In this paper, a new image reconstruction scheme is presented based on Tuy's cone-beam inversion scheme and its fan-beam counterpart. It is demonstrated that Tuy's inversion scheme may be used to derive a new framework for fanbeam and cone-beam image reconstruction. In this new framework, images are reconstructed via filtering the backprojection image of differentiated projection data. The new framework is mathematically exact and is applicable to a general source trajectory provided the Tuy data sufficiency condition is satisfied. By choosing a piece-wise constant function for one of the components in the factorized weighting function, the filtering kernel is one dimensional, viz. the filtering process is along a straight line. Thus, the derived image reconstruction algorithm is mathematically exact and efficient. In the cone-beam case, the derived reconstruction algorithm is applicable to a large class of source trajectories where the pi-lines or the generalized pi-lines exist. In addition, the new reconstruction scheme survives the super-short scan mode in both the fan-beam and cone-beam cases provided the data are not transversely truncated. Numerical simulations were conducted to validate the new reconstruction scheme for the fan-beam case.

Background: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose. Methods: We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%. Findings: Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0–24.9) and 5.7% (95% CI: 3.5–8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02–2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67–0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69–0.92]). iGray had a wide dose range (21.1–277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases. Interpretation: Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

Our results demonstrate an increased rate of local failure in patients with squamous cell carcinoma. Standard approaches for radiotherapy that demonstrate efficacy for a population may not achieve optimal results for individual patients. Establishing the differential dose effect of SBRT across histological groups is likely to improve efficacy and inform ongoing and future studies that aim to expand indications for SBRT.

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Stereotactic body radiation therapy (SBRT) has been increasingly used as an efficacious treatment modality for early‐stage non‐small cell lung cancer. The accuracy of dose calculations is compromised due to the presence of inhomogeneity. For the purpose of a consistent prescription, radiation doses were calculated without heterogeneity correction in several RTOG trials. For patients participating in these trials, recalculations of the planned doses with more accurate dose methods could provide better correlations between the treatment outcomes and the planned doses. Using a Monte Carlo (MC) dose calculation algorithm as a gold standard, we compared the recalculated doses with the MC algorithm to the original pencil beam (PB) calculations for our institutional clinical lung SBRT plans. The focus of this comparison is to investigate the volume and location dependence on the differences between the two dose calculations. Thirty‐one clinical plans that followed RTOG and other protocol guidelines were retrospectively investigated in this study. Dosimetric parameters, such as D1, D95, and D99 for the PTV and D1 for organs at risk, were compared between two calculations. Correlations of mean lung dose and V20 of lungs between two calculations were investigated. Significant dependence on tumor size and location was observed from the comparisons between the two dose calculation methods. When comparing the PB calculations without heterogeneity correction to the MC calculations with heterogeneity correction, we found that in terms of D95 of PTV: (1) the two calculations resulted in similar D95 for edge tumors with volumes greater than 25.1 cc; (2) an average overestimation of 5% in PB calculations for edge tumors with volumes less than 25.1 cc; and (3) an average overestimation of 9% or underestimation of 3% in PB calculations for island tumors with volumes smaller or greater than 22.6 cc, respectively. With heterogeneity correction, the PB calculations resulted in an average reduction of 23.8% and 15.3% in the D95 for the PTV for island and edge lesions, respectively, when compared to the MC calculations. For organs at risks, very small differences were found among all the comparisons. Excellent correlations for mean dose and V20 of lungs were observed between the two calculations. This study demonstrated that using a single scaling factor may be overly simplified when accounting for the effects of heterogeneity correction. Accurate dose calculations, such as the Monte Carlo algorithms, are highly recommended to understand dose responses in lung SBRT.PACS number: 87.53.Ly

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In this paper, we present a new algorithm designed for a specific data truncation problem in fan-beam CT. We consider a scanning configuration in which the fan-beam projection data are acquired from an asymmetrically positioned half-sized detector. Namely, the asymmetric detector only covers one half of the scanning field of view. Thus, the acquired fan-beam projection data are truncated at every view angle. If an explicit data rebinning process is not invoked, this data acquisition configuration will reek havoc on many known fan-beam image reconstruction schemes including the standard filtered backprojection (FBP) algorithm and the super-short-scan FBP reconstruction algorithms. However, we demonstrate that a recently developed fan-beam image reconstruction algorithm which reconstructs an image via filtering a backprojection image of differentiated projection data (FBPD) survives the above fan-beam data truncation problem. Namely, we may exactly reconstruct the whole image object using the truncated data acquired in a full scan mode (2pi angular range). We may also exactly reconstruct a small region of interest (ROI) using the truncated projection data acquired in a short-scan mode (less than 2pi angular range). The most important characteristic of the proposed reconstruction scheme is that an explicit data rebinning process is not introduced. Numerical simulations were conducted to validate the new reconstruction algorithm.

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