Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model

Zhou, You; Folkert, Michael R.; Huang, Xiaokun; Ren, Lei; Meyer, Jeffrey; Tehrani, Joubin Nasehi; Reynolds, R. C.; Wang, Jing

doi:10.21037/qims.2019.07.04

Cited by 10 publications

(15 citation statements)

References 40 publications

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“…We also made the approximation of

D^{0 \to t} = - D^{t \to 0}

in Equation (). The detailed implementation can be found in Zhang et al 40,43 . As described, the 2D‐3D deformable registration is an intensity‐driven method, which shows higher accuracy at high‐contrast regions including organ boundaries, but lower accuracy at homogeneous regions such as intra‐liver regions 33 …”

Section: Methodsmentioning

confidence: 99%

“…In this study, we used the open-source FEBio package to perform the finite element analysis. 60 The detailed implementation can be found in Zhang et al 40,43…”

Section: Biomechanical Modelingmentioning

confidence: 99%

“…To address this issue, recently we developed a principal component analysis (PCA)‐based liver boundary optimization method, named MM‐Bio‐CBCT 43 . PCA is an orthogonal linear‐transformation method that transforms a high‐dimensional vector into a low dimensional PCA projection to reduce redundancy information and find the correlation of features through a motion model.…”

Section: Introductionmentioning

confidence: 99%

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Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

et al. 2021

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Purpose Recently, two‐dimensional‐to‐three‐dimensional (2D‐3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone‐beam computed tomography (CBCT) target images to automate on‐board tumor localizations. Biomechanical modeling has also been introduced to fine‐tune the intra‐liver deformation‐vector‐fields (DVFs) solved by 2D‐3D deformable registration, especially at low‐contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone‐beam projections, which degrades the accuracy of the intensity‐based 2D‐3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep‐learning (DL)‐based method to optimize the liver boundary DVFs after 2D‐3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization. Methods The DL‐based network was built based on the U‐Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D‐3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient‐specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra‐liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D‐3D deformable registration, 2D‐3D‐Bio (2D‐3D deformable registration with biomechanical modeling), and DL‐Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center‐of‐mass‐errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented “gold‐standard” contours. Results The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D‐3D, 2D‐3D‐Bio, and DL‐Bio techniques were 4.7 ± 1.9 mm, 2.9 ± 1.0 mm, and 1.7 ± 0.4 mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6 mm, 5.4 ± 1.5 mm, and 4.5 ± 1.3 mm, respectively. Conclusion DL‐Bio solves a general correlation model to improve the accuracy of ...

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“…We also made the approximation of

D^{0 \to t} = - D^{t \to 0}

Section: Methodsmentioning

confidence: 99%

“…In this study, we used the open-source FEBio package to perform the finite element analysis. 60 The detailed implementation can be found in Zhang et al 40,43…”

Section: Biomechanical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

et al. 2021

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“…As image registration has become more popular and easier to use, it has been applied to various scenarios in IGRT, including target motion tracking (9)(10)(11), organ segmentation (12), and adaptive radiotherapy (13,14). However, the demand for more accurate and efficient registration has not abated and remains a priority for clinical applications.…”

Section: Review Articlementioning

confidence: 99%

A review of deep learning-based three-dimensional medical image registration methods

Xiao¹,

Teng²,

Liu³

et al. 2021

Quant Imaging Med Surg

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Medical image registration is a vital component of many medical procedures, such as imageguided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research.The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.

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“…An ultrasound imaging system was used to observe the movement of the diaphragm and the realtime diaphragm motion signals were captured by using our previously developed ultrasound image tracking algorithm (UITA) (15). In the subsequent verification and respiration displacement compensation experiments, the ultrasound imaging system was used again to track the diaphragm phantom, to perform a real-time respiratory motion tracking and compensation experiments (16)(17)(18)(19)(20)(21), because the diaphragm motion is highly related to the tumors near lung and liver.…”

Section: Introductionmentioning

confidence: 99%

Fast Fourier transform combined with phase leading compensator for respiratory motion compensation system

Kuo¹,

Chuang²,

Liao³

et al. 2020

Quant Imaging Med Surg

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Background: The reduction of the delaying effect in the respiratory motion compensation system (RMCS) is still impossible to completely correct the respiratory waveform of the human body due to each patient has a unique respiratory rate. In order to further improve the effectiveness of radiation therapy, this study evaluates our previously developed RMCS and uses the fast Fourier transform (FFT) algorithm combined with the phase lead compensator (PLC) to further improve the compensation rate (CR) of different respiratory frequencies and patterns of patients.Methods: In this study, an algorithm of FFT automatic frequency detection was developed by using LabVIEW software, uisng FFT combined with PLC and RMCS to compensate the system delay time.Respiratory motion compensation experiments were performed using pre-recorded respiratory signals of 25 patients. During the experiment, the respiratory motion simulation system (RMSS) was placed on the RMCS, and the pre-recorded patient breathing signals were sent to the RMCS by using our previously developed ultrasound image tracking algorithm (UITA). The tracking error of the RMCS is obtained by comparing the encoder signals of the RMSS and RMCS. The compensation effect is verified by root mean squared error (RMSE) and system CR. Results:The experimental results show that the patient's respiratory patterns compensated by the RMCS after using the proposed FFT combined with PLC control method, the RMSE is between 1.50-5.71 and 3.15-8.31 mm in the right-left (RL) and superior-inferior (SI) directions, respectively. CR is between 72. respectively. Conclusions: This study used FFT combined with PLC control method to apply to RMCS, and used UITA for respiratory motion compensation. Under the automatic frequency detection, the best dominant frequency of the human respiratory waveform can be determinated. In radiotherapy, it can be used to compensate the tumor movement caused by respiratory motion and reduce the radiation damage and side effects of normal tissues nearby the tumor.

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Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model

Cited by 10 publications

References 40 publications

Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

A review of deep learning-based three-dimensional medical image registration methods

Fast Fourier transform combined with phase leading compensator for respiratory motion compensation system

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