Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Giger, Alina; Jud, Christoph; Nguyen, Damien; Krieger, Miriam; Zhang, Ye; Lomax, Anthony; Bieri, Oliver; Salomir, Rarès; Cattin, Philippe C.

doi:10.1007/978-3-030-32281-6_2

Cited by 4 publications

(10 citation statements)

References 12 publications

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“…Autoregressive model The autoregressive model used in this study has been described previously (Giger et al 2019) and, therefore, is only briefly summarised here. Let α t ∈ R u describe the vector of the u most dominant principal components of the US image acquired at time t. Furthermore, let α j t denote the jth element of the vector α t .…”

Section: Motion Modellingmentioning

confidence: 99%

Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study

et al. 2021

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Pencil beam scanned (PBS) proton therapy of lung tumours is hampered by respiratory motion and the motion-induced density changes along the beam path. In this simulation study, we aim to investigate the effectiveness of proton beam tracking for lung tumours both under ideal conditions and in conjunction with a respiratory motion model guided by real-time ultrasound imaging of the liver. Multiple-breathingcycle 4DMRIs of the thorax and abdominal 2D ultrasound images were acquired simultaneously for five volunteers. Deformation vector fields extracted from the 4DMRI, referred to as ground truth motion, were used to generate 4DCT(MRI) data sets of two lung cancer patients, resulting in 10 data sets with variable motion patterns. Given the 4DCT(MRI) and the corresponding ultrasound images as surrogate data, a patient-specific motion model was built. The model consists of an autoregressive model and Gaussian process regression for the temporal and spatial prediction, respectively. Two-field PBS plans were optimised on the reference CTs, and 4D dose calculations (4DDC) were used to simulate dose delivery for (a) unmitigated motion, (b) ideal 2D and 3D tracking (both beam adaption and 4DDC based on ground truth motion), and (c) realistic 2D and 3D tracking (beam adaption based on motion predictions, 4DDC on ground truth motion). Model-guided tracking retrieved clinically acceptable target dose homogeneity, as seen in a substantial reduction of the D5-D95 % compared to the non-mitigated simulation. Tracking in 2D and 3D resulted in a similar improvement of the dose homogeneity, as did ideal and realistic tracking simulations. In some

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Section: Motion Modellingmentioning

confidence: 99%

Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study

et al. 2021

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“…That is why registration algorithms with a temporal regularizer have been proposed [19]- [24]. For respiratory motion modeling, learning-based regression models using ultrasound and MR images of different respiratory states have been used to learn respiratory motion patterns [25], [26]. In the computer vision community, temporal video super-resolution and motion compensation are a related research topic [27], [28].…”

Section: A State-of-the-artmentioning

confidence: 99%

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

Krebs¹,

Delingette²,

Ayache³

et al. 2020

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We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic spacethe motion matrix -which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.

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“…Due to physical constraints, direct US imaging of lung tissue is not possible. However, with the help of respiratory motion models, motion characteristics extracted from liver US can be used to estimate lung tumour motion (Mostafaei et al 2018) and lung deformation (Giger et al , 2019. While Mostafaei et al 2018 have demonstrated the correlation between diaphragm motion and lung tumour motion in superior-inferior (SI) direction, Giger et al ( , 2019 have inferred dense lung motion information from 2D abdominal US images.…”

Section: Introductionmentioning

confidence: 99%

“…However, with the help of respiratory motion models, motion characteristics extracted from liver US can be used to estimate lung tumour motion (Mostafaei et al 2018) and lung deformation (Giger et al , 2019. While Mostafaei et al 2018 have demonstrated the correlation between diaphragm motion and lung tumour motion in superior-inferior (SI) direction, Giger et al ( , 2019 have inferred dense lung motion information from 2D abdominal US images. Since we aim to investigate respiratory motion and its variabilities in the context of PBS proton treatment, monitoring 1D tumour motion only is not sufficient as motion information of the surrounding tissue needs to be additionally taken into account (Trnková et al 2018, Bertholet et al 2019.…”

Section: Introductionmentioning

confidence: 99%

“…Since we aim to investigate respiratory motion and its variabilities in the context of PBS proton treatment, monitoring 1D tumour motion only is not sufficient as motion information of the surrounding tissue needs to be additionally taken into account (Trnková et al 2018, Bertholet et al 2019. Moreover, in contrast to our previous work (Giger et al 2019), the motion model presented here is based on non-linear methods and therefore potentially has better estimation power.…”

Section: Introductionmentioning

confidence: 99%

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Liver-ultrasound based motion modelling to estimate 4D dose distributions for lung tumours in scanned proton therapy

et al. 2020

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Motion mitigation strategies are crucial for scanned particle therapy of mobile tumours in order to prevent geometrical target miss and interplay effects. We developed a patient-specific respiratory motion model based on simultaneously acquired time-resolved volumetric MRI and 2D abdominal ultrasound images. We present its effects on 4D pencil beam scanned treatment planning and simulated dose distributions. Given an ultrasound image of the liver and the diaphragm, principal component analysis and Gaussian process regression were applied to infer dense motion information of the lungs. 4D dose calculations for scanned proton therapy were performed using the estimated and the corresponding ground truth respiratory motion; the differences were compared by dose difference volume metrics. We performed this simulation study on 10 combined CT and 4DMRI data sets where the motion characteristics were extracted from 5 healthy volunteers and fused with the anatomical CT data of two lung cancer patients. Median geometrical estimation errors below 2 mm for all data sets and maximum dose differences of V diff>5 % = 43.2 % and V diff>10 % = 16.3 % were found. Moreover, it was shown that abdominal ultrasound imaging allows to monitor organ drift. This study demonstrated the feasibility of the proposed ultrasound-based motion modelling approach for its application in scanned proton therapy of lung tumours.

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Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Cited by 4 publications

References 12 publications

Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study

Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

Liver-ultrasound based motion modelling to estimate 4D dose distributions for lung tumours in scanned proton therapy

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