Several radiation dose- and time-dependent tissue sequelae develop following acute high-dose radiation exposure. One of the recognized delayed effects of such exposures is lung injury, characterized by respiratory failure as a result of pneumonitis that may subsequently develop into lung fibrosis. Since this pulmonary subsyndrome may be associated with high morbidity and mortality, comprehensive treatment following high-dose irradiation will ideally include treatments that mitigate both the acute hematologic and gastrointestinal subsyndromes as well as the delayed pulmonary syndrome. Currently, there are no drugs approved by the Food and Drug Administration to counteract the effects of acute radiation exposure. Moreover, there are no relevant large animal models of radiation-induced lung injury that permit efficacy testing of new generation medical countermeasures in combination with medical management protocols under the FDA animal rule criteria. Herein is described a nonhuman primate model of delayed lung injury resulting from whole thorax lung irradiation. Rhesus macaques were exposed to 6 MV photon radiation over a dose range of 9.0-12.0 Gy and medical management administered according to a standardized treatment protocol. The primary endpoint was all-cause mortality at 180 d. A comparative multiparameter analysis is provided, focusing on the lethal dose response relationship characterized by a lethal dose50/180 of 10.27 Gy [9.88, 10.66] and slope of 1.112 probits per linear dose. Latency, incidence, and severity of lung injury were evaluated through clinical and radiographic parameters including respiratory rate, saturation of peripheral oxygen, corticosteroid requirements, and serial computed tomography. Gross anatomical and histological analyses were performed to assess radiation-induced injury. The model defines the dose response relationship and time course of the delayed pulmonary sequelae and consequent morbidity and mortality. Therefore, it may provide an effective platform for the efficacy testing of candidate medical countermeasures against the delayed pulmonary syndrome.

Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected.

Characterizing the biological effects of flattening filter-free (FFF) X-ray beams from linear accelerators is of importance, due to their increasing clinical availability. The purpose of this work is to determine whether in vitro cell survival is affected by the higher dose-per-pulse present in FFF beams in comparison with flattened X-ray beams. A Varian TrueBeam(®) linear accelerator was used to irradiate the T98G, V79-4 and U87-MG cell lines with a single fraction of 5 Gy or 10 Gy doses of X-rays. Beams with energies of 6 MegaVolt (MV), 6 MV FFF and 10 MV FFF were used, with doses-per-pulse as measured at the monitor chamber of 0.28, 0.78 and 1.31 mGy/pulse for 6 MV, 6 MV FFF and 10 MV FFF, respectively. The dose delivered to each Petri dish was verified by means of ionization chamber measurements. No statistically significant effects on survival fraction were observed for any of the cell lines considered, either as a function of dose-per-pulse, average dose rate or total dose delivered. Biological effects of higher instantaneous rates should not be excluded on the basis of in vitro experimental results such as the ones presented in this work. The next step toward an assessment of the biological impact of FFF beams will require in vivo studies.

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Purpose We develop and validate a motion model that uses real‐time surface photogrammetry acquired concurrently with four‐dimensional computed tomography (4DCT) to estimate respiration‐induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles. Methods A research, couch‐mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm2) from the thoraco‐abdominal surface of a consented lung SBRT patient, concurrently with their standard‐of‐care 4DCT. The end‐exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase‐averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time‐varying surface and the underlying time‐varying volume. Proof‐of‐concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real‐time surface data and “ground truth” kV fluoroscopic data (FL). A ray‐tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high‐contrast, soft‐tissue, anatomical features in the DRF were compared with those from kV FL. Results For five of the six fluoroscopic acquisition sessions, the model out‐performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy‐derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to −15 mm while 4DCT errors ranged between 5 and −22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal–external correlation resulted in model errors comparable to those of 4DCT. Conclusions We described the development and a proof‐of‐concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time‐varying thoraco‐abdominal surface to the time‐varying internal thoraco‐abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF‐based proof‐of‐concept technique developed here is model‐agnostic, and therefore, has the potential to be used as an in‐patient validation tool for other volumetric motion models.