Real-time Image-guided Radiotherapy Research Articles

The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database.

The US National Institutes of Health state that Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024-01-24.). Aligned with this initiative, the Trial Management Committee of the Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data. The data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi-institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real-time image-guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real-time tumor translational and rotational motion were determined from x-ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs-at-risk. Patient-reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment. The dataset contains planning CT images, treatment plans, structure sets, planned and motion-included dose-volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d-6058. The 3.6Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real-time image-guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate-specific antigen kinetics.

Radio-opaque contrast agents for liver cancer targeting with KIM during radiation therapy (ROCK-RT): an observational feasibility study.

This observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular carcinoma. This study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are ≤ 2mm and ≤ 3mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial participants. Developing a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial markers. Registered to ClinicalTrials.gov (NCT05169177) 12th October 2021.

Application of motion prediction based on a long short-term memory network for imaging dose reduction in real-time tumor-tracking radiation therapy

PurposeTo demonstrate the possibility of using a lower imaging rate while maintaining acceptable accuracy by applying motion prediction to minimize the imaging dose in real-time image-guided radiation therapy. MethodsTime-series of three-dimensional internal marker positions obtained from 98 patients in liver stereotactic body radiation therapy were used to train and test the long-short-term memory (LSTM) network. For real-time imaging, the root mean squared error (RMSE) of the prediction on three-dimensional marker position made by LSTM, the residual motion of the target under respiratory-gated irradiation, and irradiation efficiency were evaluated. In the evaluation of the residual motion, the system-specific latency was assumed to be 100 ms. ResultsExcept for outliers in the superior–inferior (SI) direction, the median/maximum values of the RMSE for imaging rates of 7.5, 5.0, and 2.5 frames per second (fps) were 0.8/1.3, 0.9/1.6, and 1.2/2.4 mm, respectively. The median/maximum residual motion in the SI direction at an imaging rate of 15.0 fps without prediction of the marker position, which is a typical clinical setting, was 2.3/3.6 mm. For rates of 7.5, 5.0, and 2.5 fps with prediction, the corresponding values were 2.0/2.6, 2.2/3.3, and 2.4/3.9 mm, respectively. There was no significant difference between the irradiation efficiency with and that without prediction of the marker position. The geometrical accuracy at lower frame rates with prediction applied was superior or comparable to that at 15 fps without prediction. In comparison with the current clinical setting for real-time image-guided radiation therapy, which uses an imaging rate of 15.0 fps without prediction, it may be possible to reduce the imaging dose by half or more. ConclusionsMotion prediction can effectively lower the frame rate and minimize the imaging dose in real-time image-guided radiation therapy.

Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients

Objective. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional imaging dose and provides motion data in the treatment beam frame of reference. However, accurate tracking using MV images is challenging due to low contrast and modulated fields. Here, a novel real-time marker tracking system based on a convolutional neural network (CNN) classifier was developed and evaluated on retrospectively acquired patient data for MV-based IGRT for prostate cancer patients. Approach. MV images, acquired from 29 volumetric modulated arc therapy (VMAT) prostate cancer patients treated in a multi-institutional clinical trial, were used to train and evaluate a CNN-based marker tracking system. The CNN was trained using labelled MV images from 9 prostate cancer patients (35 fractions) with implanted markers. CNN performance was evaluated on an independent cohort of unseen MV images from 20 patients (78 fractions), using a Precision–Recall curve (PRC), area under the PRC plot (AUC) and sensitivity and specificity. The accuracy of the tracking system was evaluated on the same unseen dataset and quantified by calculating mean absolute (±1 SD) and [1st, 99th] percentiles of the geometric tracking error in treatment beam co-ordinates using manual identification as the ground truth. Main results. The CNN had an AUC of 0.99, sensitivity of 98.31% and specificity of 99.87%. The mean absolute geometric tracking error was 0.30 ± 0.27 and 0.35 ± 0.31 mm in the lateral and superior–inferior directions of the MV images, respectively. The [1st, 99th] percentiles of the error were [−1.03, 0.90] and [−1.12, 1.12] mm in the lateral and SI directions, respectively. Significance. The high classification performance on unseen MV images demonstrates the CNN can successfully identify implanted prostate markers. Furthermore, the sub-millimetre accuracy and precision of the marker tracking system demonstrates potential for adaptation to real-time applications.

A review of artificial intelligence applications for motion tracking in radiotherapy.

During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

Delocalization of Hydrophobicity using Random/Statistical Copolymers: A Facile Tactic towards Developing High-performance X-ray Contrast Media for Real-time Image-guided Radiation Therapy

Traditional X-ray contrast media (XRCM) suffers from rapid renal clearance (half-life < 30 min), leading to a narrow acquisition time window for the X-ray computed tomography (CT) after intravenous (IV) administration, and limiting the operation time for the potential real-time CT-guided radiation therapy. Real-time image-guided radiation therapy can improve tumor-specific radiation dosages while sparing healthy tissue and critical structures. To broaden operational time and improve performance of XRCM, the next-generation XRCM with long blood residence time, high contrast efficiency, specific biodistribution, facile access to dual or multiple diagnostic imaging modalities, and biocompatibility is desirable. We report a simple strategy for preparing iodinated nano-XRCM for CT-PET dual imaging through intra/intermolecular self-assembly of random/statistical amphiphilic iodocopolymers (ICPs). ICPs were synthesized from reversible addition-fragmentation chain transfer (RAFT) copolymerization of triiodobenzoyl acrylate and oligo(ethylene oxide) acrylate monomers. The controlled RAFT copolymerization allowed for installation of DOTA moieties to chelate 64Cu radionuclides, enabling CT and positron emission tomography (PET) dual imaging and targeted radiotherapy. An EMT6 breast cancer mouse model (C57BL/6 mice) was used for in vivo biodistribution evaluation using 185 kBq of 64Cu-DOTA-ICP (45 mg/mouse) in 75 μL saline injected via the tail vein of C57BL/6 mice weighing 20–25 g (n = 4/group). The mice were euthanized at 1, 4, 24, and 48 h post-injection, and organs were collected, weighed, and counted in a well gamma counter. Intra/intermolecular self-assembly of ICPs formed nano-XRCM particles with ultrahigh iodine concentration (up to ca. 300 mg/mL), low viscosity (4.75 cSt at 37 °C), long term thermodynamic stability (> 6 months), and the identical hydrodynamic size of ca. 10 nm independent of the degree of polymerization. The resulting ICP-based nano-XRCMs showed excellent biocompatibility and exhibited long blood circulation time over 2 days with adequate tumor accumulation and low non-specific accumulation after single IV administration, allowing ample operation time for potential real-time CT image-guided radiation therapy application. Furthermore, the micro-CT imaging showed clear tumor contrast 6 days post-injection. The simple synthetic methodology allows the construction of multi-functional theranostic agents by incorporating diverse moieties. The ICP-based nano-XRCMs demonstrated extended blood residence time, high contrast efficiency, tumor specific accumulation, and biocompatibility for potential real-time image-guided radiotherapy application. Clear tumor contrast 6 days post-injection allowed convenient monitoring, which could be important for future studies of therapeutic effect.

Image guidance in radiation therapy for better cure of cancer.

The key goal and main challenge of radiation therapy is the elimination of tumors without any concurring damages of the surrounding healthy tissues and organs. Radiation doses required to achieve sufficient cancer‐cell kill exceed in most clinical situations the dose that can be tolerated by the healthy tissues, especially when large parts of the affected organ are irradiated. High‐precision radiation oncology aims at optimizing tumor coverage, while sparing normal tissues. Medical imaging during the preparation phase, as well as in the treatment room for localization of the tumor and directing the beam, referred to as image‐guided radiotherapy (IGRT), is the cornerstone of precision radiation oncology. Sophisticated high‐resolution real‐time IGRT using X‐rays, computer tomography, magnetic resonance imaging, or ultrasound, enables delivery of high radiation doses to tumors without significant damage of healthy organs. IGRT is the most convincing success story of radiation oncology over the last decades, and it remains a major driving force of innovation, contributing to the development of personalized oncology, for example, through the use of real‐time imaging biomarkers for individualized dose delivery.

Real-Time Image Guided Ablative Prostate Cancer Radiation Therapy: Results From the TROG 15.01 SPARK Trial

Kilovoltage intrafraction monitoring (KIM) is a novel software platform implemented on standard radiation therapy systems and enabling real-time image guided radiation therapy (IGRT). In a multi-institutional prospective trial, we investigated whether real-time IGRT improved the accuracy of the dose patients with prostate cancer received during radiation therapy. Forty-eight patients with prostate cancer were treated with KIM-guided SABR with 36.25 Gy in 5 fractions. During KIM-guided treatment, the prostate motion was corrected for by either beam gating with couch shifts or multileaf collimator tracking. A dose reconstruction method was used to evaluate the dose delivered to the target and organs at risk with and without real-time IGRT. Primary outcome was the effect of real-time IGRT on dose distributions. Secondary outcomes included patient-reported outcomes and toxicity. Motion correction occurred in ≥1 treatment for 88% of patients (42 of 48) and 51% of treatments (121 of 235). With real-time IGRT, no treatments had prostate clinical target volume (CTV) D98% dose 5% less than planned. Without real-time IGRT, 13 treatments (5.5%) had prostate CTV D98% doses 5% less than planned. The prostate CTV D98% dose with real-time IGRT was closer to the plan by an average of 1.0% (range, -2.8% to 20.3%). Patient outcomes showed no change in the 12-month patient-reported outcomes compared with baseline and no grade ≥3 genitourinary or gastrointestinal toxicities. Real-time IGRT is clinically effective for prostate cancer SABR.

Percutaneous insertion of hepatic fiducial true-spherical markers for real-time adaptive radiotherapy

Purpose: This study evaluated the success rate and complications of percutaneous implantation of hepatic fiducial true-spherical gold markers for real-time adaptive radiotherapy (RAR), which constitutes real-time image-guided radiotherapy with gating.Material and methods: We retrospectively evaluated 100 patients who underwent 116 percutaneous intrahepatic implantations of 2-mm-diameter, spherical, gold fiducial markers before RAR from 1999 to 2016, with Seldinger’s method. We defined technical success as marker placement at the intended liver parenchyma, without mispositioning, and clinical success as successful tracking of the gold marker and completion of planned RAR. Complications related to marker placement were assessed.Results: The technical success rate for true-spherical gold marker implantation was 92.2% (107/116). Nine of 116 markers migrated (intra-procedurally in seven patients, delayed in two patients). Migration out of the liver (n = 4) or intrahepatic vessels (n = 5) occurred without complications; these markers were not retrieved. The clinical success rate was 100.0% (115/115). Abdominal pain occurred in 16 patients, fever and hemorrhage in seven patients each, and pneumothorax and nausea in one patient each. No major complications were encountered.Conclusions: Percutaneous transhepatic implantation of true-spherical gold markers for RAR is feasible and can be conducted with a high success rate and low complication rate.

Dosimetry needs for MRI-linacs

The pursuit of real-time image-guided radiotherapy has prompted the development of hybrid devices coupling MRI scanners with radiotherapy treatment units, usually linear accelerators (linacs). One of the challenges in MRI-linac technology is the magnetic field impact on the dose deposition. Dose deposition effects have to be considered in the radiation therapy chain as they alter the dose distribution in patients and influence the response of many commonly used radiation detectors. In this presentation specific issues of dosimetry for MRI-linacs will be reviewed and illustrated with examples from the Australian MRI-linac commissioning process.

A six-degree-of-freedom robotic motion system for quality assurance of real-time image-guided radiotherapy

In this study we develop and characterise a six degree-of-freedom (6 DoF) robotic motion system for quality assurance of real-time image-guided radiotherapy techniques. The system consists of a commercially available robotic arm, an acrylic phantom with embedded Calypso markers, a custom base plate to mount the robot to the treatment couch, and control software implementing the appropriate sequence of transformations to reproduce measured tumour motion traces. The robotic motion system was evaluated in terms of the set-up and motion trace repeatability, static localization accuracy and dynamic localization accuracy. Four prostate, two liver and three lung motion traces, representing a range of tumor motion trajectories recorded in real patient treatments, were executed using the robotic motion system and compared with motion measurements from the clinical Calypso motion tracking system. System set-up and motion trace repeatability was better than 0.5 deg and 0.3 mm for rotation and translation, respectively. The static localization accuracy of the robotic motion system in the LR, SI and AP directions was 0.09 mm, 0.08 mm and 0.02 mm for translations, respectively, and 0.2°, 0.06° and 0.06° for rotations, respectively. The dynamic localization accuracy of the robotic motion system was <0.2 mm and <0.6° for translations and rotations, respectively. Thus, we have demonstrated the ability to accurately mimic rigid-body tumor motion using a robotically controlled phantom to provide precise geometric QA for advanced radiotherapy delivery approaches.

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).

Review of Real-Time 3-Dimensional Image Guided Radiation Therapy on Standard-Equipped Cancer Radiation Therapy Systems: Are We at the Tipping Point for the Era of Real-Time Radiation Therapy?

Toreview real-time 3-dimensional (3D) image guided radiation therapy (IGRT) on standard-equipped cancer radiation therapy systems, focusing on clinically implemented solutions. Three groups in 3 continents have clinically implemented novel real-time 3D IGRT solutions on standard-equipped linear accelerators. These technologies encompass kilovoltage, combined megavoltage-kilovoltage, and combined kilovoltage-optical imaging. The cancer sites treated span pelvic and abdominal tumors for which respiratory motion is present. For each method the 3D-measured motion during treatment is reported. After treatment, dose reconstruction was used to assess the treatment quality in the presence of motion with and without real-time 3D IGRT. The geometric accuracy was quantified through phantom experiments. A literature search was conducted to identify additional real-time 3D IGRT methods that could be clinically implemented in the near future. The real-time 3D IGRT methods were successfully clinically implemented and have been used to treat more than 200 patients. Systematic target position shifts were observed using all 3 methods. Dose reconstruction demonstrated that the delivered dose is closer to the planned dose with real-time 3D IGRT than without real-time 3D IGRT. In addition, compromised target dose coverage and variable normal tissue doses were found without real-time 3D IGRT. The geometric accuracy results with real-time 3D IGRT had a mean error of <0.5mm and a standard deviation of <1.1mm. Numerous additional articles exist that describe real-time 3D IGRT methods using standard-equipped radiation therapy systems that could also be clinically implemented. Multiple clinical implementations of real-time 3D IGRT on standard-equipped cancer radiation therapy systems have been demonstrated. Many more approaches that could be implemented were identified. These solutions provide a pathway for the broader adoption of methods to make radiation therapy more accurate, impacting tumor and normal tissue dose, margins, and ultimately patient outcomes.

SU‐E‐J‐44: A Novel Approach to Quantify Patient Setup and Target Motion for Real‐Time Image‐Guided Radiotherapy (IGRT)

PurposeIsocenter shifts and rotations to correct patient setup errors and organ motion cannot remedy some shape changes of large targets. We are investigating new methods in quantification of target deformation for realtime IGRT of breast and chest wall cancer.MethodsNinety‐five patients of breast or chest wall cancer were accrued in an IRB‐approved clinical trial of IGRT using 3D surface images acquired at daily setup and beam‐on time via an in‐room camera. Shifts and rotations relating to the planned reference surface were determined using iterative‐closest‐point alignment. Local surface displacements and target deformation are measured via a ray‐surface intersection and principal component analysis (PCA) of external surface, respectively. Isocenter shift, upper‐abdominal displacement, and vectors of the surface projected onto the two principal components, PC1 and PC2, were evaluated for sensitivity and accuracy in detection of target deformation. Setup errors for some deformed targets were estimated by superlatively registering target volume, inner surface, or external surface in weekly CBCT or these outlines on weekly EPI.ResultsSetup difference according to the inner‐surface, external surface, or target volume could be 1.5 cm. Video surface‐guided setup agreed with EPI results to within &lt; 0.5 cm while CBCT results were sometimes (∼20%) different from that of EPI (&gt;0.5 cm) due to target deformation for some large breasts and some chest walls undergoing deep‐breath‐hold irradiation. Square root of PC1 and PC2 is very sensitive to external surface deformation and irregular breathing.ConclusionPCA of external surfaces is quick and simple way to detect target deformation in IGRT of breast and chest wall cancer. Setup corrections based on the target volume, inner surface, and external surface could be significant different. Thus, checking of target shape changes is essential for accurate image‐guided patient setup and motion tracking of large deformable targets.NIH grant for the first author as cionsultant and the last author as the PI

A kernel-based method for markerless tumor tracking in kV fluoroscopic images

Markerless tracking of respiration-induced tumor motion in kilo-voltage (kV) fluoroscopic image sequence is still a challenging task in real time image-guided radiation therapy (IGRT). Most of existing markerless tracking methods are based on a template matching technique or its extensions that are frequently sensitive to non-rigid tumor deformation and involve expensive computation. This paper presents a kernel-based method that is capable of tracking tumor motion in kV fluoroscopic image sequence with robust performance and low computational cost. The proposed tracking system consists of the following three steps. To enhance the contrast of kV fluoroscopic image, we firstly utilize a histogram equalization to transform the intensities of original images to a wider dynamical intensity range. A tumor target in the first frame is then represented by using a histogram-based feature vector. Subsequently, the target tracking is then formulated by maximizing a Bhattacharyya coefficient that measures the similarity between the tumor target and its candidates in the subsequent frames. The numerical solution for maximizing the Bhattacharyya coefficient is performed by a mean-shift algorithm. The proposed method was evaluated by using four clinical kV fluoroscopic image sequences. For comparison, we also implement four conventional template matching-based methods and compare their performance with our proposed method in terms of the tracking accuracy and computational cost. Experimental results demonstrated that the proposed method is superior to conventional template matching-based methods.

Local metric learning in 2D/3D deformable registration with application in the abdomen.

In image-guided radiotherapy (IGRT) of disease sites subject to respiratory motion, soft tissue deformations can affect localization accuracy. We describe the application of a method of 2D/3D deformable registration to soft tissue localization in abdomen. The method, called registration efficiency and accuracy through learning a metric on shape (REALMS), is designed to support real-time IGRT. In a previously developed version of REALMS, the method interpolated 3D deformation parameters for any credible deformation in a deformation space using a single globally-trained Riemannian metric for each parameter. We propose a refinement of the method in which the metric is trained over a particular region of the deformation space, such that interpolation accuracy within that region is improved. We report on the application of the proposed algorithm to IGRT in abdominal disease sites, which is more challenging than in lung because of low intensity contrast and nonrespiratory deformation. We introduce a rigid translation vector to compensate for nonrespiratory deformation, and design a special region-of-interest around fiducial markers implanted near the tumor to produce a more reliable registration. Both synthetic data and actual data tests on abdominal datasets show that the localized approach achieves more accurate 2D/3D deformable registration than the global approach.

Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity.

We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray. The tumor can then be extracted by decomposing the fluoroscopic intensity into the tumor intensity component and the others. The decomposition problem for more than two structures is ill posed, but it can be transformed into a well-posed one by temporally accumulating constraints that must be satisfied by the decomposed moving tumor component and the rest of the intensity components. The extracted tumor image can then be used to achieve accurate tumor motion tracking without implanted markers that are widely used in the current tracking techniques. The performance evaluation showed that the extraction error was sufficiently small and the extracted tumor tracking achieved a high and sufficient accuracy less than 1 mm for clinical datasets. These results clearly demonstrate the usefulness of the proposed method for markerless tumor motion tracking.

Fast polyenergetic forward projection for image formation using OpenCL on a heterogeneous parallel computing platform

Simulated projection images of digital phantoms constructed from CT scans have been widely used for clinical and research applications but their quality and computation speed are not optimal for real-time comparison with the radiography acquired with an x-ray source of different energies. In this paper, the authors performed polyenergetic forward projections using open computing language (OpenCL) in a parallel computing ecosystem consisting of CPU and general purpose graphics processing unit (GPGPU) for fast and realistic image formation. The proposed polyenergetic forward projection uses a lookup table containing the NIST published mass attenuation coefficients (μ∕ρ) for different tissue types and photon energies ranging from 1 keV to 20 MeV. The CT images of interested sites are first segmented into different tissue types based on the CT numbers and converted to a three-dimensional attenuation phantom by linking each voxel to the corresponding tissue type in the lookup table. The x-ray source can be a radioisotope or an x-ray generator with a known spectrum described as weight w(n) for energy bin E(n). The Siddon method is used to compute the x-ray transmission line integral for E(n) and the x-ray fluence is the weighted sum of the exponential of line integral for all energy bins with added Poisson noise. To validate this method, a digital head and neck phantom constructed from the CT scan of a Rando head phantom was segmented into three (air, gray∕white matter, and bone) regions for calculating the polyenergetic projection images for the Mohan 4 MV energy spectrum. To accelerate the calculation, the authors partitioned the workloads using the task parallelism and data parallelism and scheduled them in a parallel computing ecosystem consisting of CPU and GPGPU (NVIDIA Tesla C2050) using OpenCL only. The authors explored the task overlapping strategy and the sequential method for generating the first and subsequent DRRs. A dispatcher was designed to drive the high-degree parallelism of the task overlapping strategy. Numerical experiments were conducted to compare the performance of the OpenCL∕GPGPU-based implementation with the CPU-based implementation. The projection images were similar to typical portal images obtained with a 4 or 6 MV x-ray source. For a phantom size of 512 × 512 × 223, the time for calculating the line integrals for a 512 × 512 image panel was 16.2 ms on GPGPU for one energy bin in comparison to 8.83 s on CPU. The total computation time for generating one polyenergetic projection image of 512 × 512 was 0.3 s (141 s for CPU). The relative difference between the projection images obtained with the CPU-based and OpenCL∕GPGPU-based implementations was on the order of 10(-6) and was virtually indistinguishable. The task overlapping strategy was 5.84 and 1.16 times faster than the sequential method for the first and the subsequent digitally reconstruction radiographies, respectively. The authors have successfully built digital phantoms using anatomic CT images and NIST μ∕ρ tables for simulating realistic polyenergetic projection images and optimized the processing speed with parallel computing using GPGPU∕OpenCL-based implementation. The computation time was fast (0.3 s per projection image) enough for real-time IGRT (image-guided radiotherapy) applications.

MO-F-BRA-05: Real-Time 3D Tumor Localization for Lung IGRT Using a Single X-Ray Projection.

To study the feasibility of a novel 2D/3D image registration method, called Projection Metric Learning for Shape Kernel Regression (PML-SKR), in supporting on-board x-ray imaging systems to perform real-time image-guided radiation therapy in the lung. PML-SKR works in two stages: planning and treatment. At planning stage, firstly it parameterizes the patient's respiratory deformation from the patient's treatment-planning Respiratory-Correlated CTs (RCCTs) by doing PCA analysis on the inter-phase respiratory deformations. Secondly, it simulates a set of training projection images from a set of deformed CTs where their associated deformation parameters are sampled within 3 standard deviations of the parameter's values observed in the RCCTs. Finally, it learns a Riemannian distance metric on projection intensity for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. At treatment stage, PML-SKR interpolates the patient's 3D deformation parameters from the parameter's values in the training cases using the kernel regression with the learned distance metrics. We tested PML-SKR on the NST (Nanotube Stationary Tomosynthesis) x-ray imaging system. In each test case, a DRR (dimension: 64×64) of an x-ray source in the NST was simulated from a target CT for registration. The target CTs were deformed by normally distributed random samples of the first three deformation parameters. We generated 300 synthetic test cases from 3 lung datasets and measured the registration quality by the mTRE (mean Target Registration Error) over all cases and all voxels at tumor sites. With PML-SKR's registrations, the average mTRE and its standard deviation are down from 10.89±4.44 to 0.67±0.46 mm using 125 training projection images. The computation time for each registration is 12.71±0.70 ms. The synthetic results have shown PML-SKR's promise in supporting real-time, accurate, and low-dose lung IGRT. This work was partially supported by Siemens Medical Solutions.

Brushed permanent magnet DC MLC motor operation in an external magnetic field

Linac-MR systems for real-time image-guided radiotherapy will utilize the multileaf collimators (MLCs) to perform conformal radiotherapy and tumor tracking. The MLCs would be exposed to the external fringe magnetic fields of the linac-MR hybrid systems. Therefore, an experimental investigation of the effect of an external magnetic field on the brushed permanent magnet DC motors used in some MLC systems was performed. The changes in motor speed and current were measured for varying external magnetic field strengths up to 2000 G generated by an EEV electromagnet. These changes in motor characteristics were measured for three orientations of the motor in the external magnetic field, mimicking changes in motor orientations due to installation and/or collimator rotations. In addition, the functionality of the associated magnetic motor encoder was tested. The tested motors are used with the Varian 120 leaf Millennium MLC (Maxon Motor half leaf and full leaf motors) and the Varian 52 leaf MKII MLC (MicroMo Electronics leaf motor) including a carriage motor (MicroMo Electronics). In most cases, the magnetic encoder of the motors failed prior to any damage to the gearbox or the permanent magnet motor itself. This sets an upper limit of the external magnetic field strength on the motor function. The measured limits of the external magnetic fields were found to vary by the motor type. The leaf motor used with a Varian 52 leaf MKII MLC system tolerated up to 450 +/- 10 G. The carriage motor tolerated up to 2000 +/- 10 G field. The motors used with the Varian 120 leaf Millennium MLC system were found to tolerate a maximum of 600 +/- 10 G. The current Varian MLC system motors can be used for real-time image-guided radiotherapy coupled to a linac-MR system, provided the fringe magnetic fields at their locations are below the determined tolerance levels. With the fringe magnetic fields of linac-MR systems expected to be larger than the tolerance levels determined, some form of magnetic shielding would be required.