Quantification of the kV X-ray imaging dose during real-time tumor tracking and from three- and four-dimensional cone-beam computed tomography in lung cancer patients using a Monte Carlo simulation

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images

OBJECTIVES

To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration.

METHODS

Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset.

RESULTS

The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively.

CONCLUSIONS

A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion.

ADVANCES IN KNOWLEDGE

The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images

PURPOSE

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images.

METHODS

Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s.

RESULTS

The 3D prediction error within 3 mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3 mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT .

CONCLUSION

PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images

PURPOSE

The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment.

METHODS

Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50 Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model.

RESULTS

The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm³ and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85 ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5 mm was 93.6%.

CONCLUSIONS

The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

Deep learning-based synthetization of real-time in-treatment 4D images using surface motion and pretreatment images: A proof-of-concept study

PURPOSE

To develop a deep learning model that maps body surface motion to internal anatomy deformation, which is potentially applicable to dose-free real-time 4D virtual image-guided radiotherapy based on skin surface data.

METHODS

Body contours were segmented out of 4DCT images. Deformable image registration algorithm was used to register the end-of-exhalation (EOE) phase to other phases. Deformation vector field was dimension-reduced to the first two principal components (PCs). A deep learning model was trained to predict the two PC scores of each phase from surface displacement. The instant deformation field can then be reconstructed, warping EOE image to obtain real-time CT image. This approach was validated on 4D XCAT phantom, the public DIR-Lab, and 4D-Lung dataset respectively, with and without simulated noise.

RESULTS

Validation accuracy of the tumor centroid trajectory was observed as 0.04 ± 0.02 mm on XCAT phantom. For the DIR-Lab dataset, 300 landmarks were annotated on the end-of-inhalation (EOI) images of each patient, and the mean displacements between their predicted and reference positions were below 2 mm for all studied cases. For the 4D-Lung dataset, the average dice coefficients ± std between predicted and reference tumor contours at EOI phase were 0.835 ± 0.092 for all studied cases.

CONCLUSIONS

A deep learning-based approach was proposed and validated to predict internal anatomy deformation from the surface motion, which is potentially applicable to on-line target navigation for accurate radiotherapy based on real-time 4D skin surface data and pretreatment images.

A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model

PURPOSE

Due to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work.

METHODS

A patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under different clinical parametric combinations.

RESULTS

Real 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66 mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2 mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention.

CONCLUSIONS

The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.

Contribution of Imaging to Organs at Risk Dose during Lung Stereotactic Body Radiation Therapy

Background:

The use of imaging is indispensable in modern radiation therapy, both for simulation and treatment delivery. For safe and sure utilization, dose delivery from imaging must be evaluated.

Objective:

This study aims to investigate the dose to organ at risk (OAR) delivered by imaging during lung stereotactic body radiation therapy (SBRT) and to evaluate its contribution to the treatment total dose.

Material and Methods:

In this retrospectively study, imaging total dose to organs at risk (OARs) (spinal cord, esophagus, lungs, and heart) and effective dose were retrospectively evaluated from 100 consecutive patients of a single institution who had lung SBRT. For each patient, dose was estimated using Monte-Carlo convolution for helical computed tomography (helical CT), Four-Dimensional CT (4D-CT), and kilovoltage Cone-Beam CT (kV-CBCT). Helical CT and kV-CBCT dose were evaluated for the entire thorax acquisition, while 4D-CT dose was analyzed on upper lobe (UL) or lower lobe (LL) acquisition. Treatment dose was extracted from treatment planning system and compared to imaging total dose.

Results:

Imaging total dose maximum values were 117 mGy to the spinal cord, 127 mGy to the esophagus, 176 mGy to the lungs and 193 mGy to the heart. The maximum effective dose was 19.65 mSv for helical CT, 10.62 mSv for kV-CBCT, 25.95 mSv and 38.45 mSv for 4D-CT in UL and LL regions, respectively. Depending on OAR, treatment total dose was higher from 1.7 to 8.2 times than imaging total dose. Imaging total dose contributed only to 0.3% of treatment total dose.

Conclusion:

Imaging dose delivered with 4D-CT to the OARs is higher than those of others modalities. The heart received the highest imaging dose for both UL and LL. Total imaging dose is negligible since it contributed only to 0.3% of treatment total dose.

Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver

Organ motion as a result of respiratory and cardiac motion poses significant challenges for the accurate delivery of radiotherapy to both the thorax and the upper abdomen. Modern imaging techniques during radiotherapy simulation and delivery now permit better quantification of organ motion, which in turn reduces tumour and organ at risk position uncertainty. These imaging advances, coupled with respiratory correlated radiotherapy delivery techniques, have led to the development of a range of approaches to manage respiratory motion. This review summarises the key strategies of image-guided respiratory motion management with a focus on lung and liver radiotherapy.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Direct measurement and correction of both megavoltage and kilovoltage scattered x-rays for orthogonal kilovoltage imaging subsystems with dual flat panel detectors

PURPOSE

To measure the scattered x-rays of megavoltage (MV) and kilovoltage (kV) beams (MV scatter and kV scatter, respectively) on the orthogonal kV imaging subsystems of Vero4DRT.

METHODS

Images containing MV- and kV-scatter from another source only (i.e., MV- and kV-scatter maps) were acquired for each investigated flat panel detector. The reference scatterer was a water-equivalent cuboid phantom. The maps were acquired by changing one of the following parameters from the reference conditions while keeping the others fixed: field size: 10.0 × 10.0 cm² ; dose rate: 400 MU/min; gantry and ring angles: 0°; kV collimator aperture size at isocenter: 10.0 × 10.0 cm² : tube voltage: 110 kV; and exposure: 0.8 mAs. The average pixel values of MV- and kV-scatter (i.e., the MV- and kV-scatter values) at the center of each map were calculated and normalized to the MV-scatter value under the reference conditions (MV- and kV-scatter value factor, respectively). In addition, an MV- and kV-scatter correction experiment with intensity-modulated beams was performed using a phantom with four gold markers (GMs). The ratios between the intensities of the GMs and those of their surroundings were calculated.

RESULTS

The measurements showed a strong dependency of the MV-scatter on the field size and dose rate. The maximum MV-scatter value factors were 2.0 at a field size of 15.0 × 15.0 cm² and 2.5 at a dose rate of 500 MU/min. The maximum kV-scatter value was 0.48 with a fully open kV collimator aperture. In the phantom experiment, the intensity ratios of kV images with MV- and kV-scatter were decreased from the reference ones. After correction of kV-scatter only, MV-scatter only, and both MV- and kV-scatter, the intensity ratios gradually improved.

CONCLUSIONS

MV- and kV-scatter could be corrected by subtracting the scatter maps from the projections, and the correction improved the intensity ratios of the GMs.

Incorporating Pathology-Induced Heterogeneities in a Patient-Specific Biomechanical Model of the Lung for Accurate Tumor Motion Estimation

Radiation therapy (RT) is an important component of treatment for lung cancer. However, the accuracy of this method can be affected by the complex respiratory motion/deformation of the target tumor during treatment. To improve the accuracy of RT, patient-specific biomechanical models of the lung have been proposed for estimating the tumor's respiratory motion/deformation. Chronic obstructive pulmonary disease (COPD) has a high incidence among lung cancer patients and is associated with heterogeneous destruction of lung parenchyma. This key heterogeneity element, however, has not been incorporated in lung biomechanical models developed in previous studies. In this work, we have developed a physiologically and patho-physiologically realistic lung biomechanical model that accounts for lung tissue heterogeneity. Four-dimensional computed tomography (4DCT) images were used to build a patient-specific finite element (FE) model of the lung. Image information was used to identify and incorporate inhomogeneities within the model. Mechanical properties of normal and diseased regions in the lung and the transpulmonary pressure driving the respiratory motion were estimated using an optimization algorithm that maximizes the similarity between the actual and simulated tumor and lung image data. Results from this proof of concept study on a lung cancer patient indicated improved accuracy of tumor motion estimation when COPD-induced lung tissue heterogeneities were incorporated in the model.

[Inter- and intrafraction imaging during stereotactic body radiation therapy: Which solutions for which tumours?]

Due to high dose gradients, stereotactic body radiation therapy requires high precision in the location of the tumour. Uncertainties in the positioning can introduce serious damage on organs at risk and consequently can reduce tumour local control. A better tumour location can be achieved by controlling its position with an efficient inter and intrafraction imaging procedure. The various imaging techniques available on treatment systems are presented and performances are discussed. Finally, propositions are given in terms of imaging system according to the location treated by stereotactic body radiation therapy.

First clinical retrospective investigation of limited projection CBCT for lung tumor localization in patients receiving SBRT treatment

To clinically investigate the limited-projection CBCT (LP-CBCT) technology for daily positioning of patients receiving breath-hold lung SBRT radiation treatment and to investigate the feasibility of reconstructing fast 4D-CBCT from 1 min 3D-CBCT scan. Eleven patients who underwent breath-hold lung SBRT radiation treatment were scanned daily with on-board full-projection CBCT (CBCT) using half-fan scan. A subset of the CBCT projections and the prior planning CT were used to estimate the LP-CBCT images using the weighted free-form deformation method. The limited projections are clusteringly sampled within fifteen sub-angles in 360° in order to simulate the fast 1 min scan for 4D-CBCT. The estimated LP-CBCTs were rigidly registered to the planning CT to determine the clinical shifts needed for patient setup corrections, which were compared with shifts determined by the CBCT for evaluation. Both manual and automatic registrations were performed in order to compare the systematic registration errors. Fifty CBCT volumes were obtained from the eleven patients in fifty fractions for this pilot clinical study. For the CBCT images, the mean (±standard deviation) shifts between CBCT and planning CT from manual registration in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions are 1.1  ±  1.2 mm, 2.1  ±  1.9 mm, 5.2  ±  3.6 mm, respectively. The mean deviation difference between shifts determined by CBCT and LP-CBCT images are 0.3  ±  0.5 mm, 0.5  ±  0.8 mm, 0.4  ±  0.3 mm, in LR, AP, and SI directions, respectively. The mean vector length of CBCT shift for all fractions is 6.1  ±  3.6 mm, and the mean vector length difference between CBCT and LP-CBCT for all fractions studied is 1.0  ±  0.9 mm. The automatic registrations yield similar results as manual registrations. The pilot clinical study shows that LP-CBCT localization offers comparable accuracy to CBCT localization for daily tumor positioning while reducing the projection number to 1/10 for patients receiving breath hold lung radiation treatment. The cluster projection sampling in this study also shows the feasibility of reconstructing fast 4D-CBCT from 1 min 3D-CBCT scan.

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

Background

During therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data.

Methods

Dual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session—i.e., modeling periods (T_M)—were employed: T_M ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction—i.e., correction periods (T_Cs)—were employed: T_C ∈ {5,10,15; T_C ≤ T_M} [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE).

Results

The range of mean respiratory period and 3D motion amplitude of the target was 2.4–13.0 s and 2.8–34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5–2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All T_Ms exhibited 75th percentile 3D RMSEs of 5.0–6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each T_Ms fell by 1.4–2.3 mm. The median 3D RMSE for both the 10-s T_M and the T_C period was 2.4 mm, which plateaued when the two training periods exceeded 10 s.

Conclusions

A widely-applicable estimation model for the 3D target positions during beam irradiation was developed. The optimal T_M and T_C for the model were both 10 s, to allow for more than one respiratory cycle.

Trial registration

UMIN000014825. Registered: 11 August 2014.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1019-9) contains supplementary material, which is available to authorized users.