A motion-compensated image filter for low-dose fluoroscopy in a real-time tumor-tracking radiotherapy system

A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumour motion monitoring: development and phantom‐based validation

Background

Real‐time tumor motion monitoring (TMM) is a crucial process for intra‐fractional respiration management in lung cancer radiotherapy. Since the tumor can be partly or fully located behind the ribs, the TMM is challenging.

Purpose

The aim of this work was to develop a bone suppression (BS) algorithm designed for real‐time 2D/3D marker‐less TMM to increase the visibility of the tumor when overlapping with bony structures and consequently to improve the accuracy of TMM.

Method

A BS method was implemented in the in‐house developed software for ultrafast intensity‐based 2D/3D tumor registration (Fast Image‐based Registration [FIRE]). The method operates on both, digitally reconstructed radiograph (DRR) and intra‐fractional X‐ray images. The bony structures are derived from computed tomography data by thresholding during ray‐casting, and the resulting bone DRR is subtracted from intra‐fractional X‐ray images to obtain a soft‐tissue‐only image for subsequent tumor registration. The accuracy of TMM utilizing BS was evaluated within a retrospective phantom study with nine different 3D‐printed tumor phantoms placed in the in‐house developed Advanced Radiation DOSimetry (ARDOS) breathing phantom. A 24 mm craniocaudal tumor motion, including rib eclipses, was simulated, and X‐ray images were acquired on the Elekta Versa HD Linac in the lateral and posterior–anterior directions. An error assessment for BS images was evaluated with respect to the ground truth tumor position.

Results

A total error (root mean square error) of 0.87 ± 0.23 mm and 1.03 ± 0.26 mm was found for posterior–anterior and lateral imaging; the mean time for BS was 8.03 ± 1.54 ms. Without utilizing BS, TMM failed in all X‐ray images since the registration algorithm focused on the rib position due to the predominant intensity of this tissue within DRR and X‐ray images.

Conclusion

The BS algorithm developed and implemented improved the accuracy, robustness, and stability of real‐time TMM in lung cancer in a phantom study, even in the case of rib interlude where normal tumor registration fails.

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers' positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior-inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

Real-time tumor localization with single x-ray projection at arbitrary gantry angles using a convolutional neural network (CNN)

For tumor tracking therapy, precise knowledge of tumor position in real-time is very important. A technique using single x-ray projection based on a convolutional neural network (CNN) was recently developed which can achieve accurate tumor localization in real-time. However, this method was only validated at fixed gantry angles. In this study, an improved technique is developed to handle arbitrary gantry angles for rotational radiotherapy. To evaluate the highly complex relationship between x-ray projections at arbitrary angles and tumor motion, a special CNN was proposed. In this network, a binary region of interest (ROI) mask was applied on every extracted feature map. This avoids the overfitting problem due to gantry rotation by directing the network to neglect those irrelevant pixels whose intensity variation had nothing to do with breathing motion. In addition, an angle-dependent fully connection layer (ADFCL) was utilized to recover the mapping from extracted feature maps to tumor motion, which would vary with the gantry angles. The method was tested with images from 15 realistic patients and compared with a variant network of VGG, developed by Oxford University's Visual Geometry Group. The tumors were clearly visible on x-ray projections for five patients only. The average tumor localization error was under 1.8 mm and 1.0 mm in superior-inferior and lateral directions. For the other ten patients whose tumors were not clearly visible in the x-ray projection, a feature point localization error was computed to evaluate the proposed method, the mean value of which was no more than 1.5 mm and 1.0 mm in both directions for all patients. A tumor localization method for single x-ray projection at arbitrary angles based on a novel CNN was developed and validated in this study for real-time operation. This greatly expanded the applicability of the tumor localization framework to the rotation therapy.

Estimation of patient-specific imaging dose for real-time tumour monitoring in lung patients during respiratory-gated radiotherapy

PURPOSE

To quantify the patient-specific imaging dose for real-time tumour monitoring in the lung during respiratory-gated stereotactic body radiotherapy (SBRT) in clinical cases using SyncTraX.

METHODS AND MATERIALS

Ten patients who underwent respiratory-gated SBRT with SyncTraX were enrolled in this study. The imaging procedure for real-time tumour monitoring using SyncTraX was simulated using Monte Carlo. We evaluated the dosimetric effect of a real-time tumour monitoring in a critical organ at risk (OAR) and the planning target volume (PTV) over the course of treatment. The relationship between skin dose and gating efficiency was also investigated.

RESULTS

For all patients, the mean D50 to the PTV, ipsilateral lung, liver, heart, spinal cord and skin was 118.3 (21.5-175.9), 31.9 (9.5-75.4), 15.4 (1.1-31.6), 10.1 (1.3-18.1), 25.0 (1.6-101.8), and 3.6 (0.9-7.1) mGy, respectively. The mean D2 was 352.0 (26.5-935.8), 146.4 (27.3-226.7), 90.7 (3.6-255.0), 42.2 (4.8-82.7), 88.0 (15.4-248.5), and 273.5 (98.3-611.6) mGy, respectively. The D2 of the skin dose was found to increase as the gating efficiency decreased.

CONCLUSIONS

The additional dose to the PTV was at most 1.9% of the prescribed dose over the course of treatment for real-time tumour monitoring. For OARs, we could confirm the high dose region, which may not be susceptible to radiation toxicity. However, to reduce the skin dose from SyncTraX, it is necessary to increase the gating efficiency.

Development and evaluation of a short-range applicator for treating superficial moving tumors with respiratory-gated spot-scanning proton therapy using real-time image guidance

Treatment of superficial tumors that move with respiration (e.g. lung tumors) using spot-scanning proton therapy (SSPT) is a high-priority research area. The recently developed real-time image-gated proton beam therapy (RGPT) system has proven to be useful for treating moving tumors deep inside the liver. However, when treating superficial tumors, the proton's range is small and so is the sizes of range straggling, making the Bragg-peaks extremely sharp compared to those located in deep-seated tumors. The extreme sharpness of Bragg-peaks is not always beneficial because it necessitates a large number of energy layers to make a spread-out Bragg-peak, resulting in long treatment times, and is vulnerable to motion-induced dose deterioration. We have investigated a method to treat superficial moving tumors in the lung by the development of an applicator compatible with the RGPT system. A mini-ridge filter (MRF) was developed to broaden the pristine Bragg-peak and, accordingly, decrease the number of required energy layers to obtain homogeneous irradiation. The applicator position was designed so that the fiducial marker's trajectory can be monitored by fluoroscopy during proton beam-delivery. The treatment plans for three lung cancer patients were made using the applicator, and four-dimensional (4D) dose calculations for the RGPT were performed using patient respiratory motion data. The effect of the MRF on the dose distributions and treatment time was evaluated. With the MRF, the number of energy layers was decreased to less than half of that needed without it, whereas the target volume coverage values (D99%, D95%, D50%, D2%) changed by less than 1% of the prescribed dose. Almost no dose distortion was observed after the 4D dose calculation, whereas the treatment time decreased by 26%-37%. Therefore, we conclude that the developed applicator compatible with RGPT is useful to solve the issue in the treatment of superficial moving tumors with SSPT.