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This paper presents a practical method for converting dose measured with thermoluminescent dosimeters (TLD) to dose in lung and bone for 6 MV and 15 MV photon beams. Monte Carlo (MC) simulations and Burlin cavity theory calculations were performed to calculate [Formula: see text], the dose-to-TLD to dose-to-medium conversion factor. A practical method was proposed for converting TLD-measured-dose to dose-in-medium using the TLD dose calibration in water and [Formula: see text] dose-to-medium to dose-to-water conversion factor. Theoretical calculations for [Formula: see text] were performed using photon spectrum weighted parameters and were compared with MC simulations. Verification of the proposed method was done using phantoms having either bone or lung equivalent slabs stacked in between solid water slabs. Percent depth dose (PDD) curves were measured using 0.089 cm thick LiF:Mg,Ti (TLD-100) dosemeters placed at various depths within these phantoms. They were then corrected with [Formula: see text] factors using the proposed dose conversion method, and were compared with the MC simulations. For 6 MV beam, the MC calculated [Formula: see text] factors were 0.942 and 1.002 for bone and lung, and for 15 MV it was 0.927 and 1.005 for bone and lung, respectively. The difference between the MC simulated and spectrum weighted theoretical [Formula: see text] factors were within 3% for both lung and bone. The PDD curves measured with TLD-100 chips that were corrected using the proposed method agreed well within 1.5% of the MC simulated PDD curves for both the water/lung/water and water/bone/water (WBW) phantoms. The dose-to-medium correction using MC simulated [Formula: see text] is convenient, easy, and accurate. Therefore, it can be used instead of Burlin cavity theory, especially in media with high atomic numbers such as bone for accurate dose quantification.

To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. The results after VM-CBCT production showed good material decomposition and consistent HU VM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0%  ±  1.8%. This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.

Purpose: To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm., Methods: A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported., Results: In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air., Conclusion: These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied., (© 2017 American Association of Physicists in Medicine.)

Objective: To evaluate a template-based matching algorithm on single-energy (SE) and dual-energy (DE) radiographs for markerless localization of lung tumours., Methods: A total of 74 images from 17 patients with Stages IA-IV lung cancer were considered. At the time of radiotherapy treatment, gated end-expiration SE radiographs were obtained at 60 and 120 kVp at different gantry angles (33° anterior and 41° oblique), from which soft-tissue-enhanced DE images were created. A template-based matching algorithm was used to localize individual tumours on both SE and DE radiographs. Tumour centroid co-ordinates obtained from the template-matching software on both SE and DE images were compared with co-ordinates defined by physicians., Results: The template-based matching algorithm was able to successfully localize the gross tumor volume within 5 mm on 70% (52/74) of the SE images vs 91% (66/74) of the DE images (p < 0.01). The mean vector differences between the co-ordinates of the template matched by the algorithm and the co-ordinates of the physician-defined ground truth were 3.2 ± 2.8 mm for SE images vs 2.3 ± 1.7 mm for DE images (p = 0.03)., Conclusion: Template-based matching on DE images was more accurate and precise than using SE images. Advances in knowledge: This represents, to the authors' knowledge, the largest study evaluating template matching on clinical SE and DE images, considering not only anterior gantry angles but also oblique angles, suggesting a novel lung tumour matching technique using DE subtraction that is reliable, accurate and precise.

Purpose and Objectives: To quantify, through an observer study, the reduction in metal artifacts on cone beam computed tomographic (CBCT) images using a projection-interpolation algorithm, on images containing metal artifacts from dental fillings and implants in patients treated for head and neck (H&N) cancer., Methods and Materials: An interpolation-substitution algorithm was applied to H&N CBCT images containing metal artifacts from dental fillings and implants. Image quality with respect to metal artifacts was evaluated subjectively and objectively. First, 6 independent radiation oncologists were asked to rank randomly sorted blinded images (before and after metal artifact reduction) using a 5-point rating scale (1 = severe artifacts; 5 = no artifacts). Second, the standard deviation of different regions of interest (ROI) within each image was calculated and compared with the mean rating scores., Results: The interpolation-substitution technique successfully reduced metal artifacts in 70% of the cases. From a total of 60 images from 15 H&N cancer patients undergoing image guided radiation therapy, the mean rating score on the uncorrected images was 2.3 ± 1.1, versus 3.3 ± 1.0 for the corrected images. The mean difference in ranking score between uncorrected and corrected images was 1.0 (95% confidence interval: 0.9-1.2, P<.05). The standard deviation of each ROI significantly decreased after artifact reduction (P<.01). Moreover, a negative correlation between the mean rating score for each image and the standard deviation of the oral cavity and bilateral cheeks was observed., Conclusion: The interpolation-substitution algorithm is efficient and effective for reducing metal artifacts caused by dental fillings and implants on CBCT images, as demonstrated by the statistically significant increase in observer image quality ranking and by the decrease in ROI standard deviation between uncorrected and corrected images., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: To evaluate the efficacy of dual-energy (DE) vs single-energy (SE) fluoroscopic imaging of lung tumors using a markerless template-based tracking algorithm., Methods: Ten representative patient breathing patterns were programmed into a Quasar™ motion phantom. The phantom was modified by affixing pork ribs to the surface, and a cedar insert with a small spherical volume was used to simulate lung and tumor, respectively. Sequential 60 kVp (6 mA) and 120 kVp (1.5 mA) fluoroscopic sequences were acquired. Frame-by-frame weighted logarithmic subtraction was performed resulting in a DE fluoroscopic sequence. A template-based algorithm was then used to track tumor motion throughout the DE and SE fluoroscopy sequences. Tracking coordinates were evaluated against ground-truth tumor locations. Fluoroscopic images were also acquired for two lung cancer patients, neither of which had implanted fiducials., Results: For phantom imaging, a total of 1925 frames were analyzed. The algorithm successfully tracked the target on 99.9% (1923/1925) of DE frames vs 90.7% (1745/1925) SE images (p < 0.01). The displacement between tracking coordinates and ground truth for the phantom was 1.4 mm ± 1.1 mm for DE vs 2.0 mm ± 1.3 mm for SE (p < 0.01). Images from two patients, one with a larger tumor and one with a smaller tumor, were also analyzed. For the patient with the larger tumor, the average displacement from physician defined ground truth was 1.2 mm ± 0.6 mm for DE vs 1.4 mm ± 0.7 mm for SE (p = 0.016). For the patient that presented with a smaller tumor, the average displacement from physician defined ground truth was 2.2 mm ± 1.0 mm for DE vs 3.2 mm ± 1.4 mm for SE (p < 0.01). Importantly, for this single patient with the smaller tumor, 15.6% of the SE frames had >5 mm displacements from the ground truth vs 0% for DE fluoroscopy., Conclusions: This work indicates the potential for markerless tumor tracking utilizing DE fluoroscopy. With DE imaging, the algorithm showed improved detectability vs SE fluoroscopy and was able to accurately track the tumor in nearly all cases.