Lung stereotactic body radiation therapy: personalized PTV margins according to tumor location and number of four-dimensional CT scans

4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

BACKGROUND AND OBJECTIVE

In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series.

METHODS

We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation.

RESULTS

The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo.

CONCLUSIONS

Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective.Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.Approach.In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude.Main results.Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim).Significance.This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found athttps://github.com/cyiheng/Dynagan.

Evaluation of Microscopic Tumour Extension in Localized Stage Non-Small-Cell Lung Cancer for Stereotactic Radiotherapy Planning

Background: Stereotactic radiotherapy for localised stage non-small-cell lung carcinoma (NSCLC) is an alternative indication for patients who are inoperable or refuse surgery. A study showed that the microscopic tumour extension (ME) of NSCLC varied according to the histological type, which allowed us to deduce adapted margins for the clinical target volume (CTV). However, to date, no study has been able to define the most relevant margins for patients with stage 1 tumours. Methods: We performed a retrospective analysis including patients with adenocarcinoma (ADC) or squamous cell carcinoma (SCC) of localised stage T1N0 or T2aN0 who underwent surgery. The ME was measured from this boundary. The profile of the type of tumour spread was also evaluated. Results: The margin required to cover the ME of a localised NSCLC with a 95% probability is 4.4 mm and 2.9 mm for SCC and ADC, respectively. A significant difference in the maximum distance of the ME between the tumour-infiltrating lymphocytes (TILs), 0−10% and 50−90% (p < 0.05), was noted for SCC. There was a significant difference in the maximum ME distance based on whether the patient had chronic obstructive pulmonary disease (COPD) (p = 0.011) for ADC. Multivariate analysis showed a statistically significant relationship between the maximum microextension distance and size with the shrinkage coefficient. Conclusion: This study definitively demonstrated that the ME depends on the pathology subtype of NSCLC. According to International Commission on Radiation Units and Measurements (ICRU) reports, 50, 62 and 83 CTV margins, proposed by these results, should be added to the GTV (Gross tumour volume). When stereotactic body radiation therapy is used, this approach should be considered in conjunction with the dataset and other margins to be applied.