Recommendation of fiducial marker implantation for better target tracking using <scp>MV</scp> imager in prostate radiotherapy

Ma, Tianjun; Kilian-Meneghin, Joshua; Kumaraswamy, Lalith K.

doi:10.1002/acm2.12390

Cited by 3 publications

(3 citation statements)

References 14 publications

(18 reference statements)

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“…These methods were found to improve the visibility of the implanted markers without compromising the final dose distribution. In addition to MLC configuration modification strategies, Ma et al (2018) developed a model to recommend optimal marker insertion locations to improve visibility of markers using MV imaging during prostate treatment.…”

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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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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“…Usually, three tags are inserted, but in some centers, four or even five tags. [ 4 ] We insert three seeds into the prostate gland.…”

Section: Case Reportmentioning

confidence: 99%

Simple method for correcting imperfectly placed fiducial markers for image-guided radiotherapy technologies of the prostate cancer

Gawlik-Jakubczak

2022

Urol Ann

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“…Some researchers have directly addressed the issue of MLCs obscuring fiducials (Azcona et al 2013, Yue et al 2011, Hu et al 2018. Others have developed indirect methods such as using combined imaging modalities with kV (Wiersma et al 2008, Cho et al 2011, modifying the MLCs during treatment planning to optimize fiducial visibility (Happersett et al 2019), and determining best fiducial placement to maximize frames containing a fiducial (Ma et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Harris¹,

Seco²,

Ferguson³

et al. 2021

Phys. Med. Biol.

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Purpose. Electronic portal image devices (EPIDs) have been investigated previously for beams-eye view (BEV) applications such as tumor tracking but are limited by low contrast-to-noise ratio and detective quantum efficiency. A novel multilayer imager (MLI), consisting of four stacked flat-panels was used to measure improvements in fiducial tracking during liver stereotactic body radiation therapy (SBRT) procedures compared to a single layer EPID. Methods. The prototype MLI was installed on a clinical TrueBeam linac in place of the conventional DMI single-layer EPID. The panel was extended during volumetric modulated arc therapy SBRT treatments in order to passively acquire data during therapy. Images were acquired for six patients receiving SBRT to liver metastases over two fractions each, one with the MLI using all 4 layers and one with the MLI using the top layer only, representing a standard EPID. The acquired frames were processed by a previously published tracking algorithm modified to identify implanted radiopaque fiducials. Truth data was determined using respiratory traces combined with partial manual tracking. Results for 4- and 1-layer mode were compared against truth data for tracking accuracy and efficiency. Tracking and noise improvements as a function of gantry angle were determined. Results. Tracking efficiency with 4-layers improved to 82.8% versus 58.4% for the 1-layer mode, a relative improvement of 41.7%. Fiducial tracking with 1-layer returned a root mean square error (RMSE) of 2.1 mm compared to 4-layer RMSE of 1.5 mm, a statistically significant (p < 0.001) improvement of 0.6 mm. The reduction in noise correlated with an increase in successfully tracked frames (r = 0.913) and with increased tracking accuracy (0.927). Conclusion. Increases in MV photon detection efficiency by utilization of a MLI results in improved fiducial tracking for liver SBRT treatments. Future clinical applications utilizing BEV imaging may be enhanced by including similar noise reduction strategies.

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Recommendation of fiducial marker implantation for better target tracking using MV imager in prostate radiotherapy

Cited by 3 publications

References 14 publications

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

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

Simple method for correcting imperfectly placed fiducial markers for image-guided radiotherapy technologies of the prostate cancer

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

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