Dual-energy Fluoroscopy Research Articles

Evaluation of Markerless Tumor Tracking Using Synthetic Dual Energy Images Produced Using a Convolutional Neural Network

<h3>Purpose/Objective(s)</h3> Dual-energy (DE) fluoroscopy is being considered for markerless tumor tracking (MTT) of lung tumors. The principal advantage of DE imaging is that it removes overlying bony anatomy and improves tumor tracking accuracy, compared with single energy (SE) fluoroscopy. However, to be used clinically, DE imaging requires specific hardware/software which is not widely available. To address this limitation, we developed and implemented a convolutional neural network (CNN) to generate synthetic DE (sDE) images from SE images. The goal of this study is to evaluate the accuracy of MTT using sDE as compared to SE and real DE (rDE) images. <h3>Materials/Methods</h3> To evaluate MTT with DE fluoroscopy, a thoracic motion phantom, consisting of a torso with embedded ribs and spine along with a lung-type cavity, was used. A simulated tumor (10 mm diameter) was placed inside the lung equivalent compartment of the phantom and then programmed to simulate breathing (peak-to-peak amplitude=15 mm, period = 5 s) using a programmed cos⁴ function. While the target was moving, fast kV switching images (alternating 60 and 120 kVp) were obtained using the on-board imager (OBI) of a commercial linear accelerator. Images were acquired over 360 degrees of rotation, allowing the target projection to overlap with varying amounts of bone. Weighted logarithmic subtraction was performed offline on consecutive 60 and 120 kVp projections to produce DE images. Separately, a CNN (U-net) was trained to produce sDE directly from the 120 kVp images (which are used clinically for SE fluoroscopy). The CNN uses a "U-Net" type architecture, with skip connections used to pass low-level information across the network for use in image translation. A template-based matching algorithm was then used to track target motion on SE (120 kVp), rDE, and sDE images. A quantitative analysis was performed based on tracking success rate (TSR) which was evaluated against ground truth (GT) using the programmed cos⁴ waveform. <h3>Results</h3> A total of 449 SE, rDE and sDE image frames were evaluated for the thoracic phantom with a 10 mm moving target. TSR values based on < 1 mm agreement with GT on SE, rDE, and sDE images were 53.5%, 89.7%, and 74.2%, respectively. The percentage of images with a TSR within < 2 mm agreement with GT was 71.9% for SE, 93.5% for rDE and 89.5% for sDE. Lower TSR percentages were observed on sDE and SE images where the simulated tumor overlapped the spine. <h3>Conclusion</h3> sDE images generated using a CNN have a TSR that is higher than SE images, and have TSR values that are close to rDE images. Further development in the CNN is required to improve the tracking accuracy of sDE images in the spine region. The preliminary findings of this study suggest that the use of sDE images generated from SE images potentially allow for tracking with DE-like images on any OBI, without the need for additional hardware/software.

Markerless Tumor Tracking using Short Arc Digital Tomosynthesis With Fast-kV Switching Dual Energy Imaging

Markerless tumor tracking (MTT) using template matching is being considered for real-time lung tumor tracking using single energy (SE) and dual energy (DE) fluoroscopy. While MTT results using DE are generally superior vs. SE imaging, tracking small targets (< 10 mm) is challenging for both techniques. Short arc digital tomosynthesis (DTS), in which projections over a short (6 deg) sliding arc are combined to form a tomosynthesis image, may serve as a preprocessing technique to improve tracking. The goal of this study is to evaluate MTT with DTS using SE imaging and fast-kV switching DE imaging with the on-board imager (OBI) of a commercial linear accelerator. To evaluate these techniques, a motion phantom, consisting of a torso with embedded ribs and spine along with a cavity having lung-equivalent density, was used. A 5 mm simulated tumor was placed inside the lung equivalent compartment of the phantom. The simulated tumor was placed at three different static positions representing the two extremes and the mid-point of the respiratory cycle. Full rotation cone beam computed tomography (CBCT) acquisitions were obtained using SE and DE imaging (15 frames/sec) with a gantry speed of 6 deg/sec. DE images were processed using weighted logarithmic subtraction on consecutive high-low projections to produce soft tissue images. DTS image sequences were generated from both SE and DE image sequences. A template-based matching algorithm was used to track target motion on SE and DE fluoroscopy and SE- and DE-DTS images. Tumor tracking coordinates were evaluated against ground truth motion using the receiver operating characteristics (ROC) and root-mean-squared error (RMSE) on the combined results for all three tumor positions. The ROC area-under-the-curve (AUC) was the lowest for SE (0.82) and DE fluoroscopy (0.87). The use of SE- and DE-DTS increased the AUC values to 0.93 and 0.98, respectively. For a 95% specificity, the sensitivities for SE and DE fluoroscopy were 37% and 50%, respectively. The inclusion of SE-DTS increased this sensitivity to 81%. However, the highest sensitivity was observed for DE-DTS at 96%. With respect to local accuracy, the RMSE was evaluated based on a 95% specificity. For SE and DE fluoroscopy, the RMSE were 1.57 mm and 1.13 mm, respectively, while SE- and DE-DTS had RMSE of 0.58 mm and 0.54 mm, respectively. While DE fluoroscopy improves MTT vs. SE, both techniques have low sensitivity for small targets. The use of DTS significantly improves sensitivity and tracking accuracy vs. these fluoroscopic techniques. However, the most significant improvements were noted for DE-DTS, suggesting that this technique can be a useful preprocessing technique to make markerless tracking of small targets more robust and accurate.

Characterization and potential applications of a dual-layer flat-panel detector.

Dual-energy (DE) x-ray imaging has many clinical applications in radiography, fluoroscopy, and CT. This work characterizes a prototype dual-layer (DL) flat-panel detector (FPD) and investigates its DE imaging capabilities for applications in two-dimensional (2D) radiography/fluoroscopy and quantitative three-dimensional (3D) cone-beam CT. Unlike other DE methods like kV switching, a DL FPD obtains DE images from a single exposure, making it robust against patient and system motion. The DL FPD consists of a top layer with a 200µm-thick CsI scintillator coupled to an amorphous silicon (aSi) FPD of 150µm pixel size and a bottom layer with a 550µm thick CsI scintillator coupled to an identical aSi FPD. The two layers are separated by a 1-mm Cu filter to increase spectral separation. Images (43×43cm2 active area) can be readout in 2×2 binning mode (300µm pixels) at up to 15 frames per second. Detector performance was first characterized by measuring the MTF, NPS, and DQE for the top and bottom layers. For 2D applications, a qualitative study was conducted using an anthropomorphic thorax phantom containing a porcine heart with barium-filled coronary arteries (similar to iodine). Additionally, fluoroscopic lung tumor tracking was investigated by superimposing a moving tumor phantom on the thorax phantom. Tracking accuracies of single-energy (SE) and DE fluoroscopy were compared against the ground truth motion of the tumor. For 3D quantitative imaging, a phantom containing water, iodine, and calcium inserts was used to evaluate overall DE material decomposition capabilities. Virtual monoenergetic (VM) images ranging from 40 to 100keV were generated, and the optimal VM image energy which achieved the highest image uniformity and maximum contrast-to-noise ratio (CNR) was determined. The spatial resolution of the top layer was substantially higher than that of the bottom layer (top layer 50% MTF=2.2mm-1 , bottom layer=1.2mm-1 ). A substantial increase in NNPS and reduction in DQE were observed for the bottom layer mainly due to photon loss within the top layer and Cu filter. For 2D radiographic and fluoroscopic applications, the DL FPD was capable of generating high-quality material-specific images separating soft tissue from bone and barium. For lung tumor tracking, DE fluoroscopy yielded more accurate results than SE fluoroscopy, with an average reduction in the root mean square error (RMSE) of over 10×. For the DE-CBCT studies, accurate basis material decompositions were obtained. The estimated material densities were 294.68 17.41 and 92.14 15.61mg/ml for the 300 and 100mg/ml calcium inserts, respectively, and 8.93 1.45, 4.72 1.44, and 2.11 1.32mg/ml for the 10, 5, and 2mg/ml iodine inserts, respectively, with an average error of less than 5%. The optimal VM image energy was found to be 60keV. We characterized a prototype DL FPD and demonstrated its ability to perform accurate single-exposure DE radiography/fluoroscopy and DE-CBCT. The merits of the DL detector approach include superior spatial and temporal registration between its constituent images, and less complicated acquisition sequences.

Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual-energy fluoroscopy.

To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual-energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT). A CNN was developed to automate WLS (aWLS) of DE fluoroscopy to enhance soft tissue visibility. Briefly, this algorithm consists of two phases: training a CNN architecture to predict pixel-wise weighting factors followed by application of WLS subtraction to reduce anatomical noise. To train the CNN, a custom phantom was built consisting of aluminum (Al) and acrylic (PMMA) step wedges. Per-pixel ground truth (GT) weighting factors were calculated by minimizing the contrast of Al in the step wedge phantom to train the CNN. The pretrained model was then utilized to predict pixel-wise weighting factors for use in WLS. For comparison, the weighting factor was manually determined in each projection (mWLS). A thorax phantom with five simulated spherical targets (5-25mm) embedded in a lung cavity, was utilized to assess aWLS performance. The phantom was imaged with fast-kV dual-energy (120 and 60kVp) fluoroscopy using the on-board imager of a commercial linear accelerator. DE images were processed offline to produce soft tissue images using both WLS methods. MTT was compared using soft tissue images produced with both mWLS and aWLS techniques. Qualitative evaluation demonstrated that both methods achieved soft tissue images with similar quality. The use of aWLS increased the number of tracked frames by 1-5% compared to mWLS, with the largest increase observed for the smallest simulated tumors. The tracking errors for both methods produced agreement to within 0.1 mm. A novel method to perform automated WLS for DE fluoroscopy was developed. Having similar soft tissue quality as well as bone suppression capability as mWLS, this method allows for real-time processing of DE images for MTT.

Markerless Tumor Tracking using Fast-kV Switching Dual Energy Imaging with the On-Board Imager of a Commercial Linac

Markerless tumor tracking (MTT) using template matching is currently being considered for real-time lung tumor tracking using single energy (SE) fluoroscopy. A limitation of MTT is that the tumor has to be clearly visible on images. In cases where the tumor is obscured by bony anatomy, dual energy (DE) imaging may be used to create a soft tissue only image, improving tumor visibility as well as the accuracy of MTT. The goal of this study is to evaluate MTT with fast-kV switching DE imaging using the on-board imager (OBI) of a commercial linear accelerator. Fast-kV switching DE fluoroscopy was implemented on the OBI of a commercial linac. This technique produces x-ray pulses that alternate between programmed tube voltages (ie, 120 and 60 kVp) at a rate of 15 Hz. To evaluate MTT with DE fluoroscopy, a motion phantom, consisting of a torso with embedded ribs and spine along with a cavity having lung-equivalent density, was used. Simulated tumors (5-25 mm diameter) were placed inside the lung equivalent compartment of the phantom, and then programmed to simulate breathing. Three categories of motion with varying periods were considered: static (ST), slow-motion (SM) - 5 sec period and fast-motion (FM) - 2.5 sec period. While the target was moving, fast-kV switching DE images were acquired over 360° of rotation allowing the tumor projection to overlap with varying amounts of bone. Weighted logarithmic subtraction was performed on consecutive high-low projections to produce soft tissue images. A template-based matching algorithm was then used to track target motion on both DE and SE sequences. Successful matching was defined as any instance in which the algorithm matched the template on the image. Tumor tracking coordinates were also evaluated against ground truth motion using the root-mean-squared error (RMSE). For the 5 mm target, the success rates ranged from 11-17% for SE vs. 36-63% for DE imaging, for the three types of motion considered. Because of the low success rates, RMSE were not evaluated for this target. The 10 mm target demonstrated success rates of 66-73% and 87-90% for SE and DE, respectively. The corresponding RMSE for SE were 2.7, 2.9 and 3.4 mm, while for DE they were 0.2, 1.0 and 0.5 mm, for ST, SM and FM, respectively. For the 15 mm target, SE success rates ranged from 84-89% vs. 90-94% for DE. RMSE for SE was 0.5, 2.0 and 1.4 mm vs. 0.2, 0.5, and 0.2 mm for DE for ST, SM and FM, respectively. For the larger tumors (20 and 25 mm), the success rates were > 90% for both SE and DE. RMSE for SE and DE were <1.1 mm and <0.4 mm, respectively, for all motion categories. Fast-kV switching DE imaging has been implemented on the OBI of a commercial linac. Overall, MTT with DE imaging results in higher success rates and lower RMSE than using SE imaging alone. The approach allows for the accurate tracking of small tumors where MTT fails on SE imaging due to overlapping bony anatomy.

Markerless tumor tracking using fast-kV switching dual-energy fluoroscopy on a benchtop system.

To evaluate markerless tumor tracking (MTT) using fast-kV switching dual-energy (DE) fluoroscopy on a bench top system. Fast-kV switching DE fluoroscopy was implemented on a bench top which includes a turntable stand, flat panel detector, and x-ray tube. The customized generator firmware enables consecutive x-ray pulses that alternate between programmed high and low energies (e.g., 60 and 120kVp) with a maximum frame rate of 15Hz. In-house software was implemented to perform weighted DE subtraction of consecutive images to create an image sequence that removes bone and enhances soft tissues. The weighting factor was optimized based on gantry angle. To characterize this system, a phantom was used that simulates the chest anatomy and tumor motion in the lung. Five clinically relevant tumor sizes (5-25mm diameter) were considered. The targets were programmed to move in the inferior-superior direction of the phantom, perpendicular to the x-ray beam, using a cos4 waveform to mimic respiratory motion. Target inserts were then tracked with MTT software using a template matching method. The optimal computed tomography (CT) slice thickness for template generation was also evaluated. Tracking success rate and accuracy were calculated in regions of the phantom where the target overlapped ribs vs spine, to compare the performance of single energy (SE) and DE imaging methods. For the 5mm target, a CT slice thickness of 0.75mm resulted in the lowest tracking error. For the larger targets (≥10mm) a CT slice thickness ≤2mm resulted in comparable tracking errors for SE and DE images. Overall DE imaging improved MTT accuracy, relative to SE imaging, for all tumor targets in a rotational acquisition. Compared to SE, DE imaging increased tracking success rate of small target inserts (5 and 10mm). For fast motion tracking, success rates improved from 23% to 64% and 74% to 90% for 5 and 10mm targets inserts overlapping ribs, respectively. For slow moving targets success rates improved from 19% to 59% and 59% to 91% in 5 and 10mm targets overlapping the ribs, respectively. Similar results were observed when the targets overlapped the spine. For larger targets (≥15mm) tracking success rates were comparable using SE and DE imaging. This work presents the first results of MTT using fast-kV switching DE fluoroscopy. Using DE imaging has improved the tracking accuracy of MTT, especially for small targets. The results of this study will guide the future implementation of fast-kV switching DE imaging using the on-board imager of a linear accelerator.

Evaluation of Radiomics to Predict the Accuracy of Markerless Motion Tracking of Lung Tumors: A Preliminary Study.

Template-based matching algorithms are currently being considered for markerless motion tracking of lung tumors. These algorithms use tumor templates derived from the planning CT scan, and track the motion of the tumor on single energy fluoroscopic images obtained at the time of treatment. In cases where bone may obstruct the view of the tumor, dual energy fluoroscopy may be used to enhance soft tissue contrast. The goal of this study is to predict which tumors will have a high degree of accuracy for markerless motion tracking based on radiomic features obtained from the planning CT scan, using peak-to-sidelobe ratio (PSR) as a surrogate of tracking accuracy. In this study, CT imaging data of 8 lung cancer patients were obtained and analyzed through the open source IBEX program to generate 2,287 radiomic features. Agglomerative hierarchical clustering was used to narrow down these features into 145 clusters comprised of the highest correlation to PSR. The features among the clusters with the least inter-correlation were then chosen to limit redundancy in the data. The results of this study demonstrated a number of radiomic features that are positively correlated to PSR. The features with the highest degree of correlation included complexity, orientation and range. This approach may be used to determine patients for whom markerless motion tracking would be beneficial.

Dual Energy Fluoroscopy Improves the Accuracy of Template-Based Tracking in Lung Cancer Patients Receiving Stereotactic Body Radiation Therapy

Template-based matching algorithms are currently being considered for markerless motion tracking (MMT) of lung tumors using fluoroscopy. The accuracy of this match is quantified through a metric known as peak-to-side lobe ratio (PSR). A previous study has shown that template matches with PSR≥3 are significantly more accurate than those with PSR<3. The goal of this study is to evaluate the fraction of single energy (SE) fluoroscopic images, obtained in SBRT lung cancer patients, that have PSR≥3, and if the addition of dual energy (DE) subtraction fluoroscopy can increase the PSR value. Moreover, radiomic parameters from CT images are evaluated to determine if any are predictive of PSR. In this prospective study, a total of 2,448 fluoroscopy frames were acquired, at multiple imaging angles, from 8 SBRT patients with Stage IA - IIA lung cancer. Sequential 60 kVp and 120 kVp fluoroscopic sequences were acquired and aligned based on respiratory signal. DE fluoroscopic sequences were created using a frame-by-frame weighted logarithmic subtraction. Separately, a template was derived from the contoured gross tumor volume (GTV) on the CT simulation scan. A template-based matching algorithm was used to track tumor motion throughout the DE and SE (120 kVp) fluoroscopy sequences. This algorithm shifts the template across the image and calculates the normalized cross correlation (NCC) between the template and image, resulting in a match score surface. The offset at which the NCC has the maximum value (i.e., the position of the peak of the surface) represents the potential target position. The strength of this peak relative to NCC values away from the peak, called side lobe values, is quantified by the PSR. Radiomic parameters using the GTV from the planning CT scans were examined to determine correlation with the PSR values. Approximately 68% of the SE fluoroscopic images had PSR ≥3. Of the SE images with PSR <3, 79.5% were obtained at an oblique imaging angle, while the remaining were obtained at an anterior imaging angle. By adding DE fluoroscopy, over 86% of the images obtained PSR ≥3 (P < 0.001). Of the 23 unique imaging sessions, SE imaging alone allowed for tracking in over 80% of the images in 12/23 (52%) sessions vs. 18/23 (78%) when combined with DE fluoroscopy (P = 0.12). In those cases where DE imaging increased the PSR to ≥3, the change in template positioning was 1.14 +/- 0.61 mm. Of the 200 unique radiomic parameters analyzed, “energy” was predictive of PSR value, having a Pearson’s correlation coefficient = 0.846 and 0.841 for SE and DE, respectively. The PSR value provides an independent metric for assessing the accuracy of a template match when used in conjunction with MMT. The addition of DE imaging to SE fluoroscopy increases the fraction of images with PSR >3, thus improving the overall accuracy of template matching.

Validation of an External Motion Surrogate Using Markerless Motion Tracking With Single and Dual Energy Fluoroscopy in Lung Stereotactic Body Radiation Therapy

Validation of an External Motion Surrogate Using Markerless Motion Tracking With Single and Dual Energy Fluoroscopy in Lung Stereotactic Body Radiation Therapy

Dual Energy Fluoroscopy for Markerless Motion Tracking of Lung Tumors in Stereotactic Body Radiation Therapy (SBRT)

Dual Energy Fluoroscopy for Markerless Motion Tracking of Lung Tumors in Stereotactic Body Radiation Therapy (SBRT)

Feasibility of markerless tumor tracking by sequential dual-energy fluoroscopy on a clinical tumor tracking system

A novel approach to dual-energy imaging for markerless tumor tracking was proposed consisting of sequential dual-energy fluoroscopy, omitting the need for fast-switching kV generators. The implementation of this approach on a clinical tumor tracking system and its efficacy is shown feasible through optimization of the imaging parameters.

SU-E-J-59: Feasibility of Markerless Tumor Tracking by Sequential Dual-Energy Fluoroscopy On a Clinical Tumor Tracking System

Purpose: To evaluate the feasibility of markerless tumor tracking through the implementation of a novel dual-energy imaging approach into the clinical dynamic tracking (DT) workflow of the Vero SBRT system. Methods: Two sequential 20 s (11 Hz) fluoroscopy sequences were acquired at the start of one fraction for 7 patients treated for primary and metastatic lung cancer with DT on the Vero system. Sequences were acquired using 2 on-board kV imaging systems located at ±45° from the MV beam axis, at respectively 60 kVp (3.2 mAs) and 120 kVp (2.0 mAs). Offline, a normalized cross-correlation algorithm was applied to match the high (HE) and low energy (LE) images. Per breathing phase (inhale, exhale, maximum inhale and maximum exhale), the 5 best-matching HE and LE couples were extracted for DE subtraction. A contrast analysis according to gross tumor volume was conducted based on contrast-to-noise ratio (CNR). Improved tumor visibility was quantified using an improvement ratio. Results: Using the implanted fiducial as a benchmark, HE-LE sequence matching was effective for 13 out of 14 imaging angles. Overlying bony anatomy was removed on all DE images. With the exception of two imaging angles, the DE images showed no significantly improved tumor visibility comparedmore » to HE images, with an improvement ratio averaged over all patients of 1.46 ± 1.64. Qualitatively, it was observed that for those imaging angles that showed no significantly improved CNR, the tumor tissue could not be reliably visualized on neither HE nor DE images due to a total or partial overlap with other soft tissue. Conclusion: Dual-energy subtraction imaging by sequential orthogonal fluoroscopy was shown feasible by implementing an additional LE fluoroscopy sequence. However, for most imaging angles, DE images did not provide improved tumor visibility over single-energy images. Optimizing imaging angles is likely to improve tumor visibility and the efficacy of dual-energy imaging. This work was in part sponsored by corporate funding from BrainLAB AG.(BrainLAB AG, Feldkirchen, Germany)« less

OC-0550: Feasibility of markerless tumor tracking by sequential dual-energy fluoroscopy on a clinical tumor tracking system

OC-0550: Feasibility of markerless tumor tracking by sequential dual-energy fluoroscopy on a clinical tumor tracking system

Markerless motion tracking of lung tumors using dual-energy fluoroscopy.

To evaluate the efficacy of dual-energy (DE) vs single-energy (SE) fluoroscopic imaging of lung tumors using a markerless template-based tracking algorithm. Ten representative patient breathing patterns were programmed into a Quasar™ motion phantom. The phantom was modified by affixing pork ribs to the surface, and a cedar insert with a small spherical volume was used to simulate lung and tumor, respectively. Sequential 60 kVp (6 mA) and 120 kVp (1.5 mA) fluoroscopic sequences were acquired. Frame-by-frame weighted logarithmic subtraction was performed resulting in a DE fluoroscopic sequence. A template-based algorithm was then used to track tumor motion throughout the DE and SE fluoroscopy sequences. Tracking coordinates were evaluated against ground-truth tumor locations. Fluoroscopic images were also acquired for two lung cancer patients, neither of which had implanted fiducials. For phantom imaging, a total of 1925 frames were analyzed. The algorithm successfully tracked the target on 99.9% (1923/1925) of DE frames vs 90.7% (1745/1925) SE images (p < 0.01). The displacement between tracking coordinates and ground truth for the phantom was 1.4 mm ± 1.1 mm for DE vs 2.0 mm ± 1.3 mm for SE (p < 0.01). Images from two patients, one with a larger tumor and one with a smaller tumor, were also analyzed. For the patient with the larger tumor, the average displacement from physician defined ground truth was 1.2 mm ± 0.6 mm for DE vs 1.4 mm ± 0.7 mm for SE (p = 0.016). For the patient that presented with a smaller tumor, the average displacement from physician defined ground truth was 2.2 mm ± 1.0 mm for DE vs 3.2 mm ± 1.4 mm for SE (p < 0.01). Importantly, for this single patient with the smaller tumor, 15.6% of the SE frames had >5 mm displacements from the ground truth vs 0% for DE fluoroscopy. This work indicates the potential for markerless tumor tracking utilizing DE fluoroscopy. With DE imaging, the algorithm showed improved detectability vs SE fluoroscopy and was able to accurately track the tumor in nearly all cases.

SU‐D‐18A‐03: Markerless Motion Tracking of Lung Tumors Using Dual‐Energy Fluoroscopy

Purpose:To evaluate the efficacy of dual energy (DE) vs. single energy (SE) fluoroscopic imaging in lung tumors using a markerless template‐based tracking algorithm.Methods:Ten representative patient breathing patterns were programmed into a Quasar™ motion phantom. The phantom was modified by affixing pork ribs to the surface, and a cedar insert with a small spherical volume was used to simulate lung and tumor, respectively. Gated 60kVp(6mA) and 120kVp(1.5mA) fluoroscopic sequences were acquired using the Varian TrueBeam Integrated Imaging Solution. These high and low energy frames were subsequently aligned by phase and amplitude. Frame‐by‐frame weighted logarithmic subtraction was performed resulting in a DE fluoroscopic image sequence that highlighted soft tissue structures. A template‐based algorithm was then used to track tumor motion throughout the sequence for DE and SE images (120kVp). Tracking coordinates were evaluated against ground truth tumor locations as identified by a physician. A displacement of greater than 3 mm was considered a non‐successful track. The same process was also repeated for images obtained from 3 SBRT lung patients, none of which had implanted fiducials.Results:For phantom imaging, a total of 1925 frames were analyzed. The algorithm successfully tracked the target on 91.6%(1763/1925) of DE frames vs. 74.9%(1441/1923) SE images (p&lt;0.001). The displacement between tracking coordinates and ground truth for the phantom was 1.1mm+/−0.7mm for DE and 1.5mm+/− 0.6mm for SE (p&lt;0.001). Images from 3 patients were analyzed with the tumor tracked on 84.9%(478/563) of frames for DE vs. 53.5%(301/563) for SE (p&lt;0.001). The average difference to ground truth for successfully tracked DE frames was 0.6mm+/−0.6mm vs. 1.7mm+/−0.7mm SE (p&lt;0.001).Conclusion:This work indicates the potential for markerless tumor tracking utilizing DE fluoroscopy. With DE imaging, the algorithm showed improved detectability vs. SE fluoroscopy and was able to accurately track the tumor in a majority of cases.This work was supported by Varian Medical Systems

Scatter-glare corrections in quantitative dual-energy fluoroscopy.

Previous attempts to use time subtraction intravenous digital subtraction angiography for ventricular imaging have been hampered by artifacts due to cardiac and respiratory motion. We have previously reported a motion-immune dual-energy technique in which kVp is switched between 60 and 120, at 300-500 mA, 30 times/s. In order to quantitate parameters such as ejection fraction and left ventricular volume, it is necessary to correct for scatter and veiling glare (SVG), which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). In this report, a convolution filtering method has been investigated to estimate SVG in DSA images. In the first step, a grey level transformation of the detected image is utilized to get an estimated SVG image. In the second step this image is convolved to produce an image with appropriate spatial frequency content. Estimates of SVG in several Humanoid chest phantom images were obtained using Gaussian convolution kernels with a full width at half-maximum (FWHM) of 51-125 pixels. The root-mean-square (rms) percentage error of these estimates was obtained by comparison with direct SVG measurement. A convolution kernel with a FWHM of 75 pixels in each dimension applied to 16 Humanoid phantom images with various projections, thicknesses, and beam energies resulted in an average rms percentage error of 9.7% in the SVG estimate, for the 16 cases studied. The SVG estimation consisting of grey scale-to-SVG fraction lookup table (LUT) is made based on previous measurements. The x-ray settings required for each patient are utilized to alter the LUT in order to account for patient thickness variations.(ABSTRACT TRUNCATED AT 250 WORDS)