Evaluation of a template-based algorithm for markerless lung tumour localization on single- and dual-energy kilovoltage images

Block, Alec M.; Patel, Rakesh; Sürücü, Murat; Harkenrider, Matthew M.; Roeske, John C.

doi:10.1259/bjr.20160648

Cited by 10 publications

(15 citation statements)

References 49 publications

(57 reference statements)

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“…These values are consistent with a previous study by Block et al. 27 In their study based on images from 17 patients, it was determined that the false positive rate was 20.9% (PSR < 3) versus 4.0% (PSR ≥ 3; P <.01). It is important to note that Block et al results were based on clinical images that were obtained sequentially and processed offline.…”

Section: Discussionsupporting

confidence: 92%

Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging

Roeske

Mostafavi

Haytmyradov

et al. 2020

Advances in Radiation Oncology

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Purpose To describe and characterize fast-kV switching, dual-energy (DE) imaging implemented within the on-board imager of a commercial linear accelerator for markerless tumor tracking (MTT). Methods and Materials Fast-kV switching, DE imaging provides for rapid switching between programmed tube voltages (ie, 60 and 120 kVp) from one image frame to the next. To characterize this system, the weighting factor used for logarithmic subtraction and signal difference-to-noise ratio were analyzed as a function of time and frame rate. MTT was evaluated using a thorax motion phantom and fast kV, DE imaging was compared versus single energy (SE) imaging over 360 degrees of rotation. A template-based matching algorithm was used to track target motion on both DE and SE sequences. Receiver operating characteristics were used to compare tracking results for both modalities. Results The weighting factor was inversely related to frame rate and stable over time. After applying the frame rate–dependent weighting factor, the signal difference-to-noise ratio was consistent across all frame rates considered for simulated tumors ranging from 5 to 25 mm in diameter. An analysis of receiver operating characteristics curves showed improved tracking with DE versus SE imaging. The area under the curve for the 10-mm target ranged from 0.821 to 0.858 for SE imaging versus 0.968 to 0.974 for DE imaging. Moreover, the residual tracking errors for the same target size ranged from 2.02 to 2.18 mm versus 0.79 to 1.07 mm for SE and DE imaging, respectively. Conclusions Fast-kV switching, DE imaging was implemented on the on-board imager of a commercial linear accelerator. DE imaging resulted in improved MTT accuracy over SE imaging. Such an approach may have application for MTT of patients with lung cancer receiving stereotactic body radiation therapy, particularly for small tumors where MTT with SE imaging may fail.

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Section: Discussionsupporting

confidence: 92%

Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging

Roeske

Mostafavi

Haytmyradov

et al. 2020

Advances in Radiation Oncology

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“…The WLS technique used in conjunction with DE imaging has been shown to improve tumor localization 5,[16][17][18] and enhance performance of MTT methods 4,8,[19][20][21] by suppressing bony anatomy. Conventionally, soft tissue images are created by reducing the contrast of the bone and neighboring soft tissue region followed by assigning a global weighting factor across the image.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Purpose 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). Methods 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 (5–25 mm) embedded in a lung cavity, was utilized to assess aWLS performance. The phantom was imaged with fast‐kV dual‐energy (120 and 60 kVp) 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. Results 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. Conclusions 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.

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“…The strength of this peak relative to NCC values away from the peak, called side lobe values, is quantified by the peak‐to‐side lobe ratio (PSR). Block et al showed that PSR values can be used as a predictor of successful template matches, and PSR > 3 can reduce the false detection rate …”

Section: Methodsmentioning

confidence: 99%

“…The PSR score is related to the strength of template matching algorithm . For each iteration, the normalized cross‐correlation (NCC) between template and image was computed to produce a 2D match score surface.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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Purpose: To evaluate markerless tumor tracking (MTT) using fast-kV switching dual-energy (DE) fluoroscopy on a bench top system. Methods: 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 120 kVp) with a maximum frame rate of 15 Hz. 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 (5-25 mm 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 cos 4 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. Results: For the 5 mm target, a CT slice thickness of 0.75 mm resulted in the lowest tracking error. For the larger targets (≥10 mm) a CT slice thickness ≤2 mm 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 10 mm). For fast motion tracking, success rates improved from 23% to 64% and 74% to 90% for 5 and 10 mm targets inserts overlapping ribs, respectively. For slow moving targets success rates improved from 19% to 59% and 59% to 91% in 5 and 10 mm targets overlapping the ribs, respectively. Similar results were observed when the targets overlapped the spine. For larger targets (≥15 mm) tracking success rates were comparable using SE and DE imaging. Conclusion: 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.

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Evaluation of a template-based algorithm for markerless lung tumour localization on single- and dual-energy kilovoltage images

Cited by 10 publications

References 49 publications

Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging

Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging

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

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

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