Improvements in beam’s eye view fiducial tracking using a novel multilayer imager

Harris, Thomas C.; Seco, Joao; Ferguson, D.; Jacobson, M.; Myronakis, Marios; Lozano, Ingrid Valencia; Lehmann, Mathias; Huber, Pascal; Fueglistaller, Rony; Morf, Daniel; Mamon, Harvey J.; Mancias, Joseph D.; Martin, Neil E.; Berbeco, R.

doi:10.1088/1361-6560/ac1246

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…The flat-panel imager used in the present study employs a multilayer architecture, vertically stacking four identical detection layers each analogous to current commercially available EPIDs. This multilayer imager (MLI) was found to increase performance metrics such as DQE and contrast-tonoise ratio (CNR) over single layer imagers (Harris et al 2020), while also conferring improvement to clinical EPID applications such as beam's-eye-view tumor tracking (Harris et al 2021). Furthermore, the MLI may provide better quality images at lower doses (Hu et al 2018, Myronakis et al 2020, and the layering can be used for spectral imaging (Myronakis et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of a novel multilayer imager on metal artifacts in MV-CBCT

et al. 2023

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Objective: Megavoltage cone-beam CT (MV-CBCT) imaging offers several advantages including reduced metal artifacts and accurate electron density mapping for adaptive or emergent situations. However, MV-CBCT imaging is limited by the poor efficiency of current detectors. Here we examine a new MV imager and compare CBCT reconstructions under clinically relevant scenarios. Approach: A multilayer imager (MLI), consisting of four vertically stacked standard flat-panel imagers, was mounted to a clinical linear accelerator. A custom anthropomorphic pelvis phantom with replaceable femoral heads was imaged using MV-CBCT and kV-CBCT. Bone, aluminum, and titanium were used as femoral head inserts. 8MU 2.5MV scans were acquired for all four layers and (as reference) the top layer. Prostate and bladder were contoured on a reference CT and transferred to the other scans after rigid registration, from which the structural similarity index measure (SSIM) was calculated. Prostate and bladder were also contoured on CBCT scans without guidance, and Dice coefficients were compared to CT contours.Main results: kV-CBCT demonstrated the highest SSIMs with bone inserts (prostate:0.86, bladder:0.94) and lowest with titanium inserts (0.32, 0.37). 4-layer MV-CBCT SSIMs were preserved with bone (0.75, 0.80) as compared to titanium (0.67, 0.74), outperforming kV-CBCT when metal is present. 1-layer MV-CBCT consistently underperformed 4-layer results across all phantom configurations. Unilateral titanium inserts and bilateral aluminum insert results fell between the bone and bilateral titanium results. Dice coefficients trended similarly, with 4-layer MV-CBCT reducing metal artifact impact relative to KV-CBCT to provide better soft-tissue identification. Significance: MV-CBCT with a 4-layer MLI showed improvement over single-layer MV scans, approaching kV-CBCT quality for soft-tissue contrast. In the presence of artifact-producing metal implants, four-layer MV-CBCT scans outperformed kV-CBCT by eliminating artifacts and single-layer MV-CBCT by reducing noise. MV-CBCT with a novel multi-layer imager may be a valuable alternative to kV-CBCT, particularly in the presence of metal.

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Section: Introductionmentioning

confidence: 99%

Impact of a novel multilayer imager on metal artifacts in MV-CBCT

et al. 2023

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“…A variety of promising techniques are being explored, including multi-layered scintillators, novel scintillating materials, and direct conversion detectors 43 – 47 New EPIDs have been developed with measured DQE(0) of 6.7%, 44 8.0%, 45 9.7%, 46 and 22% 43 . One application of that research demonstrated that single-energy MV CT images acquired with a four-layer EPID have superior image quality relative to MV and kV images acquired with a single-layer detector in the presence of titanium and aluminum implants 48 .…”

Section: Discussionmentioning

confidence: 99%

Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra

Jadick,

Schlafly,

La Rivière

2024

J. Med. Imag.

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Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts.Approach: A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (3 kV, 2 MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images. Results:The 80 kV-140 kV pair typically yielded the best SNRs, but for bone thicknesses >8 cm, the detunedMV-80 kV pair surpassed it. Peak MV-kV SNR was achieved with ∼90% dose allocated to the MV spectrum. In CT simulations of the pelvis with a steel implant, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT and kV-kV DE-CT. Without steel, the MV-kV VMIs produced higher contrast but lower CNR than single-energy CT.Conclusions: This work analyzes MV-kV DE-CT imaging and assesses its potential advantages. The technique may be used for metal artifact correction and generation of VMIs with higher native contrast than single-energy CT. Improved denoising is generally necessary for greater CNR without metal.

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“…This improved the robustness and transferrability of the model between institutions. Furthermore, recent research has been dedicated towards the improvement of detective quantum efficiency and contrast-tonoise ratio of the MV imager, which include the multi-layer imager (MLI) design (Myronakis et al 2017, Hu et al 2018, Harris et al 2020, 2021, pixelated scintillation (Star-Lack et al 2015) and advanced scintillation materials (Hu et al 2019). Harris et al (2021) demonstrated a 41.7% increase in marker tracking efficiency (number of frames that successfully track markers compared to maximum number of trackable frames) when using a novel multilayer MV imager.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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. MV images, acquired from 29 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. 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.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.

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Improvements in beam’s eye view fiducial tracking using a novel multilayer imager

Cited by 3 publications

References 29 publications

Impact of a novel multilayer imager on metal artifacts in MV-CBCT

Impact of a novel multilayer imager on metal artifacts in MV-CBCT

Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra

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

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