Preliminary results on the feasibility of using ultrasound to monitor intrafractional motion during radiation therapy for pancreatic cancer

An integrated ultrasound imaging and abdominal compression device for respiratory motion management in radiation therapy

BACKGROUND

Radiotherapy to tumors in the abdomen is challenging because of the significant organ movement and tissue deformation caused by respiration.

PURPOSE

A motion management strategy that integrated ultrasound (US) imaging with abdominal compression was developed and evaluated, where US was used to real-time monitor organ motion after abdominal compression.

METHODS

A device that combined a US imaging system and an abdominal compression plate (ACP) was developed. Twenty-one healthy volunteers were involved to evaluate the motion management efficacy. Each volunteer was immobilized on a flat bench by the device. Abdominal US data were successively collected with and without ACP compression and experiments were repeated three times to verify the imaging reproducibility. A template matching algorithm based on normalized cross correlation (NCC) was implemented to track the targets (vessels in the liver, pancreas and stomach) automatically. The matching algorithm was validated by comparing with the manual references. Automatic tracking was judged as failed if the center of mass difference from manual tracking was beyond a failure threshold. Based on the locations obtained through the template matching algorithm, the motion correlation between liver and pancreas/stomach was investigated using Pearson correlation test. Paired Student's t-test was used to analyze the difference between the results without and with ACP compression.

RESULTS

The liver motion amplitude over all 21 volunteers was significantly (p<0.001) reduced from 14.9 ± 5.5/3.4 ± 1.8 mm in superior-inferior (SI)/anterior-posterior (AP) direction before ACP compression to 7.3 ± 1.5/1.6 ± 0.7 mm after ACP compression. The mean liver motion standard deviation before compression was on average 2.8/1.4 mm in SI/AP direction, and was significantly (p<0.001) reduced to 0.9/0.4 mm after compression. The failure rates of automatic tracking for liver, pancreas and stomach were reduced for failure thresholds of 1-5 mm after applying ACP. The Pearson correlation coefficients between liver and pancreas/stomach were 0.98/0.97 without ACP and 0.96/0.94 with ACP in SI direction, and were 0.68/0.68 and 0.43/0.42 in AP direction. The motion prediction errors for pancreas/stomach with ACP have significantly (p<0.001) reduced to 0.45 ± 0.36/0.52 ± 0.43 mm from 0.69 ± 0.56/0.71 ± 0.66 mm without ACP in SI direction, and to 0.38 ± 0.33/0.39 ± 0.27 mm from 0.44 ± 0.35/0.61 ± 0.59 mm in AP direction.

CONCLUSIONS

The proposed strategy that combines real-time US imaging and abdominal compression has the potential to reduce the abdominal organ motion while improving both target tracking reliability and motion reproducibility. Furthermore, the observed correlation between liver and pancreas/stomach motion indicates the possibility of indirect pancreas/stomach tracking using liver markers as tracking surrogates. The strategy is expected to provide an alternative for respiratory motion management in the radiation treatment of abdominal tumors. This article is protected by copyright. All rights reserved.

Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning

PURPOSE

For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X-ray imaging systems, internal markers must be implemented to facilitate tumor tracking. The purpose of this study was to develop a markerless deep learning-based pancreatic tumor positioning procedure for real-time tumor tracking with a kV X-ray imaging system.

METHODS AND MATERIALS

Fourteen pancreatic cancer patients treated with intensity-modulated radiation therapy from six fixed gantry angles with a gimbal-head radiotherapy system were included in this study. For a gimbal-head radiotherapy system, the three-dimensional (3D) intrafraction target position can be determined using an orthogonal kV X-ray imaging system. All patients underwent four-dimensional computed tomography (4DCT) simulations for treatment planning, which were divided into 10 respiratory phases. After a patient's 4DCT was acquired, for each X-ray tube angle, 10 digitally reconstructed radiograph (DRR) images were obtained. Then, a data augmentation procedure was conducted. The data augmentation procedure first rotated the CT volume around the superior-inferior and anterior-posterior directions from -3^° to 3^° in 1.5^° intervals. Then, the Super-SloMo model was adapted to interpolate 10 frames between respiratory phases. In total, the data augmentation procedure expanded the data scale 250-fold. In this study, for each patient, 12 datasets containing the DRR images from each specific X-ray tube angle based on the radiation therapy plan were obtained. The augmented dataset was randomly divided into training and testing datasets. The training dataset contained 2000 DRR images with clinical target volume (CTV) contours labeled for fine-tuning the pre-trained target contour prediction model. After the fine-tuning, the patient and X-ray tube angle-specific CTV contour prediction model was acquired. The testing dataset contained the remaining 500 images to evaluate the performance of the CTV contour prediction model. The dice similarity coefficient (DSC) between the area enclosed by the CTV contour and predicted contour was calculated to evaluate the model's contour prediction performance. The 3D position of the CTV was calculated based on the centroid of the contour in the orthogonal DRR images, and the 3D error of the prediction position was calculated to evaluate the CTV positioning performance. For each patient, the DSC results from 12 X-ray tube angles and 3D error from 6 gantry angles were calculated, representing the novelty of this study.

RESULTS

The mean and standard deviation (SD) of all patients' DSCs were 0.98 and 0.015, respectively. The mean and SD of the 3D error were 0.29 mm and 0.14 mm, respectively. The global maximum 3D error was 1.66 mm, and the global minimum DSC was 0.81. The mean calculation time for CTV contour prediction was 55 ms per image. This fulfills the requirement of RTTT.

CONCLUSIONS

Regarding the positioning accuracy and calculation efficiency, the presented procedure can provide a solution for markerless real-time tumor tracking for pancreatic cancer patients. This article is protected by copyright. All rights reserved.

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.

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning

PURPOSE

Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to use a deep neural network to localize the pancreatic tumor target on kV x-ray images acquired using an on-board imager for image guided radiation therapy.

METHODS AND MATERIALS

The network is set up in such a way that the input is either a digitally reconstructed radiograph image or a monoscopic x-ray projection image acquired by the on-board imager from a given direction, and the output is the location of the planning target volume in the projection image. To produce a sufficient number of training x-ray images reflecting the vast number of possible clinical scenarios of anatomy distribution, a series of changes were introduced to the planning computed tomography images, including deformation, rotation, and translation, to simulate inter- and intrafractional variations. After model training, the accuracy of the model was evaluated by retrospectively studying patients who underwent pancreatic cancer radiation therapy. Statistical analysis using mean absolute differences (MADs) and Lin's concordance correlation coefficient were used to assess the accuracy of the predicted target positions.

RESULTS

MADs between the model-predicted and the actual positions were found to be less than 2.60 mm in anteroposterior, lateral, and oblique directions for both axes in the detector plane. For comparison studies with and without fiducials, MADs are less than 2.49 mm. For all cases, Lin's concordance correlation coefficients between the predicted and actual positions were found to be better than 93%, demonstrating the success of the proposed deep learning for image guided radiation therapy.

CONCLUSIONS

We demonstrated that markerless pancreatic tumor target localization is achievable with high accuracy by using a deep learning technique approach.

Thermoacoustic range verification in the presence of acoustic heterogeneity and soundspeed errors - Robustness relative to ultrasound image of underlying anatomy

PURPOSE

To demonstrate robustness of thermooacoustic range verification to acoustic heterogeneity and discrepancies between assumed and true propagation speed, i.e., soundspeed errors.

METHODS

A beam sweeper was used to deliver 250 ns pulses that deposited 0.26 Gy of 16 MeV protons and 2.3 Gy of 60 MeV helium ions into water and oil targets, respectively. Thermoacoustic signals were detected by a 96-channel ultrasound array with a 1-4 MHz sensitivity band (-6 dB), bandpass filtered and backprojected to create thermoacoustic images in the plane of the ultrasound array. The same soundspeed and transducer array were used to estimate range and generate the ultrasound images onto which Bragg peak locations were overlaid. An air-gap phantom that displaced the Bragg peak by 6.5 mm demonstrated accuracy. Robustness to soundspeed errors was demonstrated in a waterbath as the assumed propagation speed scanner setting was altered by ± 5 % . Tissue-mimicking gelatin and a bone sample were introduced to demonstrate robustness to acoustic heterogeneity relative to ultrasound images of the underlying morphology.

RESULTS

Single ion pulse measurements sufficed during the helium run, but signal averaging was required for protons. Range and entry point into the target were estimated from data collected by transducers placed at least 6 cm distal to the Bragg peak. When ultrasound images depicted the air-target interface where the beam enters, estimates of the entry point agreed with ultrasound images and range estimates agreed with Monte Carlo simulations to within 300 μm, even when thermoacoustic emissions traveled through a strongly scattering bone sample. Estimated Bragg peak locations were translated 6.5 mm by the air-gap phantom and correctly identified scenarios when the beam stopped inside the bone.

CONCLUSIONS

Soundspeed errors dilate and acoustic heterogeneities deform ultrasound images. When thermoacoustic receivers are co-located with the ultrasound imaging array, the same transformations shift thermoacoustic range estimates. Therefore, thermoacoustic range verification is robust relative to ultrasound images of underlying anatomy. When the treatment target is visible in ultrasound, e.g., prostate, online thermoacoustic range estimates could verify that the treatment spot is inside the target.

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins

PURPOSE

Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle.

METHODS

We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins.

RESULTS

Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%.

CONCLUSIONS

Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

Proton Beam Therapy without Fiducial Markers Using Four-Dimensional CT Planning for Large Hepatocellular Carcinomas

We evaluated the effectiveness and toxicity of proton beam therapy (PBT) for hepatocellular carcinomas (HCC) >5 cm without fiducial markers using four-dimensional CT (4D-CT) planning. The subjects were 29 patients treated at our hospital between March 2011 and March 2015. The median total dose was 76 Cobalt Gray Equivalents (CGE) in 20 fractions (range; 66-80.5 CGE in 10-32 fractions). Therapy was delivered with end-expiratory phase gating. An internal target volume (ITV) margin was added through the analysis of respiratory movement with 4D-CT. Patient age ranged from 38 to 87 years (median, 71 years). Twenty-four patients were Child-Pugh class A and five patients were class B. Tumor size ranged from 5.0 to 13.9 cm (median, 6.9 cm). The follow-up period ranged from 2 to 72 months (median; 27 months). All patients completed PBT according to the treatment protocol without grade 4 (CTCAE v4.03 (draft v5.0)) or higher adverse effects. The two-year local tumor control (LTC), progression-free survival (PFS), and overall survival (OS) rates were 95%, 22%, and 61%, respectively. The LTC was not inferior to that of previous reports using fiducial markers. Respiratory-gated PBT with 4D-CT planning without fiducial markers is a less invasive and equally effective treatment for large HCCs as PBT with fiducial markers.

Evaluation of the intra- and interfractional tumor motion and variability by fiducial-based real-time tracking in liver stereotactic body radiation therapy

PURPOSE

Tumor motion amplitude varies during treatment. The purpose of the study was to evaluate the intra- and interfraction tumor motion and variability in patients with liver cancer treated with fiducial-based real-time tracking stereotactic body radiotherapy (SBRT).

METHODS

Fourteen liver patients were treated with SBRT using a CyberKnife. Two to four fiducial markers implanted near the tumor were used for real-time monitoring using the Synchrony system. The tumor motion information during treatment was extracted from the log files recorded by the Synchrony system. Logfile-based amplitudes in the superior-posterior (SI), left-right (LR) and anterior-posterior (AP) directions were compared to the 4DCT-based amplitudes. The intra- and interfraction amplitude variations and the incidence of baseline shifts were analyzed for 66 fractions administered to 14 patients.

RESULTS

The median (range) logfile-based liver motion amplitudes for all patients were 11.9 (5.1-17.3) mm, 1.3 (0.4-4) mm and 3.8 (0.9-7.7) mm in the SI, LR and AP directions, respectively. Compared with the logfile-based amplitude, the 4DCT-based amplitude was underestimated (P < 0.05). The median (range) intra- and interfraction liver motion amplitude variations were 4.3 (1.6-6.0) mm (SI), 0.5 (0.2-2.2) mm(LR) and 1.5 (0.3-3.3) mm (AP) and 1.7 (0.5-4.6) mm (SI), 0.3 (0.1-3.0) mm (LR) and 0.7 (0.3-2.7) mm (AP), respectively. Baseline shifts exceeding 2 mm, 3 mm and 5 mm were observed in 27.3%, 7.6% and 3% of the measurements, respectively, within 10 min, and in 66.7%, 38.1% and 19%, respectively, within 30 min for the square root of the sum of the squares of the distances in the SI, LR and AP directions (3D). The tumor motion amplitude was found to be correlated with the baseline shift.

CONCLUSIONS

Most patients showed significant intra- and interfraction liver motion amplitude variations over the entire course of radiation. More caution is needed for patients with large tumor motion amplitudes.

A Monte Carlo study of the effect of an ultrasound transducer on surface dose during intrafraction motion imaging for external beam radiation therapy

PURPOSE

The aim of this study was to estimate changes in surface dose due to the presence of the Clarity Autoscan™ ultrasound (US) probe during prostate radiotherapy using Monte Carlo (MC) methods.

METHODS

MC models of the Autoscan US probe were developed using the BEAMnrc/DOSXYZnrc code based on kV and MV CT images. CT datasets were converted to voxelized mass density phantoms using a CT number-to-mass density calibration. The dosimetric effect of the probe, in the contact region (an 8 mm × 12 mm single layer of voxels), was investigated using a phantom set-up mimicking two scenarios (a) a transperineal imaging configuration (radiation beam perpendicular to the central US axial direction), and (b) a transabdominal imaging configuration (radiation beam parallel to the central US axial direction). For scenario (a), the dosimetric effect was evaluated as a function of the probe to inferior radiation field edge distance. Clinically applicable distances from 5 mm separation to 2 mm overlap were determined from the radiotherapy plans of 27 patients receiving Clarity imaging. Overlaps of 3 to 14 (1 to 3 SD) mm were also considered to include the effect of interfraction motion correction. The influence of voxel size on surface dose estimation was investigated. Approved clinical plans from two prostate patients were used to simulate worst-case dosimetric impact of the probe when large couch translations were applied to correct for interfraction prostate motion.

RESULTS

The dosimetric impact of both the MV and kV probe models agreed within ±2% for both beam configurations. For scenario (a) and 1 mm voxel model, the probe gave mean dose increases of 1.2% to 4.6% (of the dose at isocenter) for 5 mm separation to 0 mm overlap in the probe-phantom contact region, respectively. This increased to 27.5% for the largest interfraction motion correction considered (14 mm overlap). For separations of ≥ 2 mm dose differences were < 2%. Simulated dose perturbations were found to be superficial; for the 14 mm overlap the dose increase reduced to < 3% at 5.0 mm within the phantom. For scenario (b), dose increases due to the probe were < 5% in all cases. The dose increase was underestimated by up to ~13% when the voxel size was increased from 1 mm to 3 mm. MC simulated dose to the PTV and OARs for the two clinical plans considered showed good agreement with commercial treatment planning system results (within 2%). Mean dose increases due to the presence of the probe, after the maximum interfraction motion correction, were ~16.3% and ~8.0%, in the contact region, for plan 1 and plan 2, respectively.

CONCLUSIONS

The presence of the probe results in superficial dose perturbations for patients with an overlap between the probe and the radiation field present in either the original treatment plan or due to translation of the radiation field to simulate correction of interfraction internal prostate motion.