Techniques for preclinical intensity modulated radiation therapy are being developed to improve translation by replicating the clinical paradigm. This study presents the first treatment planning comparison between small animal IMRT (SA-IMRT) and three-dimensional conformal radiotherapy (CRT) in a model application, oxygen-guided dose painting of tumor hypoxia, using actual mouse data. A novel compensator-based platform was employed to generate SA-IMRT and CRT plans with 2-15 beam angles for seventeen mice with fibrosarcoma tumors. The whole tumor received a dose of 22.5 Gy, with a simultaneous integrated boost of 13 Gy to hypoxic voxels identified via electron paramagnetic resonance imaging. Plan quality was assessed using the Paddick conformity index (CI), uniformity, and dose volume histograms. For 3-angles, SA-IMRT yielded significantly improved dose conformity (median hypoxic CI = 0.45 vs. 0.17), tumor dose uniformity (11.0% vs. 14.3%), and dosimetric spread between boost and non-boost targets (D50% difference = 13.0 Gy [ideal], 13.1 Gy [SA-IMRT], 7. 3 Gy [CRT]). No significant improvement in CI was associated with > 3 beam angles (Wilcoxon signed-rank test, p < 0.05). This study demonstrates that SA-IMRT provides significant improvements in radiation plan quality and yields dose distributions that more closely mimic the clinical setting relative to current CRT approaches.

Purpose: This project aimed to develop a stereotactic radiosurgery mouse model of radiation-induced brain injury to study endothelial damage within the neurovascular unit (NVU). Classification of focal endothelial damage and the resulting inflammatory cascade has never been cataloged for assessing intrinsic changes versus extrinsic factors of inflammation at the NVU. The study of endothelial damage with invasive models, such as open transcranial injury or vessel occlusion, introduces multiple variables into the system that our model can help overcome to study secondary or systemic insults of the NVU. Methods: Using an X-ray image-guided radiation therapy system, mice were focally irradiated with 30, 45, and 70 Gy to a 5 mm brain target. Mice were followed up at 2, 4, 6, 12 and 18 weeks after irradiation with contrast-enhanced CT imaging for volumetric quantification of radiation effects. Brains were harvested for characterization by histology and immune-multiplexing of reactionary and inflammatory changes to the NVU. Whole coronal slides were scanned and analyzed using machine learning classifiers to quantify cell types within the NVU in the radiated vs. control hemispheres. Results: Radiation-induced changes such as telangiectasias, vessel hyalinization, and white matter necrosis were observed. An inflammatory response with a predominance of T cells was observed, as well as an increase in microglial cells and reactive astrogliosis. These changes were time and dose-dependent after radiation. Serial CTs allowed for determining the development of radiation damage, and the contrast enhancement was proportional to the degree of radiation-induced changes. Conclusions: This radiation model allows precise image-guided targeting with a steep radiation fall-off into surrounding tissues. The NVU’s response to focal insult of endothelial damage presents a novel method of brain injury to specifically study diseases associated with endothelial dysfunction.

[This corrects the article DOI: 10.3389/fmed.2023.1269689.].

Purpose: Deep learning (DL) applications strongly depend on the training dataset and convolutional neural network architecture; however, it is unclear how to objectively select such parameters. We investigate the classification performance of different DL models and training schemes for the anatomic classification of cone-beam computed tomography (CBCT) projections. Approach: CBCT scans from 1055 patients were collected and manually classified into five anatomic classes and used to develop DL models to predict the anatomic class from single x-ray projections. VGG-16, Xception, and Inception v3 architectures were trained with 75% of the data, and the remaining 25% was used for testing and evaluation. To study the dependence of the classification performance on dataset size, training data was downsampled to various dataset sizes. Gradient-weighted class activation maps (grad-CAM) were generated using the model with highest classification performance, to identify regions with strong influence on CNN decisions. Results: The highest precision and recall values were achieved with VGG-16. One of the best performing combinations was the VGG-16 trained with 90 deg projections (mean class precision = 0.87). The training dataset size could be reduced to similar to 50% of its initial size, without compromising the classification performance. For correctly classified cases, Grad-CAM were more heavily weighted for anatomically relevant regions. Conclusions: It was possible to determine those dependencies with a higher influence on the classification performance of DL models for the studied task. Grad-CAM enabled the identification of possible sources of class confusion. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

We evaluated the impact of respiratory motion on the lung dose during linac-based intensity-modulated total marrow irradiation (IMTMI) using two different approaches: (1) measurement of doses within the lungs of an anthropomorphic phantom using thermoluminescent detectors (TLDs) and (2) treatment delivery measurements using ArcCHECK where gamma passing rates (GPRs) and the mean lung doses were calculated and compared with and without motion. In the first approach, respiratory motions were simulated using a programmable motion platform by using typical published peak-to-peak motion amplitudes of 5, 8, and 12 mm in the craniocaudal (CC) direction, denoted here as M1, M2, and M3, respectively, with 2 mm in both anteroposterior (AP) and lateral (LAT) directions. TLDs were placed in five selected locations in the lungs of a RANDO phantom. Average TLD measurements obtained with motion were normalized to those obtained with static phantom delivery. The mean dose ratios were 1.01 (0.98–1.03), 1.04 (1.01–1.09), and 1.08 (1.04–1.12) for respiratory motions M1, M2, and M3, respectively. To determine the impact of directional respiratory motion, we repeated the experiment with 5-, 8-, and 12-mm motion in the CC direction only. The differences in average TLD doses were less than 1% when compared with the M1, M2, and M3 motions indicating a minimal impact from CC motion on lung dose during IMTMI. In the second experimental approach, we evaluated extreme respiratory motion 15 mm excursion in only the CC direction. We placed an ArcCHECK device on a commercial motion platform and delivered the clinical IMTMI plans of five patients. We compared, with and without motion, the dose volume histograms (DVHs) and mean lung dose calculated with the ArcCHECK-3DVH tool as well as GPR with 3%, 5%, and 10% dose agreements and a 3-mm constant distance to agreement (DTA). GPR differed by 11.1 ± 2.1%, 3.8 ± 1.5%, and 0.1 ± 0.2% with dose agreement criteria of 3%, 5%, and 10%, respectively. This indicates that respiratory motion impacts dose distribution in small and isolated parts of the lungs. More importantly, the impact of respiratory motion on the mean lung dose, a critical indicator for toxicity in IMTMI, was not statistically significant (p > 0.05) based on the Student’s t-test. We conclude that most patients treated with IMTMI will have negligible dose uncertainty due to respiratory motion. This is particularly reassuring as lung toxicity is the main concern for future IMTMI dose escalation studies.

Purpose: There is growing interest in the use of modern 3D printing technology to implement intensity-modulated radiation therapy (IMRT) on the preclinical scale which is analogous to clinical IMRT. However, current 3D-printed IMRT methods suffer from long delivery times, excess filament usage, and complex modulation patterns. In this work, we have developed a total variation regularization (TVR) approach to address these issues. Methods: TVR-IMRT, a technique designed to minimize the intensity difference between neighboring beamlets, was used to optimize the beamlet intensity map, which was then converted to corresponding compensator thicknesses in copper-doped PLA filament. IMRT and TVR-IMRT plans using five beams were generated to treat a mouse heart while sparing lung tissue. The individual field doses and composite dose were delivered to film and compared to the corresponding planned doses using gamma analysis. Results: TVR-IMRT reduced the total variation of both the beamlet intensities and compensator thicknesses around 50 when compared to standard 3D printed compensator IMRT. The total mass of compensator material consumed and radiation beam-on time were reduced by 20-30%, while DVHs remained comparable. Gamma analysis passing rate with 3%/0.3mm criterion was 89.07% for IMRT and 95.37% for TVR-IMRT. Conclusion: TVR can be applied to small animal IMRT in order to produce fluence maps and subsequent 3D-printed compensator patterns with less total variation, facilitating 3D printing and reducing the amount of filament required. The TVR-IMRT plan required less beam-on time while maintaining the dose conformity when compared to a traditional IMRT plan.

Deep learning (DL) applications strongly depend on the training dataset size; however, it is unclear how to objectively select this parameter. This work investigates the dependence of the classification performance of a DL model on the dataset size for the anatomic classification of cone-beam computed tomography (CBCT) projections. CBCT scans from 1055 patients were collected and manually classified into 5 anatomic classes and used to develop a DL model to predict the anatomic class from 0º x-ray projections. A VGG-16 convolutional neural network (CNN) was trained with 75% of the data, while the remaining 25% was used for testing and evaluation. The training dataset was downsampled to various sizes. The training dataset size could be reduced to ~50% of its initial size, without compromising the classification performance.

Checkpoint blockade elicits durable responses in immunogenic cancers, but it is largely ineffective in immunologically ‘cold’ tumours. Here we report the design, synthesis and performance of a bismuth-based nanoscale metal–organic framework that modulates the immunological and mechanical properties of the tumour microenvironment for enhanced radiotherapy–radiodynamic therapy. In mice with non-immunogenic prostate and pancreatic tumours irradiated with low X-ray doses, the intratumoural injection of the radiosensitizer mediated potent outcomes via the repolarization of immunosuppressive M2 macrophages into immunostimulatory M1 macrophages, the reduction of the concentration of intratumoural transforming growth factor beta (TGF-β) and of collagen density, and the inactivation of cancer-associated fibroblasts. When intravenously injected in combination with checkpoint-blockade therapy, the radiosensitizer mediated the reversal of immunosuppression in primary and distant tumours via the systemic reduction of TGF-β levels, which led to the downregulation of collagen expression, the stimulation of T-cell infiltration in the tumours and a robust abscopal effect. Nanoscale radiosensitizers that stimulate anti-tumour immunity and T-cell infiltration may enhance the therapeutic outcomes of checkpoint blockade in other tumour types. A nanoscale metal–organic framework that modulates the immunological and mechanical properties of the tumour microenvironment enhances the outcomes of radiotherapy–radiodynamic therapy, especially when combined with checkpoint blockade.

Purpose Inkjet printers can be used to fabricate anthropomorphic phantoms by the use of iodine-doped ink. However, challenges persist in implementing this technique. The calibration from grayscale to ink density is complex and time-consuming. The purpose of this work is to develop a printing methodology that requires a simpler calibration and is less dependent on printer characteristics to produce the desired range of x-ray attenuation values. Methods Conventional grayscale printing was substituted by single-tone printing; that is, the superposition of pure black layers of iodinated ink. Printing was performed with a consumer-grade inkjet printer using ink made of potassium-iodide (KI) dissolved in water at 1 g/ml. A calibration for the attenuation of ink was measured using a commercial x-ray system at 70 kVp. A neonate radiograph obtained at 70 kVp served as an anatomical model. The attenuation map of the neonate radiograph was processed into a series of single-tone images. Single-tone images were printed, stacked, and imaged at 70 kVp. The phantom was evaluated by comparing attenuation values between the printed phantom and the original radiograph; attenuation maps were compared using the structural similarity index measure (SSIM), while attenuation histograms were compared using the Kullback-Leibler (KL) divergence. A region of interest (ROI)-based analysis was also performed, where the attenuation distribution within given ROIs was compared between phantom and patient. The phantom sharpness was evaluated in terms of modulation transfer function (MTF) estimates and signal spread profiles of high spatial resolution features in the image. Results The printed phantom required 36 pages. The printing queue was automated and it took about 2 h to print the phantom. The radiograph of the printed phantom demonstrated a close resemblance to the original neonate radiograph. The SSIM of the phantom with respect to that of the patient was 0.53. Both patient and phantom attenuation histograms followed similar distributions, and the KL divergence between such histograms was 0.20. The ROI-based analysis showed that the largest deviations from patient attenuation values were observed at the higher and lower ends of the attenuation range. The limiting resolution of the proposed methodology was about 1 mm. Conclusion A methodology to generate a neonate phantom for 2D imaging applications, using single-tone printing, was developed. This method only requires a single-value calibration and required less than 2 h to print a complete phantom.

PurposePreclinical radiation replicating clinical intensity modulated radiation therapy (IMRT) techniques can provide data translatable to clinical practice. For this work, treatment plans were created for oxygen-guided dose-painting in small animals using inverse-planned IMRT. Spatially varying beam intensities were achieved using 3-dimensional (3D)-printed compensators.Methods and MaterialsOptimized beam fluence from arbitrary gantry angles was determined using a verified model of the XRAD225Cx treatment beam. Compensators were 3D-printed with varied thickness to provide desired attenuation using copper/polylactic-acid. Spatial resolution capabilities were investigated using printed test-patterns. Following American Association of Physicists in Medicine TG119, a 5-beam IMRT plan was created for a miniaturized (∼1/8th scale) C-shape target. Electron paramagnetic resonance imaging of murine tumor oxygenation guided simultaneous integrated boost (SIB) plans conformally treating tumor to a base dose (Rx1) with boost (Rx2) based on tumor oxygenation. The 3D-printed compensator intensity modulation accuracy and precision was evaluated by individually delivering each field to a phantom containing radiochromic film and subsequent per-field gamma analysis. The methodology was validated end-to-end with composite delivery (incorporating 3D-printed tungsten/polylactic-acid beam trimmers to reduce out-of-field leakage) of the oxygen-guided SIB plan to a phantom containing film and subsequent gamma analysis.ResultsResolution test-patterns demonstrate practical printer resolution of ∼0.7 mm, corresponding to 1.0 mm bixels at the isocenter. The miniaturized C-shape plan provides planning target volume coverage (V95% = 95%) with organ sparing (organs at risk Dmax < 50%). The SIB plan to hypoxic tumor demonstrates the utility of this approach (hypoxic tumor V95%,Rx2 = 91.6%, normoxic tumor V95%,Rx1 = 95.7%, normal tissue V100%,Rx1 = 7.1%). The more challenging SIB plan to boost the normoxic tumor rim achieved normoxic tumor V95%,Rx2 = 90.9%, hypoxic tumor V95%,Rx1 = 62.7%, and normal tissue V100%,Rx2 = 5.3%. Average per-field gamma passing rates using 3%/1.0 mm, 3%/0.7 mm, and 3%/0.5 mm criteria were 98.8% ± 2.8%, 96.6% ± 4.1%, and 90.6% ± 5.9%, respectively. Composite delivery of the hypoxia boost plan and gamma analysis (3%/1 mm) gave passing results of 95.3% and 98.1% for the 2 measured orthogonal dose planes.ConclusionsThis simple and cost-effective approach using 3D-printed compensators for small-animal IMRT provides a methodology enabling preclinical studies that can be readily translated into the clinic. The presented oxygen-guided dose-painting demonstrates that this methodology will facilitate studies driving much needed biologic personalization of radiation therapy for improvements in patient outcomes.

BACKGROUND:Some patients cannot be imaged with cone-beam CT for image-guided radiation therapy because their size, pose, or fixation devices cause collisions with the machine. OBJECTIVE:To investigate imaging trajectories that avoid such collisions by using virtual isocenter and variable magnification during acquisition while yielding comparable image quality. METHODS:The machine components most likely to collide are the gantry and kV detector. A virtual isocenter trajectory continuously moves the patient during gantry rotation to maintain an increased separation between the two. With dynamic magnification, the kV detector is dynamically moved to increase clearance for an angular range around the potential collision point while acquiring sufficient data to maintain the field-of-view. Both strategies were used independently and jointly with the resultant image quality evaluated against the standard circular acquisition. RESULTS:Collision avoiding trajectories show comparable contrast and resolution to standard techniques. For an anthropomorphic phantom, the RMSE is <7×10- 4, multi-scale structural similarity index is >0.97, and visual image fidelity is >0.96 for all trajectories when compared to a standard circular scan. CONCLUSIONS:The proposed trajectories avoid machine-patient collisions while providing comparable image quality to the current standard thereby enabling CBCT imaging for patients that could not otherwise be scanned.

Flat panel detectors remain a new and emerging technology in under-table fluoroscopy systems. This technology is more susceptible than image intensifiers to electronic noise, which degrades image contrast resolution. Compensation for increased electronic noise is provided through proprietary vendor image processing algorithms. Lacking optimization in pediatrics, these algorithms interfere with patient anatomy particularly in neonate patients with low native anatomic contrast from bony structures, which serve as landmarks during fluoroscopic procedures. Existing phantoms do not adequately mimic the neonate anatomy making assessment and optimization of image quality for these patients difficult if not impossible. This work presents a method to inexpensively print iodine based anthropomorphic phantoms derived from patient radiographs with sufficient anatomic detail to assess system image quality. First, the attenuation of iodine ink densities (IA) was correlated to a standard pixel value grayscale map. Next, for proof-of-principle, radiographs of an anthropomorphic chest phantom were developed into a series of iodine ink printed sheets. Sheets were stacked to build a compact 2D phantom matching the x-ray attenuation of the original radiographs. The iodine ink printed phantom was imaged and attenuation values per anatomical regions of interest were compared. This study provides the fundamentals and techniques of phantom construction, enabling generation of anatomically realistic phantoms for a variety of patient age and size groups by use of clinical radiographs. Future studies will apply these techniques to generate neonatal phantoms from radiographs. These phantoms provide realistic imaging challenges to enable optimization of image quality in fluoroscopy and other projection-based x-ray modalities.

Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp-Davis-Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration.

BACKGROUND:Patient dose from image guidance in radiotherapy is small compared to the treatment dose. However, the imaging beam is untargeted and deposits dose equally in tumor and healthy tissues. It is desirable to minimize imaging dose while maintaining efficacy.OBJECTIVE:Image guidance typically does not require full image quality throughout the patient. Dynamic filtration of the kV beam allows local control of CT image noise for high quality around the target volume and lower quality elsewhere, with substantial dose sparing and reduced scatter fluence on the detector.METHODS:The dynamic Intensity-Weighted Region of Interest (dIWROI) technique spatially varies beam intensity during acquisition with copper filter collimation. Fluence is reduced by 95% under the filters with the aperture conformed dynamically to the ROI during cone-beam CT scanning. Preprocessing to account for physical effects of the collimator before reconstruction is described.RESULTS:Reconstructions show image quality comparable to a standard scan in the ROI, with higher noise and streak artifacts in the outer region but still adequate quality for patient localization. Monte Carlo modeling shows dose reduction by 10-15% in the ROI due to reduced scatter, and up to 75% outside.CONCLUSIONS:The presented technique offers a method to reduce imaging dose by accepting increased image noise outside the ROI, while maintaining full image quality inside the ROI.

Spectral CT adds an additional dimension of energy to the conventional CT imaging. As a result, more than one images are usually to be reconstructed. Conventionally, these images are reconstructed separately as isolated inverse problems, requiring minimum effort in adapting existing reconstruction algorithm. Joint reconstruction, on the other hand, takes advantages of the correlation among the images and seems to be more robust and less demanding on the scanning configuration. In this study, we develop an one-step optimization-based reconstruction method with regularization for the basis images. In a simulation study with a dual kVp scan consisting of two sequential limited-angle acquisition, the results have suggested that the method with the regularization improves the basis images by reducing the crosstalk in the bone regions and rendering more uniform textures in soft tissue regions.

PURPOSE This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient's treatment position and allow for its modification if necessary. METHODS A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the skanect software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0°, while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in pinnacle, and this information was exported to AlignRT (VisionRT, London, UK)--a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. RESULTS Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation -1.2°). The accuracy study for the Kinect-Skanect surface showed an average discrepancy between the CT external contour and the surface scan of 2.2 mm. CONCLUSIONS This methodology provides fast and reliable collision predictions using surface imaging. The use of the Kinect-Skanect system allows for a comprehensive modeling of the patient topography including all the relevant anatomy and immobilization devices that may lead to collisions. The use of this tool at the treatment simulation stage may allow therapists to evaluate the clearance of a patient's treatment position and optimize it before the planning CT scan is performed. This can allow for safer treatments for the patients due to better collision predictions and improved clinical workflow by minimizing replanning and resimulations due to unforeseen clearance issues.