cytoskeleton-associated proteins were simultaneously imaged in both live and fixed fibroblasts coexpressing Dendra2-hemagglutinin, PAmKate-transferrin receptor, and PAmCherry1-β-actin fusion constructs, revealing correlations between the membrane proteins and membrane-associated actin structures.
Purpose To develop an online graphic processing unit (GPU)‐accelerated Monte Carlo‐based adaptive radiation therapy (ART) workflow for pencil beam scanning (PBS) proton therapy to address interfraction anatomical changes in patients treated with PBS. Methods and Materials A four‐step workflow was developed using our in‐house developed GPU‐accelerated Monte Carlo‐based treatment planning system to implement online Monte Carlo‐based ART for PBS. The first step conducts diffeomorphic demon‐based deformable image registration (DIR) to propagate contours on the initial planning CT (pCT) to the verification CT (vCT) to form a new structure set. The second step performs forward dose calculation of the initial plan on the vCT with the propagated contours after manual approval (possible modifications involved). The third step triggers a reoptimization of the plan depending on whether the verification dose meets the clinical requirements or not. A robust evaluation will be done for both the verification plan in the second step and the reopotimized plan in the third step. The fourth step involves a two‐stage (before and after delivery) patient‐specific quality assurance (PSQA) of the reoptimized plan. The before‐delivery PSQA is to compare the plan dose to the dose calculated using an independent fast open‐source Monte Carlo code, MCsquare. The after‐delivery PSQA is to compare the plan dose to the dose recalculated using the log file (spot MU, spot position, and spot energy) collected during the delivery. Jaccard index (JI), dice similarity coefficients (DSCs), and Hausdorff distance (HD) were used to assess the quality of the propagated contours in the first step. A commercial plan evaluation software, ClearCheck™, was integrated into the workflow to carry out efficient plan evaluation. 3D Gamma analysis was used during the fourth step to ensure the accuracy of the plan dose from reoptimization. Three patients with three different disease sites were chosen to evaluate the feasibility of the online ART workflow for PBS. Results For all three patients, the propagated contours were found to have good volume conformance [JI (lowest‐highest: 0.833–0.983) and DSC (0.909–0.992)] but suboptimal boundary coincidence [HD (2.37–20.76 mm)] for organs‐at‐risk. The verification dose evaluated by ClearCheck™ showed significant degradation of the target coverage due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality to a clinically acceptable level. 3D Gamma analyses of PSQA confirmed the accuracy of the plan dose before delivery (mean Gamma index = 98.74% with a threshold of 2%/2 mm/10%), and after delivery based on the log files (mean Gamma index = 99.05% with a threshold of 2%/2 mm/10%). The average time cost for the complete execution of the workflow was around 858 s, excluding the time for manual intervention. Conclusion The proposed online ART workflow for PBS was demonstrated to be efficient and effective by generating a reoptimized plan that significantl...
Background Cone‐beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT). Purpose This study aims to characterize iCBCT image quality, establish image value (HU)‐to‐relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites. Material and methods Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On‐Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp–Davis–Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site‐specific HU‐RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK‐based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison. Results Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU‐RED calibration from CT showed 0.5%–2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site‐specific HU‐RED calibration curves. Conclusion iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU‐RED calibration curve from CT.
Personal dosimeters are critical tools to ensure environmental and occupational health and safety. Inherent to all wearable dosimeters is a compliance risk because they can be removed from a person accidentally or intentionally (and thus be misplaced, forgotten, or tampered with) or can cause discomfort. Intradermal smart tattoos are nanoscale implants that overcome these limitations because they can be permanent and comfortable within the skin. Here, we demonstrate a proof of concept for reversible intradermal colorimetric γ-ray dosimeters comprising polystyrene nanoparticles doped with photoresponsive dithienylethenes. We speculate that smart tattoos sensing dangerous radiation may improve convenience and compliance in personal dosimetry applications.
To investigate the dependency of dose-volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators. Methods: DVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any "stair step" behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology. Results: Calculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%. Conclusions: Commercial dose-evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer. K E Y W O R D S dose-volume histogram, DVH analysis, treatment planning systems
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