Design and validation of a MV/kV imaging-based markerless tracking system for assessing real-time lung tumor motion

Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy

BACKGROUND

In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential.

PURPOSE

To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition.

METHODS

We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor.

RESULTS

The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 ± 0.7 mm in the superior-inferior (SI) direction and 0.9 ± 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, it took approximately 35 ms to generate one sDTI, and 13 ms to perform the tumor tracking using template matching.

CONCLUSIONS

Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study

BACKGROUND

Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging.

PURPOSE

To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR).

METHODS

Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung.

RESULTS

Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom.

CONCLUSIONS

The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

Intrafractional accuracy and efficiency of a surface imaging system for deep inspiration breath hold during ablative gastrointestinal cancer treatment

PURPOSE

Beam gating with deep inspiration breath hold (DIBH) usually depends on some external surrogate to infer internal target movement, and the exact internal movement is unknown. In this study, we tracked internal targets and characterized residual motion during DIBH treatment, guided by a surface imaging system, for gastrointestinal cancer. We also report statistics on treatment time.

METHODS AND MATERIALS

We included 14 gastrointestinal cancer patients treated with surface imaging-guided DIBH volumetrically modulated arc therapy, each with at least one radiopaque marker implanted near or within the target. They were treated in 25, 15, or 10 fractions. Thirteen patients received treatment for pancreatic cancer, and one underwent separate treatments for two liver metastases. The surface imaging system monitored a three-dimensional surface with ± 3 mm translation and ± 3° rotation threshold. During delivery, a kilovolt image was automatically taken every 20° or 40° gantry rotation, and the internal marker was identified from the image. The displacement and residual motion of the markers were calculated. To analyze the treatment efficiency, the treatment time of each fraction was obtained from the imaging and treatment timestamps in the record and verify system.

RESULTS

Although the external surface was monitored and limited to ± 3 mm and ± 3°, significant residual internal target movement was observed in some patients. The range of residual motion was 3-21 mm. The average displacement for this cohort was 0-3 mm. In 19% of the analyzed images, the magnitude of the instantaneous displacement was > 5 mm. The mean treatment time was 17 min with a standard deviation of 4 min.

CONCLUSIONS

Precaution is needed when applying surface image guidance for gastrointestinal cancer treatment. Using it as a solo DIBH technique is discouraged when the correlation between internal anatomy and patient surface is limited. Real-time radiographic verification is critical for safe treatments.

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.

Evaluation of a proprietary software application for motion monitoring during stereotactic paraspinal treatment

PURPOSE

Stereotactic paraspinal treatment has become increasingly popular due to its favorable clinical outcome. An often-overlooked factor that compromises the effectiveness of such treatment is the patients' involuntary intrafractional motion. This work introduces and validates a proprietary software application that quantifies such motion for accurate patient monitoring during treatment.

METHODS

The software uses a separate full-trajectory cone-beam computed tomography (CBCT) after daily patient setup to establish reference projections. Once treatment starts, the software grabs the intrafraction motion review (IMR) image acquired by TrueBeam via the Varian iTools Capture software and compares it against the corresponding reference projection to instantly determine the 2D shifts of the vertebrae being monitored using the classical downhill simplex optimization method. To evaluate its performance, an anthropomorphic phantom was shifted 0, 0.6, 1.2, 1.8, 2.4, 3.0, and 5 mm in three orthogonal directions, immediately after the full-trajectory CBCT but prior to treatment. Depending on the scenario of shift, a nine-field fixed gantry intensity-modulated radiation therapy (IMRT) plan and/or a four partial-posterior-arcs volume-modulated radiation therapy (VMAT) plan were delivered. For the IMRT plan, three IMR images were acquired sequentially every 200 monitor units (MU) at each treatment angle. For the VMAT plan, one IMR image was acquired every 15° of each arc. For each IMR image, the software-reported 2D shift was compared with the ground truth. Certain tests were repeated with 1°, 2°, and 3° of rotation, pitch, and roll, respectively. Some of these tests were also repeated independently on separate days.

RESULTS

Based on the group of tests that involved only the IMRT delivery, the maximum standard deviation of the software-reported shifts for each set of three IMR images was 0.16 mm, with 95th percentile at 0.02 mm. For translational shift, the maximum registration error was 0.44 mm, with 95th percentile at 0.23 mm. Left unaccounted for, rotation and pitch degraded the registration accuracy mainly in the longitudinal direction, while roll degraded it mainly in the lateral direction. The degradation of registration accuracy is positively related to the degree of rotation, pitch, and roll. The maximum registration errors under 3° rotation, pitch, and roll were 2.97, 1.44, 2.72 mm, respectively. Based on the group of tests that compared IMRT delivery with VMAT delivery, the registration errors slightly increased as magnitude of shifts increased; however, they were well under the 0.5-mm threshold. No significant differences in registration errors were observed between IMRT and VMAT deliveries. In addition, the variation in registration errors among different days was limited for both IMRT and VMAT deliveries.

CONCLUSIONS

Our proprietary software has high repeatability, both intrafractionally and interfractionally, and high accuracy in registering IMR images with the reference projections for motion monitoring, regardless of the magnitude of shifts or treatment delivery technique. Rotation, pitch, and roll degrade registration accuracy and need to be accounted for in the future work.

Shape-Sensing Robotic-Assisted Bronchoscopy in the Diagnosis of Pulmonary Parenchymal Lesions

BACKGROUND

The landscape of guided bronchoscopy for the sampling of pulmonary parenchymal lesions is evolving rapidly. Shape-sensing robotic-assisted bronchoscopy (ssRAB) recently was introduced as means to allow successful sampling of traditionally challenging lesions.

RESEARCH QUESTION

What are the feasibility, diagnostic yield, determinants of diagnostic sampling, and safety of ssRAB in patients with pulmonary lesions?

STUDY DESIGN AND METHODS

Data from 131 consecutive ssRAB procedures performed at a US-based cancer center between October 2019 and July 2020 were captured prospectively and analyzed retrospectively. Definitions of diagnostic procedures were based on prior standards. Associations of procedure- and lesion-related factors with diagnostic yield were examined by univariate and multivariate generalized linear mixed models.

RESULTS

A total of 159 pulmonary lesions were targeted during 131 ssRAB procedures. The median lesion size was 1.8 cm, 59.1% of lesions were in the upper lobe, and 66.7% of lesions were beyond a sixth-generation airway. The navigational success rate was 98.7%. The overall diagnostic yield was 81.7%. Lesion size of ≥ 1.8 cm and central location were associated significantly with a diagnostic procedure in the univariate analysis. In the multivariate model, lesions of ≥ 1.8 cm were more likely to be diagnostic compared with lesions < 1.8 cm, after adjusting for lung centrality (OR, 12.22; 95% CI, 1.66-90.10). The sensitivity and negative predictive value of ssRAB for primary thoracic malignancies were 79.8% and 72.4%, respectively. The overall complication rate was 3.0%, and the pneumothorax rate was 1.5%.

INTERPRETATION

This study was the first to provide comprehensive evidence regarding the usefulness and diagnostic yield of ssRAB in the sampling of pulmonary parenchymal lesions. ssRAB may represent a significant advancement in the ability to access and sample successfully traditionally challenging pulmonary lesions via the bronchoscopic approach, while maintaining a superb safety profile. Lesion size seems to remain the major predictor of a diagnostic procedure.

Stability and Reliability of Enhanced External-Internal Motion Correlation via Dynamic Phase-Shift Corrections Over 30-min Timeframe for Respiratory-Gated Radiotherapy

To assess the stability of patient-specific phase shifts between external- and internal-respiratory motion waveforms, the reliability of enhanced external–internal correlation with phase-shift correction, and the feasibility of guiding respiratory-gated radiotherapy (RGRT) over 30 min. In this clinical feasibility investigation, external bellows and internal-navigator waveforms were simultaneously and prospectively acquired along with two four-dimensional magnetic resonance imaging (4DMRI) scans (6–15 m each) with 15–20 m intervals in 10 volunteers. A bellows was placed 5 cm inferior to the xiphoid to monitor abdominal motion, and an MR navigator was used to track the diaphragmatic motion. The mean phase-domain (MPD) method was applied, which combines three individual phase-calculating methods: phase-space oval fitting, principal component analysis, and analytic signal analysis, weighted by the reciprocal of their residual errors (RE) excluding outliers (RE >2σ). The time-domain cross-correlation (TCC) analysis was applied for comparison. Dynamic phase-shift correction was performed based on the phase shift detected on the fly within two 10 s moving datasets. Simulating bellows-triggered gating, the median and 95% confidence interval for the navigator's position at beam-on/beam-off and %harm (percentage of beam-on time outside the safety margin) were calculated. Averaged across all subjects, the mean phase shifts are found indistinguishable (p > .05) between scan 1 (55˚ ± 9˚) and scan 2 (59˚ ± 11˚). Using the MPD method the averaged correlation increases from 0.56 ± 0.22 to 0.85 ± 0.11 for scan 1 and from 0.47 ± 0.30 to 0.84 ± 0.08 for scan 2. The TCC correction results in similar results. After phase-shift correction, the number of cases that were suitable for amplitude gating (with <10%harm) increased from 2 to 17 out of 20 cases. A patient-specific, stable phase-shift between the external and internal motions was observed and corrected using the MPD and TCC methods, producing long-lasting enhanced motion correlation over 30m. Phase-shift correction offers a feasible strategy for improving the accuracy of tumor-motion prediction during RGRT.

Results of Radiation Therapy as Local Ablative Therapy for Oligometastatic Non-Small Cell Lung Cancer

Oligometastatic cancer is characterized by a limited number of metastatic deposits. Compared with lung cancer patients who have more widespread disease, oligometastatic lung cancer patients have more favorable survival outcomes. Therefore, it has been hypothesized that local ablative therapy (LAT) directed at the metastatic deposits in addition to standard-of-care systemic therapy may further improve survival outcomes in oligometastatic lung cancer patients. One LAT modality that has been utilized in oligometastatic lung cancer is radiation therapy. In particular, ultra-hypofractionated radiotherapy, also known as stereotactic body radiotherapy (SBRT), has been shown to provide excellent local control with a favorable safety profile. Here, we reviewed the retrospective studies and prospective trials that have deployed radiation therapy as LAT in oligometastatic lung cancer, including randomized studies showing benefits for progression-free survival and overall survival with the addition of LAT. We also discuss the impact of targeted therapies and immunotherapy on radiation as LAT.

Markerless Real-Time 3-Dimensional kV Tracking of Lung Tumors During Free Breathing Stereotactic Radiation Therapy

Purpose

Accurate verification of tumor position during irradiation could reduce the probability of target miss. We investigated whether a commercial gantry-mounted 2-dimensional (2D) kilo-voltage (kV) imaging system could be used for real-time 3D tumor tracking during volumetric modulated arc therapy (VMAT) lung stereotactic body radiation therapy (SBRT). Markerless tumor tracking on kV fluoroscopic images was validated using a life-like moving thorax phantom and subsequently performed on kV images continuously acquired before and during free-breathing VMAT lung SBRT.

Methods and Materials

The 3D-printed/molded phantom containing 3 lung tumors was moved in 3D in TrueBeam developer mode, using simulated regular/irregular breathing patterns. Planar kV images were acquired at 7 frames/s during 11 Gy/fraction 10 MV flattening filter free VMAT. 2D reference templates were created for each gantry angle using the planning 4D computed tomography inspiration phase. kV images and templates were matched using normalized cross correlation to determine 2D tumor position, and triangulation of 2D matched projections determined the third dimension. 3D target tracking performed on cone beam computed tomography projection data from 18 patients (20 tumors) and real-time online tracking data from 2 of the 18 patients who underwent free-breathing VMAT lung SBRT are presented.

Results

For target 1 and 2 of the phantom (upper lung and middle/medial lung, mean density –130 Hounsfield units), 3D results within 2 mm of the known position were present in 92% and 96% of the kV projections, respectively. For target 3 (inferior lung, mean density –478 Hounsfield units) this dropped to 80%. Benchmarking against the respiratory signal, 13/20 (65%) tumors (10.5 ± 11.1 cm³) were considered successfully tracked on the cone beam computed tomography data. Tracking was less successful (≤50% of the time) in 7/20 (1.2 ± 1.5 cm³). Successful online tracking during lung SBRT was demonstrated.

Conclusions

3D markerless tumor tracking on a standard linear accelerator using template matching and triangulation of free-breathing kV fluoroscopic images was possible in 65% of small lung tumors. The smallest tumors were most challenging.

Feasibility of MR-guided radiotherapy using beam-eye-view 2D-cine with tumor-volume projection

PURPOSE

Current magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) applies sagittal/coronal 2D-cine to monitor major tumor motions, however, the beam eye's view (BEV) with volumetric tumor projection would be the best measure for radiation beam conformality, independent of tumor through-plane motion. The goal is to assess the feasibility, accuracy, and performance of the BEV approach.

METHODS

Beam-specific BEV 2D-cine with volume-projected tumor contours were simulated to establish a 2D/3D tumor match against a tumor-motion library based on multi-breath time-resolved (TR) 4DMRI images. Two BEV-library-matching methods were developed: (1) fast screening with tumor center-of-mass (∆COM), in-plane area ratio, and DICE similarity, and finalizing with the highest DICE score and (2) DICE screening for top-3 candidates and finalizing with rigid registration. A 4D-XCAT digital phantom and 8 lung-cancer patients were used for assessment. For each patient, 3 sets of 40 s TR-4DMRI were acquired at 2 Hz and 6 representative BEV were created with the isocenter set at tumor COM in mid-respiration. One TR-4DMRI set (40 × 2 = 80-images) was used to simulate BEV 2D-cine and the other two (160-images) were used to create a library. The matching result was validated against the ground truth within the test set. Using a leave-one-out strategy, the success rate, accuracy, and speed of tumor matching were assessed for volume-projected tumors over 11520 time-points (=8patients•3sets•80images•6BEVs).

RESULTS

Volume-projected tumor contour area on the 6 BEVs varies by 60% ± 8% and [Formula: see text] (in-plane/volume-projected) varies by 82% ± 9%. The [Formula: see text] changes with tumor shape, orientation, and through-plane motion. Method-1 produces 96% matching success (ΔCOM = 0.7 ± 0.2 mm, [Formula: see text]=1.01 ± 0.02, Dice=0.92 ± 0.02) with the computational time of 15 ± 1 ms/match, while method-2 produces 94% ± 1% success (ΔCOM = 0.2 ± 0.1 mm, [Formula: see text]=1.00 ± 0.01, Dice = 0.94 ± 0.02) with 223 ± 13 ms/match.

CONCLUSION

This study has demonstrated the feasibility, accuracy, and benefits of BEV 2D-cine imaging with tumor-volume projection, allowing real-time tumor motion monitoring and beam conformality checking. Further clinical evaluation is necessary before MRgRT applications.

Technical Note: Synthetic treatment beam imaging for motion monitoring during spine SBRT treatments - a phantom study

PURPOSE

One of the biggest challenges in applying megavoltage (MV) treatment beam imaging for monitoring spine motion in stereotactic body radiotherapy (SBRT) is the small beam apertures in the images due to strong beam modulations in IMRT planning. The purpose of this study is to investigate the feasibility of a markerless motion tracking method in spine SBRT delivery using a novel enhanced synthetic treatment beam (ESTB) imaging technique.

METHODS

Three clinical spine SBRT plans using 6XFFF beams and sliding window IMRT technique were transferred to a thorax phantom and delivered by a TrueBeam machine. Before delivery, the phantom was aligned to the plan isocenter using CBCT setup and verified with a second CBCT, and then, 2 mm shifts were introduced in both the craniocaudal (CC) and the left-right (LR) directions with the couch. During beam delivery, MV images were continuously taken with an electronic portal imaging device (EPID) and automatically grabbed by Varian iTools Capture software with a frame rate of 11.6 Hz. After preprocessing for scatter correction and beam intensity compensation, every 50 frames of MV images were combined to generate a series of ESTB images for each beam. The ESTB images were then registered to the projections of the verification CBCT at the matched beam angles to detect the 2 mm shifts.

RESULTS

Compared to snapshot MV images, the ESTB images had significantly enlarged fields of view (FOVs) and improved image quality. Based on two-dimensional (2D) rigid registration, the ESTB image to CBCT projection matching showed submillimeter accuracy in detecting motion. Specifically, the root mean square errors in detecting the LR/CC shifts were 0.35/0.28, 0.32/0.35, 0.63/0.44, 0.55/0.51, and 0.69/0.42 mm at gantry angles 180, 160, 140, 120, and 100, respectively.

CONCLUSION

Our results in the phantom study suggest that ESTB images from a sliding window IMRT plan can be used to detect spine motion, with submillimeter precision in the 2D plane perpendicular to the beam.

The first prospective implementation of markerless lung target tracking in an experimental quality assurance procedure on a standard linear accelerator

The ability to track tumour motion without implanted markers on a standard linear accelerator (linac) could enable wide access to real-time adaptive radiotherapy for cancer patients. We previously have retrospectively validated a method for 3D markerless target tracking using intra-fractional kilovoltage (kV) projections acquired on a standard linac. This paper presents the first prospective implementation of markerless lung target tracking on a standard linac and its quality assurance (QA) procedure. The workflow and the algorithm developed to track the 3D target position during volumetric modulated arc therapy treatment delivery were optimised. The linac was operated in clinical QA mode, while kV projections were streamed to a dedicated computer using a frame-grabber software. The markerless target tracking accuracy and precision were measured in a lung phantom experiment under the following conditions: static localisation of seven distinct positions, dynamic localisation of five patient-measured motion traces, and dynamic localisation with treatment interruption. The QA guidelines were developed following the AAPM Task Group 147 report with the requirement that the tracking margin components, the margins required to account for tracking errors, did not exceed 5 mm in any direction. The mean tracking error ranged from 0.0 to 0.9 mm (left-right), -0.6 to  -0.1 mm (superior-inferior) and  -0.7 to 0.1 mm (anterior-posterior) over the three tests. Larger errors were found in cases with large left-right or anterior-posterior and small superior-inferior motion. The tracking margin components did not exceed 5 mm in any direction and ranged from 0.4 to 3.2 mm (left-right), 0.7 to 1.6 mm (superior-inferior) and 0.8 to 1.5 mm (anterior-posterior). This study presents the first prospective implementation of markerless lung target tracking on a standard linac and provides a QA procedure for its safe clinical implementation, potentially enabling real-time adaptive radiotherapy for a large population of lung cancer patients.

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

PURPOSE

To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images.

METHOD

Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500 kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy.

RESULT

Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high-performance computer cluster.

CONCLUSIONS

CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.

Real-time intrafraction motion monitoring in external beam radiotherapy

Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT)

BACKGROUND AND PURPOSE

To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kilovoltage (kV) X-ray images in image-guided radiation therapy (IGRT).

MATERIALS AND METHODS

We developed a personalized region-based convolutional neural network to localize the prostate treatment target without implanted fiducials. To train the deep neural network (DNN), we used the patient's planning computed tomography (pCT) images with pre-delineated prostate target to generate a large amount of synthetic kV projection X-ray images in the geometry of onboard imager (OBI) system. The DNN model was evaluated by retrospectively studying 10 patients who underwent prostate IGRT. Three out of the ten patients who had implanted fiducials and the fiducials' positions in the OBI images acquired for treatment setup were examined to show the potential of the proposed method for prostate IGRT. Statistical analysis using Lin's concordance correlation coefficient was calculated to assess the results along with the difference between the digitally reconstructed radiographs (DRR) derived and DNN predicted locations of the prostate.

RESULTS

Differences between the predicted target positions using DNN and their actual positions are (mean ± standard deviation) 1.58 ± 0.43 mm, 1.64 ± 0.43 mm, and 1.67 ± 0.36 mm in anterior-posterior, lateral, and oblique directions, respectively. Prostate position identified on the OBI kV images is also found to be consistent with that derived from the implanted fiducials.

CONCLUSIONS

Highly accurate, markerless prostate localization based on deep learning is achievable. The proposed method is useful for daily patient positioning and real-time target tracking during prostate radiotherapy.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.