Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

Hirai, Ryusuke; Sakata, Yoshihito; Tanizawa, Akiyuki; Mori, Shinichiro

doi:10.1016/j.ejmp.2020.02.001

Cited by 6 publications

(7 citation statements)

References 22 publications

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“…Additional to the PCA-based method, we also compared our developed technique to diaphragm tracking, which is frequently used in clinics to assess the liver and its tumor motion (Cervino et al 2009, Hindley et al 2019, Hirai et al 2020. The diaphragm is considered a viable surrogate because the liver-lung boundary is well visualized from an x-ray projection, and the diaphragm and liver tumor motion are to some extent correlated (Balter et al 2001, Yang et al 2014.…”

Section: Template Matching-based Diaphragm Tracking For Comparisonmentioning

confidence: 99%

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

et al. 2022

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Objective. Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients’ anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection. Approach. Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer. Main results. The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (<1.2 (±1.2) mm average localization error). Significance. The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.

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Section: Template Matching-based Diaphragm Tracking For Comparisonmentioning

confidence: 99%

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

et al. 2022

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“…Inserting radiopaque fiducial markers into livers as tracking surrogates requires an invasive procedure which can lead to inflammation, and the markers may migrate over time after the insertion (Bhagat et al 2010, Roman et al 2012. The diaphragm has served as an anatomical surrogate for liver tumors, as the position of the diaphragm can be easily tracked from x-ray projections (Balter et al 2001, Hirai et al 2020. However, the correlation of motion between the liver tumor and the diaphragm is case-dependent and affected by the intra-liver tumor position (Yang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Shao

Wang

et al. 2023

Phys. Med. Biol.

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Objective: Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection. Approach: Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network (GNN)-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results: Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) mm (before motion estimation) to 2.4 (±1.6) mm. The mean (± s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) mm to 1.9 (±1.6) mm. Significance: Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

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“…Other groups have reported using kV images to track tumor positions using the diaphragm as a surrogate. Hirai et al 49 developed a new method to track tumor position using fluoroscopic images and a priori knowledge of the positional relationship between the diaphragm and the tumor calculated from fourdimensional computed tomography (4DCT) data. They evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error, using data from seven liver cases.…”

Section: Discussionmentioning

confidence: 99%

“…Other groups have reported using kV images to track liver tumor positions using the diaphragm as a surrogate. Hirai et al 49 . developed a new method to track tumor position using fluoroscopic images and a priori knowledge of the positional relationship between the diaphragm and the tumor calculated from four‐dimensional computed tomography (4DCT) data.…”

Section: Discussionmentioning

confidence: 99%

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets

Rostamzadeh,

Thomas,

Camborde

et al. 2023

J Applied Clin Med Phys

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PurposeTo assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT.MethodsDiaphragm as surrogate for markers: a dome‐shaped phantom with implanted markers was fabricated and underwent dual‐orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB‐approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in‐house program to back‐project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom “diaphragm” was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath‐hold‐exhale datasets. The MDTT workflow was applied until just prior to MV beam‐on.ResultsDiaphragm as surrogate for markers: phantom data confirmed the in‐house 3D back‐projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean ± RMS difference of 0.70 ± 0.89, 1.08 ± 1.26, and 0.96 ± 1.06 mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory‐correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean ± SD difference between the image‐detected and predicted “diaphragm” position was 0.52 ± 0.18 mm. The 2D film gamma (2%/2 mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image‐detected and predicted diaphragm position was 2.02 ± 0.92 mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7 mm in the ML, SI, and AP directions.ConclusionIn phantom/patients, the diaphragm motion correlated well with markers’ motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.

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Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

Cited by 6 publications

References 22 publications

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets

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